From a1cef1193fd7846567a67dea1dd98e1b82c46cd0 Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Tue, 13 Jun 2023 16:00:03 -0600 Subject: [PATCH 01/12] Trained a linear model on plates 1 and 2 and calculated p --- .../data/model_properties.tsv | 1162 +++++++++++++++++ .../nbconverted/train_model.py | 182 +++ .../train_model.ipynb | 254 ++++ 3 files changed, 1598 insertions(+) create mode 100644 1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv create mode 100644 1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py create mode 100644 1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv b/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv new file mode 100644 index 0000000..9a238e8 --- /dev/null +++ b/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv @@ -0,0 +1,1162 @@ +feature r2_score p_value cell_count_coef plate_coef Null_coef WT_coef +Cytoplasm_AreaShape_Area 0.18474653920991335 1.1102230246251565e-16 -0.014366895583179384 0.16455192415455927 -0.23170835918151744 0.23170835918151778 +Cytoplasm_AreaShape_BoundingBoxArea 0.17189193243211054 1.1102230246251565e-16 -0.016323544414939307 0.20781946368794818 -0.17741893610840856 0.1774189361084092 +Cytoplasm_AreaShape_BoundingBoxMaximum_X 0.0006686522828195374 0.8477981783245556 -0.00020982393246391827 -0.002611737414587152 -0.02402534842193505 0.024025348421934985 +Cytoplasm_AreaShape_BoundingBoxMaximum_Y 0.004623520471195297 0.04861314077390888 -0.0009847817393303932 0.0012232505317791575 -0.05727241082936399 0.05727241082936387 +Cytoplasm_AreaShape_BoundingBoxMinimum_X 0.006643417228757387 0.008157038547171647 0.0038512013186171377 -0.0539343420742183 0.02167513974965026 -0.021675139749650487 +Cytoplasm_AreaShape_BoundingBoxMinimum_Y 0.0065068094079506755 0.009230214384759461 0.00481213451282194 -0.07467209079560387 -0.0028819431758143124 0.0028819431758139308 +Cytoplasm_AreaShape_Center_X 0.0008253840774573584 0.790237195752141 0.0017318404680637819 -0.026991759598672196 -0.00152286257861017 0.0015228625786100278 +Cytoplasm_AreaShape_Center_Y 0.0006476256396724667 0.8552666776527195 0.0018503618710664477 -0.03598224063554693 -0.03102832323811212 0.031028323238111867 +Cytoplasm_AreaShape_Compactness 0.0037210957930179367 0.1037319371043417 0.004257913124454014 -0.08431896166431146 -0.07765357386835678 0.0776535738683562 +Cytoplasm_AreaShape_ConvexArea 0.17982532454241573 1.1102230246251565e-16 -0.015633316704335737 0.19129038444518412 -0.20178093241798328 0.2017809324179838 +Cytoplasm_AreaShape_Eccentricity 0.004410350060662371 0.05830630055513919 -0.004982139383815284 0.0952028627189675 0.07662923769481841 -0.07662923769481776 +Cytoplasm_AreaShape_EquivalentDiameter 0.1494028496298394 1.1102230246251565e-16 -0.013932887661676883 0.16805131059321238 -0.1898453524123195 0.18984535241231992 +Cytoplasm_AreaShape_EulerNumber 0.001101561345625779 0.6857490404406357 0.0024104555849951514 -0.044024588888653085 -0.028692996185100357 0.028692996185100066 +Cytoplasm_AreaShape_Extent 0.010328495037530416 0.00026280138242185114 -0.0020499702462758434 0.012781197306802189 -0.07709512873755808 0.07709512873755797 +Cytoplasm_AreaShape_FormFactor 0.0051660518090178575 0.03040574483600289 -0.005589818784788794 0.10276991017516758 0.06932684755362556 -0.06932684755362487 +Cytoplasm_AreaShape_MajorAxisLength 0.12473749755865948 1.1102230246251565e-16 -0.01623183353864438 0.22430666790882142 -0.10375595998225381 0.10375595998225472 +Cytoplasm_AreaShape_MaxFeretDiameter 0.13582702830372229 1.1102230246251565e-16 -0.015693436821142704 0.208634455148764 -0.13419750390414664 0.13419750390414742 +Cytoplasm_AreaShape_MaximumRadius 0.1168327128685529 1.1102230246251565e-16 -0.013228504768089028 0.16673326314309245 -0.15070398435580012 0.15070398435580062 +Cytoplasm_AreaShape_MeanRadius 0.11640369081620916 1.1102230246251565e-16 -0.013266275613819548 0.1676720889026541 -0.14922959482847814 0.14922959482847867 +Cytoplasm_AreaShape_MedianRadius 0.11775107386397887 1.1102230246251565e-16 -0.013351006949437685 0.16880369669042444 -0.1499329201906602 0.14993292019066073 +Cytoplasm_AreaShape_MinFeretDiameter 0.14570799655408517 1.1102230246251565e-16 -0.012903500759889842 0.14898974125779602 -0.20317230066823588 0.2031723006682362 +Cytoplasm_AreaShape_MinorAxisLength 0.14568815204182783 1.1102230246251565e-16 -0.01171497300810986 0.1256130097232725 -0.2241913595616944 0.2241913595616945 +Cytoplasm_AreaShape_Orientation 0.00011232417337903033 0.993789681225094 -0.0008252930461758963 0.015078370132820918 0.00984536023256886 -0.009845360232568757 +Cytoplasm_AreaShape_Perimeter 0.13717574847089586 1.1102230246251565e-16 -0.010833583190902659 0.111447221849869 -0.2267313332606949 0.22673133326069494 +Cytoplasm_AreaShape_Solidity 0.01637981962202517 7.261393928370552e-07 -0.0044779741052228105 0.05295541845348736 -0.0653603140505073 0.06536031405050742 +Cytoplasm_AreaShape_Zernike_0_0 0.011439088937936548 9.074874710390812e-05 -0.0004049954243073187 -0.01934675273346631 -0.10525426512650221 0.10525426512650188 +Cytoplasm_AreaShape_Zernike_1_1 0.005596581406647094 0.020829660673047035 -0.00030829971541755294 -0.013072592528001317 -0.07331222170092558 0.07331222170092534 +Cytoplasm_AreaShape_Zernike_2_0 0.027911489803901013 6.3754557189099614e-12 -0.0037495390128448793 0.02827167255498375 -0.1208690817252653 0.12086908172526517 +Cytoplasm_AreaShape_Zernike_2_2 0.001321453572769915 0.6045612850898732 -0.002818920183897007 0.052186560187874535 0.036443620720813766 -0.03644362072081342 +Cytoplasm_AreaShape_Zernike_3_1 5.495999858551315e-07 0.9999998395215051 -4.9533755058258953e-05 0.0009974538229946088 0.0009714589025408957 -0.0009714589025408884 +Cytoplasm_AreaShape_Zernike_3_3 0.00031945520938991834 0.9563082435525218 -0.0013852221551404306 0.02567059924298695 0.01801561614894007 -0.0180156161489399 +Cytoplasm_AreaShape_Zernike_4_0 0.0027762270130549105 0.22003256070437405 0.0040300380514377235 -0.07091489295095138 -0.03690084874195006 0.036900848741949606 +Cytoplasm_AreaShape_Zernike_4_2 0.004051373007551873 0.07889856864035405 -0.004465972918278303 0.07442874211648193 0.023782013299101415 -0.02378201329910099 +Cytoplasm_AreaShape_Zernike_4_4 0.003137681995709074 0.16605046030928194 -0.0030562606489673567 0.06566326453041352 0.07689643374231767 -0.07689643374231717 +Cytoplasm_AreaShape_Zernike_5_1 0.0010675082288147397 0.6985940560612804 0.0024913675211216168 -0.047039312956034855 -0.03598212694359602 0.03598212694359569 +Cytoplasm_AreaShape_Zernike_5_3 0.0011295617537078195 0.6752267302181656 -0.00209974631145622 0.043262931116291176 0.04521666277577188 -0.04521666277577155 +Cytoplasm_AreaShape_Zernike_5_5 0.0005966185987893446 0.8730362010730379 0.0018503474668604045 -0.03511948725382365 -0.027478189453467274 0.02747818945346703 +Cytoplasm_AreaShape_Zernike_6_0 0.0022850810579421177 0.3175609369441468 0.0033749300870345154 -0.06651657454357736 -0.060246362267949514 0.06024636226794903 +Cytoplasm_AreaShape_Zernike_6_2 2.970658701462181e-05 0.9995404325247685 0.0001860767220049336 -0.002163602814667448 0.002867824038275239 -0.0028678240382752434 +Cytoplasm_AreaShape_Zernike_6_4 0.007918293552767297 0.0025352718563537158 -0.006839721077140726 0.12816145103295765 0.09475557652661619 -0.09475557652661531 +Cytoplasm_AreaShape_Zernike_6_6 0.0011181228145985544 0.6795205828221265 -0.0017575224654418577 0.03818270864745717 0.04595928648884736 -0.04595928648884706 +Cytoplasm_AreaShape_Zernike_7_1 0.005385854279391866 0.02508114297550923 0.0030574462643623455 -0.07119669627524755 -0.09959671835650799 0.09959671835650744 +Cytoplasm_AreaShape_Zernike_7_3 0.0019179086405771706 0.4116481021941131 0.003210530594643503 -0.06217246391482098 -0.052767816344329845 0.0527678163443294 +Cytoplasm_AreaShape_Zernike_7_5 0.001032442676744627 0.7118637178323952 0.0014998235947499192 -0.0337508111855445 -0.04402247611497913 0.04402247611497885 +Cytoplasm_AreaShape_Zernike_7_7 3.5766377674884e-05 0.9993365740153008 0.00033478746114242304 -0.007137653175124886 -0.008196147972625776 0.00819614797262572 +Cytoplasm_AreaShape_Zernike_8_0 0.0014528502275646948 0.5581676915178619 0.001034380278109718 -0.027776660335313008 -0.04888184873812218 0.04888184873812193 +Cytoplasm_AreaShape_Zernike_8_2 1.461736388130852e-05 0.9998875728891521 0.00029765458734849645 -0.005447373387437035 -0.003588450730478407 0.0035884507304783705 +Cytoplasm_AreaShape_Zernike_8_4 0.001596521357308922 0.5097792329261129 -0.0015606591041984036 0.037071909930880734 0.05384286566879661 -0.0538428656687963 +Cytoplasm_AreaShape_Zernike_8_6 0.0024511655886019224 0.2811452603365874 -0.0022950321101087743 0.03184765053510717 -0.014123752556755425 0.014123752556755558 +Cytoplasm_AreaShape_Zernike_8_8 0.0023878378749209173 0.2945966556464452 -0.003804538066846134 0.06960391941405278 0.045772361561530035 -0.045772361561529584 +Cytoplasm_AreaShape_Zernike_9_1 0.010062470539738855 0.00033850358119946744 -0.00195379139899465 0.01128891885339868 -0.07715201861704793 0.0771520186170478 +Cytoplasm_AreaShape_Zernike_9_3 0.0006214273596650832 0.8644589281830433 0.0010707097489291812 -0.024702901186262553 -0.033931856936258026 0.03393185693625782 +Cytoplasm_AreaShape_Zernike_9_5 0.00024343336737187027 0.9733039708451275 0.00121062764921286 -0.021712917281225683 -0.012772628776978757 0.012772628776978618 +Cytoplasm_AreaShape_Zernike_9_7 0.00085930298869763 0.7774832717413938 -0.0007174932145907056 0.006106450509730906 -0.020262057605748954 0.020262057605748937 +Cytoplasm_AreaShape_Zernike_9_9 0.0007003810085535189 0.8363914278888568 0.0019985911415759455 -0.0380192275027919 -0.030033967134088443 0.030033967134088173 +Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928875 0.03426765582386959 -0.005507817489283557 0.09900261499000954 0.059009099631753326 -0.059009099631752694 +Cytoplasm_Correlation_Correlation_DAPI_RFP 0.01647709493797045 6.594567361073445e-07 -0.006627974205122522 0.09667752833493706 -0.021433259287394056 0.0214332592873945 +Cytoplasm_Correlation_Correlation_GFP_RFP 0.00199587704315185 0.39006033120792405 0.0018136544577989986 -0.04256969265267932 -0.06046439275583843 0.06046439275583807 +Cytoplasm_Correlation_Costes_DAPI_GFP 0.12668875220153752 1.1102230246251565e-16 -0.007711576775701956 0.22597520296845497 0.4421891625863014 -0.44218916258629926 +Cytoplasm_Correlation_Costes_DAPI_RFP 0.058169420512800385 1.1102230246251565e-16 -0.012945726387770452 0.27949855271431157 0.33132311992936303 -0.3313231199293609 +Cytoplasm_Correlation_Costes_GFP_DAPI 0.07171136556320179 1.1102230246251565e-16 -0.0027105789835034006 0.11578817655987528 0.3050791104063639 -0.30507911040636265 +Cytoplasm_Correlation_Costes_GFP_RFP 0.016622614806175218 5.709150631139082e-07 -0.0014483839868720961 0.05829414008342612 0.14829604795815957 -0.14829604795815893 +Cytoplasm_Correlation_Costes_RFP_DAPI 0.19802434686833292 1.1102230246251565e-16 -0.019314341764655584 0.4442044078430728 0.6063003461727692 -0.6063003461727656 +Cytoplasm_Correlation_K_DAPI_GFP 0.008743794398715199 0.001175018238829817 -0.007234226804901091 0.1290649328631211 0.07351383959747056 -0.07351383959746972 +Cytoplasm_Correlation_K_DAPI_RFP 0.006214930262296137 0.012005574295978816 -0.0012012309935377358 0.0025386693453899184 -0.06555298649074703 0.0655529864907469 +Cytoplasm_Correlation_K_GFP_DAPI 0.005581927725120228 0.02110119235699781 0.005338620229683115 -0.08979893093517345 -0.03183237383401087 0.031832373834010344 +Cytoplasm_Correlation_K_GFP_RFP 0.018068858300495982 1.3558242606404036e-07 0.0069266749065762115 -0.1513700131877782 -0.18477915531346473 0.18477915531346353 +Cytoplasm_Correlation_K_RFP_DAPI 0.008078073617189152 0.0021861367942233034 -0.0019245395858671626 0.05666869649934925 0.11147939829574158 -0.11147939829574104 +Cytoplasm_Correlation_K_RFP_GFP 0.013895972690740388 8.368139717385148e-06 -0.007563196441933077 0.15446621286821482 0.15725015850203122 -0.1572501585020301 +Cytoplasm_Correlation_Manders_DAPI_GFP 0.05682677261087277 1.1102230246251565e-16 0.010518674516646192 -0.14244033562193323 0.07924108904246813 -0.07924108904246867 +Cytoplasm_Correlation_Manders_DAPI_RFP 0.005763126348429903 0.017972049149014624 0.005868469798047356 -0.10457084029436119 -0.05910909782432623 0.05910909782432558 +Cytoplasm_Correlation_Manders_GFP_DAPI 0.0017588441984072167 0.45839917038707967 -0.001973042743801615 0.04429604253452643 0.05748543996101668 -0.05748543996101633 +Cytoplasm_Correlation_Manders_GFP_RFP 0.004491751136942446 0.054405552358263165 0.005191871384719637 -0.09629033638095433 -0.06783545372254793 0.06783545372254729 +Cytoplasm_Correlation_Manders_RFP_DAPI 0.00014556354045824893 0.9898029487911398 -0.0003623708894618319 0.009384543768966584 0.015699018868684318 -0.015699018868684234 +Cytoplasm_Correlation_Manders_RFP_GFP 0.04845038492229414 1.1102230246251565e-16 0.012828051473727224 -0.1971131896075793 0.00032516819538896957 -0.00032516819538996877 +Cytoplasm_Correlation_Overlap_DAPI_GFP 0.005221386256011873 0.02897080599445301 -0.0027778261739751877 0.03487271771463569 -0.03221917381704655 0.032219173817046656 +Cytoplasm_Correlation_Overlap_DAPI_RFP 0.044700483277687386 1.1102230246251565e-16 -0.010406894851813729 0.14855634474260931 -0.04699562327442729 0.046995623274427925 +Cytoplasm_Correlation_Overlap_GFP_RFP 0.021379891039952637 4.900020389442261e-09 0.004200186245216157 -0.1104968116943455 -0.18905254048020215 0.1890525404802012 +Cytoplasm_Correlation_RWC_DAPI_GFP 0.025089935195876323 1.1402523369952178e-10 0.010217635563995615 -0.1638920903800971 -0.028099911153720125 0.02809991115371921 +Cytoplasm_Correlation_RWC_DAPI_RFP 0.0003289541781874572 0.9539638694053856 -0.0012842748998345597 0.02150149694848574 0.007242765248701214 -0.0072427652487010875 +Cytoplasm_Correlation_RWC_GFP_DAPI 0.004081527711480959 0.07693405431750933 0.004567066900040596 -0.07683843676814384 -0.027303956378637917 0.027303956378637466 +Cytoplasm_Correlation_RWC_GFP_RFP 0.027099958811750424 1.4641954315663952e-11 0.011337522020371674 -0.22572106743274456 -0.2117292249742364 0.2117292249742348 +Cytoplasm_Correlation_RWC_RFP_DAPI 0.0011901726515266553 0.6526040608105218 -0.002486853537587508 0.048560798433762334 0.042530077114726075 -0.04253007711472573 +Cytoplasm_Correlation_RWC_RFP_GFP 0.038262913928817754 1.1102230246251565e-16 0.013669864639138789 -0.22737150139842816 -0.0709547615938646 0.07095476159386327 +Cytoplasm_Granularity_10_DAPI 0.0008700166154410605 0.7734418488461786 -0.0009939826604942938 0.011451554098633802 -0.015755423018955862 0.015755423018955883 +Cytoplasm_Granularity_10_GFP 0.00013034106753262442 0.9917393246479322 -0.0008710848509915131 0.01643817208107074 0.012544977529985825 -0.012544977529985712 +Cytoplasm_Granularity_10_RFP 0.003761712566425768 0.1003252629539767 0.003566809207567474 -0.05475771180547825 0.00029267731881536113 -0.0002926773188156387 +Cytoplasm_Granularity_11_DAPI 0.005180989848801887 0.030011757301464215 0.005159648740914257 -0.08693348556675273 -0.03136190394869354 0.03136190394869302 +Cytoplasm_Granularity_11_GFP 0.002334802229619015 0.3062712118045383 0.0036441557354518323 -0.0693886760335573 -0.05503383838597352 0.05503383838597304 +Cytoplasm_Granularity_11_RFP 0.008314081070003199 0.001755350747339346 0.002034919438439572 -0.015209613918705886 0.06614763428265089 -0.06614763428265082 +Cytoplasm_Granularity_12_DAPI 0.002751908698306993 0.22416741203247903 0.0015234576059579715 -0.015570811666440941 0.03230072143294328 -0.03230072143294329 +Cytoplasm_Granularity_12_GFP 0.0041811951465038355 0.0707640952162436 0.004441508030217465 -0.07326958888922164 -0.02055889880146168 0.020558898801461258 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0.003442344409582695 -0.022632994770781373 0.12464133296710009 -0.12464133296709992 +Nuclei_Texture_Variance_RFP_3_00_256 0.004956430253377442 0.03648817770541646 0.004839538752108734 -0.0798649616319095 -0.022521642911276202 0.022521642911275737 +Nuclei_Texture_Variance_RFP_3_01_256 0.0051424931520230865 0.031037247670725132 0.004942714940089977 -0.0816701210774374 -0.02342361115892609 0.023423611158925613 +Nuclei_Texture_Variance_RFP_3_02_256 0.005079289471157655 0.03279449873612705 0.004878055900939448 -0.08033849712465993 -0.02203370479382112 0.02203370479382067 +Nuclei_Texture_Variance_RFP_3_03_256 0.0048366468761950054 0.04047219083871889 0.004774401774033 -0.07874137281610233 -0.02201823689416045 0.022018236894159993 diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py new file mode 100644 index 0000000..36c63cb --- /dev/null +++ b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py @@ -0,0 +1,182 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Fit a linear model on cell morphology features +# +# Our aim is to determine which features significantly contribute to NF1 genotype adjusted by covariates. This notebook is adapted from Dr. Way at: +# https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/fit_linear_model.ipynb + +# In[ ]: + + +from pathlib import Path +import pandas as pd + +from sklearn.linear_model import LinearRegression +import numpy as np +from scipy import stats + + +# In[ ]: + + +# Get the current working directory +cwd = Path.cwd() + +if (cwd / ".git").is_dir(): + root_dir = cwd + +else: + root_dir = None + for parent in cwd.parents: + if (parent / ".git").is_dir(): + root_dir = parent + break + +# Check if a Git root directory was found +if root_dir is None: + raise FileNotFoundError("No Git root directory found.") + + +# In[ ]: + + +sys.path.append(f"{root_dir}/utils") +import preprocess_utils as ppu + + +# In[ ]: + + +data_path = Path("data") + +data_path.mkdir( + parents=True, exist_ok=True +) # Create the parent directories if they don't exist + + +# In[ ]: + + +plate_path = Path( + f"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data" +) + +filename1 = "Plate_1_sc_norm.parquet" +filename2 = "Plate_2_sc_norm.parquet" + +path1 = plate_path / filename1 +path2 = plate_path / filename2 + +po1 = ppu.Preprocess_data(path=path1) +po2 = ppu.Preprocess_data(path=path2) + +plate1df = po1.df # Returns the dataframe generated by the csv +plate2df = po2.df # Returns the dataframe generated by the csv + +# Set plate column: +plate1df["Metadata_plate"] = "1" +plate2df["Metadata_plate"] = "2" + +common_columns = list(plate1df.columns.intersection(plate2df.columns)) +plate1df = plate1df.loc[:, common_columns] +plate2df = plate2df.loc[:, common_columns] + +# Combine the plate dataframes: +cp_df = pd.concat([plate1df, plate2df], axis="rows") +cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)] +cp_df.reset_index(inplace=True, drop=True) + + +# In[ ]: + + +pcp_features = po1.remove_meta(cp_df) +cp_features = pcp_features.columns + + +# ## Fit linear model + +# In[ ]: + + +# Setup linear modeling framework +variables = ["Metadata_number_of_singlecells", "Metadata_plate"] +X = cp_df.loc[:, variables] + +# Add dummy matrix of categorical genotypes +genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype).astype(int) + +X = pd.concat([X, genotype_x], axis=1) +X_arr = X.values + +dfreg = X.shape[1] +dfres = X.shape[0] - dfreg - 1 + + +# In[ ]: + + +# Fit linear model for each feature +lm_results = [] +i = 0 +for cp_feature in cp_features: + # Subset CP data to each individual feature (univariate test) + y = cp_df.loc[:, cp_feature] + + # Fit linear model + lm = LinearRegression(fit_intercept=True) + lm_result = lm.fit(X=X_arr, y=y) + + # Extract Beta coefficients + # (contribution of feature to X covariates) + coef = lm_result.coef_ + + # Estimate fit (R^2) + r2_score = lm.score(X=X_arr, y=y) + + y_pred = lm.predict(X_arr) + + # Calculate the residuals + residuals = y.values - y_pred + + # Calculate the Sum of Squares Regression (SSR) + ssr = np.sum((y_pred - np.mean(y.values)) ** 2) + + # Calculate the Mean Squares Regression (MSR) + msr = ssr / dfreg + + # Calculate the Sum of Squares Residual (SSE) or residual sum of squares + sse = np.sum(residuals**2) + + # Calculate the Mean Squares Residual (MSE) + mse = sse / dfres + + # Calculate the F-value + f_value = msr / mse + + # Calculate the p-value for the F-statistic + p_value = 1 - stats.f.cdf(f_value, dfreg, dfres) + + # Add results to a growing list + lm_results.append([cp_feature, r2_score, p_value] + list(coef)) + + +# In[ ]: + + +# Convert results to a pandas DataFrame +lm_results = pd.DataFrame( + lm_results, + columns=[ + "feature", + "r2_score", + "p_value", + "cell_count_coef", + "plate_coef", + "Null_coef", + "WT_coef", + ], +) + +lm_results.to_csv(f"{data_path}/model_properties.tsv", sep="\t", index=False) diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb new file mode 100644 index 0000000..4111cc8 --- /dev/null +++ b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "9A4gVcqrrm" + }, + "source": [ + "# Fit a linear model on cell morphology features\n", + "\n", + "Our aim is to determine which features significantly contribute to NF1 genotype adjusted by covariates. This notebook is adapted from Dr. Way at:\n", + "https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/fit_linear_model.ipynb" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "Lq6i6b1L6q" + }, + "source": [ + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "import numpy as np\n", + "from scipy import stats" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "261w9QY97P" + }, + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "jG7YhMVm79" + }, + "source": [ + "sys.path.append(f\"{root_dir}/utils\")\n", + "import preprocess_utils as ppu" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "EwwGYfhdHG" + }, + "source": [ + "data_path = Path(\"data\")\n", + "\n", + "data_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "9bdkn1aAvb" + }, + "source": [ + "plate_path = Path(\n", + " f\"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data\"\n", + ")\n", + "\n", + "filename1 = \"Plate_1_sc_norm.parquet\"\n", + "filename2 = \"Plate_2_sc_norm.parquet\"\n", + "\n", + "path1 = plate_path / filename1\n", + "path2 = plate_path / filename2\n", + "\n", + "po1 = ppu.Preprocess_data(path=path1)\n", + "po2 = ppu.Preprocess_data(path=path2)\n", + "\n", + "plate1df = po1.df # Returns the dataframe generated by the csv\n", + "plate2df = po2.df # Returns the dataframe generated by the csv\n", + "\n", + "# Set plate column:\n", + "plate1df[\"Metadata_plate\"] = \"1\"\n", + "plate2df[\"Metadata_plate\"] = \"2\"\n", + "\n", + "common_columns = list(plate1df.columns.intersection(plate2df.columns))\n", + "plate1df = plate1df.loc[:, common_columns]\n", + "plate2df = plate2df.loc[:, common_columns]\n", + "\n", + "# Combine the plate dataframes:\n", + "cp_df = pd.concat([plate1df, plate2df], axis=\"rows\")\n", + "cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)]\n", + "cp_df.reset_index(inplace=True, drop=True)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "K57KpbjyKN" + }, + "source": [ + "pcp_features = po1.remove_meta(cp_df)\n", + "cp_features = pcp_features.columns" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "2111g4dKC3" + }, + "source": [ + "## Fit linear model" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "YeIyN5E3xy" + }, + "source": [ + "# Setup linear modeling framework\n", + "variables = [\"Metadata_number_of_singlecells\", \"Metadata_plate\"]\n", + "X = cp_df.loc[:, variables]\n", + "\n", + "# Add dummy matrix of categorical genotypes\n", + "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype).astype(int)\n", + "\n", + "X = pd.concat([X, genotype_x], axis=1)\n", + "X_arr = X.values\n", + "\n", + "dfreg = X.shape[1]\n", + "dfres = X.shape[0] - dfreg - 1" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "4gra1XWKi3" + }, + "source": [ + "# Fit linear model for each feature\n", + "lm_results = []\n", + "i = 0\n", + "for cp_feature in cp_features:\n", + " # Subset CP data to each individual feature (univariate test)\n", + " y = cp_df.loc[:, cp_feature]\n", + "\n", + " # Fit linear model\n", + " lm = LinearRegression(fit_intercept=True)\n", + " lm_result = lm.fit(X=X_arr, y=y)\n", + "\n", + " # Extract Beta coefficients\n", + " # (contribution of feature to X covariates)\n", + " coef = lm_result.coef_\n", + "\n", + " # Estimate fit (R^2)\n", + " r2_score = lm.score(X=X_arr, y=y)\n", + "\n", + " y_pred = lm.predict(X_arr)\n", + "\n", + " # Calculate the residuals\n", + " residuals = y.values - y_pred\n", + "\n", + " # Calculate the Sum of Squares Regression (SSR)\n", + " ssr = np.sum((y_pred - np.mean(y.values)) ** 2)\n", + "\n", + " # Calculate the Mean Squares Regression (MSR)\n", + " msr = ssr / dfreg\n", + "\n", + " # Calculate the Sum of Squares Residual (SSE) or residual sum of squares\n", + " sse = np.sum(residuals**2)\n", + "\n", + " # Calculate the Mean Squares Residual (MSE)\n", + " mse = sse / dfres\n", + "\n", + " # Calculate the F-value\n", + " f_value = msr / mse\n", + "\n", + " # Calculate the p-value for the F-statistic\n", + " p_value = 1 - stats.f.cdf(f_value, dfreg, dfres)\n", + "\n", + " # Add results to a growing list\n", + " lm_results.append([cp_feature, r2_score, p_value] + list(coef))" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "DnyVti231X" + }, + "source": [ + "# Convert results to a pandas DataFrame\n", + "lm_results = pd.DataFrame(\n", + " lm_results,\n", + " columns=[\n", + " \"feature\",\n", + " \"r2_score\",\n", + " \"p_value\",\n", + " \"cell_count_coef\",\n", + " \"plate_coef\",\n", + " \"Null_coef\",\n", + " \"WT_coef\",\n", + " ],\n", + ")\n", + "\n", + "lm_results.to_csv(f\"{data_path}/model_properties.tsv\", sep=\"\\t\", index=False)" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From 0102a2a226085cd9069c151d9b60365d90e19d27 Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Tue, 13 Jun 2023 16:03:27 -0600 Subject: [PATCH 02/12] Seperated features based on compartment and significance --- .../nbconverted/seperate_compartments.py | 167 ++++++++++ .../seperate_compartments.ipynb | 296 ++++++++++++++++++ 2 files changed, 463 insertions(+) create mode 100644 3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py create mode 100644 3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py new file mode 100644 index 0000000..534aef1 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Here the features are seperated according to compartments for later analysis + +# In[1]: + + +#%%--%%| +r"""°°° +## Imports +°°°""" + + +# In[ ]: + + +import pandas as pd +import numpy as np +from pathlib import Path +from sklearn.preprocessing import LabelEncoder +from sklearn.linear_model import LogisticRegression + +from sklearn.metrics import ( + confusion_matrix, + ConfusionMatrixDisplay, + precision_score, + accuracy_score, +) +from sklearn.model_selection import train_test_split + +import matplotlib.pyplot as plt +import seaborn as sns +import itertools + +from joblib import dump, load + + +# ## Find the git root Directory + +# In[ ]: + + +# Get the current working directory +cwd = Path.cwd() + +if (cwd / ".git").is_dir(): + root_dir = cwd + +else: + root_dir = None + for parent in cwd.parents: + if (parent / ".git").is_dir(): + root_dir = parent + break + +# Check if a Git root directory was found +if root_dir is None: + raise FileNotFoundError("No Git root directory found.") + + +# ## Create the output path if it doesn't exist + +# In[ ]: + + +output_path = Path("data") + +output_path.mkdir( + parents=True, exist_ok=True +) # Create the parent directories if they don't exist + + +# ## Import the model data as a dataframe + +# In[ ]: + + +feature_properties = pd.read_csv( + root_dir + / "1.train_models/linear_reg_plate_1_2_norm_data/data" + / "model_properties.tsv", + sep="\t", +) + + +# ## Seperate cell data by channel + +# In[ ]: + + +# A map for comparments to channel +compartment2channel = {"actin": "RFP", "er": "GFP", "nucleus": "DAPI"} + +# Create a Dictionary to hold the comparment data as dataframes +compartment_data = { + compartment: feature_properties[feature_properties["feature"].str.contains(channel)] + for compartment, channel in compartment2channel.items() +} + + +# ## Find the features that use more than one compartment + +# In[ ]: + + +# Create a list of all possible comparment pairs +pairs = list(itertools.combinations(compartment_data.keys(), 2)) + +# Use a list of possible compartments for find compartment agnostic features +pos_compartments = list(compartment2channel.values()) + +# Get the features that do not belong to any compartment specifically +other_compartment = feature_properties[ + ~feature_properties["feature"].str.contains("|".join(pos_compartments)) +] + +# Find intersected rows between each pair of compartments +for pair in pairs: + + # Create placeholder dataframes for each comparment in the pair + df1 = compartment_data[pair[0]] + df2 = compartment_data[pair[1]] + + # Get duplicate features between the two compartment dataframes + intersection = pd.concat([df1, df2], axis=0) + intersection = intersection[intersection.duplicated(subset="feature", keep=False)] + + # Remove any duplicate features already added to the other compartment dataframe + other_compartment = pd.concat([other_compartment, intersection], axis=0) + other_compartment = other_compartment.drop_duplicates(subset="feature") + + +# ## Organize the data according to compartment in one dataframe + +# In[ ]: + + +# Remove features from each compartment dataframe if they are duplicates in other dataframes, or if the features only exist in the other_compartment dataframe +compartment_data = { + compartment: compartment_data[compartment][ + ~compartment_data[compartment]["feature"].isin(other_compartment["feature"]) + ] + for compartment, channel in compartment2channel.items() +} + +compartment_data["other"] = other_compartment + +# Create a compartment column for each comparment dataframe +for compartment, df in compartment_data.items(): + compartment_data[compartment]["compartment"] = len(df) * [compartment] + +# Concatenate the rows of DataFrames to create the plot below +concatenated_df = pd.concat(compartment_data.values(), ignore_index=True) + + +# ## Save the dataframes + +# In[ ]: + + +concatenated_df.to_csv(output_path / "feature_compartments.tsv", sep="\t", index=False) + +# Create a dataframe with only significant models +concatenated_df.loc[concatenated_df["p_value"] >= 0.05].to_csv( + output_path / "significant_feature_compartments.tsv", sep="\t", index=False +) diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb new file mode 100644 index 0000000..dcf45c8 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "vk3QsHOEjG" + }, + "source": [ + "# Here the features are seperated according to compartments for later analysis" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "SyZ3qa8iz3" + }, + "source": [ + "# %%--%%| \n", + "r\"\"\"°°°\n", + "## Imports\n", + "°°°\"\"\"" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "\n" + }, + { + "output_type": "execute_result", + "execution_count": 1, + "data": { + "text/plain": "'°°°\\n## Imports\\n°°°'" + }, + "metadata": {} + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "FKd1dIo45E" + }, + "source": [ + "import sys\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "from sklearn.metrics import (\n", + " confusion_matrix,\n", + " ConfusionMatrixDisplay,\n", + " precision_score,\n", + " accuracy_score,\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "from collections import Counter\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import itertools\n", + "\n", + "from joblib import dump, load" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "ItCxTfECNg" + }, + "source": [ + "## Find the git root Directory" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "zjh0SKYFgl" + }, + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "OTkUmqTBVt" + }, + "source": [ + "## Create the output path if it doesn't exist" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "W8zS6OpdNq" + }, + "source": [ + "output_path = Path(\"data\")\n", + "\n", + "output_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "KChOdgSroB" + }, + "source": [ + "## Import the model data as a dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "aYwhtTNAgx" + }, + "source": [ + "feature_properties = pd.read_csv(\n", + " root_dir\n", + " / \"1.train_models/linear_reg_plate_1_2_norm_data/data\"\n", + " / \"model_properties.tsv\",\n", + " sep=\"\\t\",\n", + ")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "QmPiNYcAdw" + }, + "source": [ + "## Seperate cell data by channel" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "wSEsy3OWnz" + }, + "source": [ + "# A map for comparments to channel\n", + "compartment2channel = {\"actin\": \"RFP\", \"er\": \"GFP\", \"nucleus\": \"DAPI\"}\n", + "\n", + "# Create a Dictionary to hold the comparment data as dataframes\n", + "compartment_data = {\n", + " compartment: feature_properties[feature_properties[\"feature\"].str.contains(channel)]\n", + " for compartment, channel in compartment2channel.items()\n", + "}" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "hAmkVoVyDd" + }, + "source": [ + "## Find the features that use more than one compartment" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "CQscjFvYen" + }, + "source": [ + "# Create a list of all possible comparment pairs\n", + "pairs = list(itertools.combinations(compartment_data.keys(), 2))\n", + "\n", + "# Use a list of possible compartments for find compartment agnostic features\n", + "pos_compartments = list(compartment2channel.values())\n", + "\n", + "# Get the features that do not belong to any compartment specifically\n", + "other_compartment = feature_properties[\n", + " ~feature_properties[\"feature\"].str.contains(\"|\".join(pos_compartments))\n", + "]\n", + "\n", + "# Find intersected rows between each pair of compartments\n", + "for pair in pairs:\n", + " # Create placeholder dataframes for each comparment in the pair\n", + " df1 = compartment_data[pair[0]]\n", + " df2 = compartment_data[pair[1]]\n", + "\n", + " # Get duplicate features between the two compartment dataframes\n", + " intersection = pd.concat([df1, df2], axis=0)\n", + " intersection = intersection[intersection.duplicated(subset=\"feature\", keep=False)]\n", + "\n", + " # Remove any duplicate features already added to the other compartment dataframe\n", + " other_compartment = pd.concat([other_compartment, intersection], axis=0)\n", + " other_compartment = other_compartment.drop_duplicates(subset=\"feature\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "UwxezzhMqY" + }, + "source": [ + "## Organize the data according to compartment in one dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "iTJOXFvhuT" + }, + "source": [ + "# Remove features from each compartment dataframe if they are duplicates in other dataframes, or if the features only exist in the other_compartment dataframe\n", + "compartment_data = {\n", + " compartment: compartment_data[compartment][\n", + " ~compartment_data[compartment][\"feature\"].isin(other_compartment[\"feature\"])\n", + " ]\n", + " for compartment, channel in compartment2channel.items()\n", + "}\n", + "\n", + "compartment_data[\"other\"] = other_compartment\n", + "\n", + "# Create a compartment column for each comparment dataframe\n", + "for compartment, df in compartment_data.items():\n", + " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n", + "\n", + "# Concatenate the rows of DataFrames to create the plot below\n", + "concatenated_df = pd.concat(compartment_data.values(), ignore_index=True)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "NtZG4xLsob" + }, + "source": [ + "## Save the dataframes" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "NUwlraBlOz" + }, + "source": [ + "concatenated_df.to_csv(output_path / \"feature_compartments.tsv\", sep=\"\\t\", index=False)\n", + "\n", + "# Create a dataframe with only significant models\n", + "concatenated_df.loc[concatenated_df[\"p_value\"] >= 0.05].to_csv(\n", + " output_path / \"significant_feature_compartments.tsv\", sep=\"\\t\", index=False\n", + ")" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 68de51e59ba2761eb0ae29364ba1e7c30b2199be Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Tue, 13 Jun 2023 16:09:23 -0600 Subject: [PATCH 03/12] Added the feature-seperated data --- .../data/feature_compartments.tsv | 1162 +++++++++++++++++ .../data/significant_feature_compartments.tsv | 278 ++++ 2 files changed, 1440 insertions(+) create mode 100644 3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv create mode 100644 3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv new file mode 100644 index 0000000..0e28574 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv @@ -0,0 +1,1162 @@ +feature r2_score p_value -log_p cell_count_coef plate_coef Null_coef WT_coef compartment +Cytoplasm_Granularity_10_RFP 0.0037617125664257 0.1003252629539767 0.998589693300464 0.0035668092075674 -0.0547577118054782 0.0002926773188153 -0.0002926773188156 actin +Cytoplasm_Granularity_11_RFP 0.0083140810700031 0.0017553507473393 2.75563609149773 0.0020349194384395 -0.0152096139187058 0.0661476342826508 -0.0661476342826508 actin +Cytoplasm_Granularity_12_RFP 0.0170346819190725 3.793612946001446e-07 6.420946981299408 -0.0086265789374558 0.1743870117677087 0.1719627433359708 -0.1719627433359696 actin +Cytoplasm_Granularity_13_RFP 0.0165343642920898 6.230874355361848e-07 6.205451006139428 -0.0013168894588963 0.0558714466217581 0.1466440481927371 -0.1466440481927365 actin +Cytoplasm_Granularity_14_RFP 0.0098501891702583 0.0004140812515598 3.382914432691137 0.0048833782463788 -0.0696969351968968 0.0221031201207769 -0.0221031201207772 actin +Cytoplasm_Granularity_15_RFP 0.034125533803847 1.0436096431476471e-14 13.981461916591304 -0.0117844982540997 0.2411427339495568 0.2469237178768606 -0.2469237178768588 actin +Cytoplasm_Granularity_16_RFP 0.0053252103386409 0.0264527395149072 1.5775293446269791 -0.0011101117449085 0.0381216351097182 0.0866696542894571 -0.0866696542894567 actin +Cytoplasm_Granularity_1_RFP 0.0797662447888054 1.1102230246251563e-16 15.954589770191005 0.0128883932375536 -0.2919905507118198 -0.3863669662163332 0.3863669662163309 actin +Cytoplasm_Granularity_2_RFP 0.0265761238729764 2.5021873462094387e-11 10.601680176505536 0.0055619175677227 -0.0646411039510882 0.0858438944240999 -0.0858438944241001 actin +Cytoplasm_Granularity_3_RFP 0.0247453188850123 1.6195356167258979e-10 9.790609496571722 0.0058182685634424 -0.0713392817216137 0.0744939196170529 -0.0744939196170531 actin +Cytoplasm_Granularity_4_RFP 0.0237508762052655 4.450164681202296e-10 9.351623917375452 0.0064129908206857 -0.0842132596546174 0.0591334779903649 -0.0591334779903652 actin +Cytoplasm_Granularity_5_RFP 0.0152669315254935 2.179214255049189e-06 5.661700068876637 0.0056152854233033 -0.0771713489077849 0.0376466898321505 -0.0376466898321508 actin +Cytoplasm_Granularity_6_RFP 0.0135583503755253 1.163737040010826e-05 4.934145142515971 0.0050243723061764 -0.0672611482336932 0.041049393082168 -0.0410493930821682 actin +Cytoplasm_Granularity_7_RFP 0.0068161411470623 0.006973486447476 2.156550038783899 0.0014019186773225 -0.0053777353069579 0.0665650786828842 -0.066565078682884 actin +Cytoplasm_Granularity_8_RFP 0.0091773954485366 0.0007819895285646 3.1067990624350723 0.001378595879782 -0.00155702630656 0.0808152695319881 -0.0808152695319879 actin +Cytoplasm_Granularity_9_RFP 0.0118764346599471 5.95448596415471e-05 4.22515572411686 0.0020014519906748 -0.009960290523731 0.0856360697971641 -0.085636069797164 actin +Cytoplasm_Intensity_IntegratedIntensityEdge_RFP 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0.0307447981671124 -0.0307447981671128 other +Nuclei_Correlation_Correlation_DAPI_RFP 0.0168432113581571 4.5875122578475924e-07 6.338422762302043 0.0001420515628276 -0.0350490459478889 -0.135272315080336 0.1352723150803355 other +Nuclei_Correlation_Costes_DAPI_RFP 0.0653722521256117 1.1102230246251563e-16 15.954589770191005 -0.0155537567470738 0.3232207860753828 0.3462710611474306 -0.3462710611474281 other +Nuclei_Correlation_Costes_RFP_DAPI 0.0179149635141995 1.580573694148768e-07 6.801185250399516 -0.0069226331361114 0.1511162345908681 0.1839903398971243 -0.1839903398971231 other +Nuclei_Correlation_K_DAPI_RFP 0.0194414548671663 3.4387600544860675e-08 7.46359812677996 -0.0011421086465465 -0.0138844420153685 -0.1294089263510944 0.129408926351094 other +Nuclei_Correlation_K_RFP_DAPI 0.0094306032780084 0.0006159131487293 3.210480524340274 -0.0025832658832332 0.0698811387485057 0.1241833980800298 -0.1241833980800292 other +Nuclei_Correlation_Manders_DAPI_RFP 0.0003548282703975 0.9473520293555764 0.0234886102283102 0.0012507589608186 -0.0252429773741715 -0.0247630483994902 0.02476304839949 other +Nuclei_Correlation_Manders_RFP_DAPI 0.0006703475639939 0.8471927376411222 0.0720177757710915 -0.0010841604293917 0.0246815908186427 0.0329931187023357 -0.0329931187023355 other +Nuclei_Correlation_Overlap_DAPI_RFP 0.0333243308966041 2.398081733190338e-14 13.620136019040071 -0.0059602732081601 0.0670969512739812 -0.1009397653269125 0.1009397653269126 other +Nuclei_Correlation_RWC_DAPI_RFP 0.0094345830398724 0.0006136031128202 3.212112445869353 -0.0064997025887186 0.1059483776570577 0.0248408015139323 -0.0248408015139317 other +Nuclei_Correlation_RWC_RFP_DAPI 0.0077959130337511 0.0028392756370917 2.54679244408663 -0.0067345404406127 0.1181910975788669 0.0603729435911854 -0.0603729435911847 other +Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928 0.0342676558238695 1.4651156036990791 -0.0055078174892835 0.0990026149900095 0.0590090996317533 -0.0590090996317526 other +Cytoplasm_Correlation_Costes_DAPI_GFP 0.1266887522015375 1.1102230246251563e-16 15.954589770191005 -0.0077115767757019 0.2259752029684549 0.4421891625863014 -0.4421891625862992 other +Cytoplasm_Correlation_Costes_GFP_DAPI 0.0717113655632017 1.1102230246251563e-16 15.954589770191005 -0.0027105789835034 0.1157881765598752 0.3050791104063639 -0.3050791104063626 other +Cytoplasm_Correlation_K_DAPI_GFP 0.0087437943987151 0.0011750182388298 2.9299553921482704 -0.007234226804901 0.1290649328631211 0.0735138395974705 -0.0735138395974697 other +Cytoplasm_Correlation_K_GFP_DAPI 0.0055819277251202 0.0211011923569978 1.6756930034969135 0.0053386202296831 -0.0897989309351734 -0.0318323738340108 0.0318323738340103 other +Cytoplasm_Correlation_Manders_DAPI_GFP 0.0568267726108727 1.1102230246251563e-16 15.954589770191005 0.0105186745166461 -0.1424403356219332 0.0792410890424681 -0.0792410890424686 other +Cytoplasm_Correlation_Manders_GFP_DAPI 0.0017588441984072 0.4583991703870796 0.3387561770280998 -0.0019730427438016 0.0442960425345264 0.0574854399610166 -0.0574854399610163 other +Cytoplasm_Correlation_Overlap_DAPI_GFP 0.0052213862560118 0.028970805994453 1.5380394221564682 -0.0027778261739751 0.0348727177146356 -0.0322191738170465 0.0322191738170466 other +Cytoplasm_Correlation_RWC_DAPI_GFP 0.0250899351958763 1.1402523369952178e-10 9.94299902898127 0.0102176355639956 -0.1638920903800971 -0.0280999111537201 0.0280999111537192 other +Cytoplasm_Correlation_RWC_GFP_DAPI 0.0040815277114809 0.0769340543175093 1.1138813802448684 0.0045670669000405 -0.0768384367681438 -0.0273039563786379 0.0273039563786374 other +Cells_Correlation_Correlation_DAPI_GFP 0.0120121827110191 5.223024593559167e-05 4.282077929203808 -0.0082808309727361 0.1441675777139306 0.0694567357186915 -0.0694567357186906 other +Cells_Correlation_Costes_DAPI_GFP 0.1287306386618018 1.1102230246251563e-16 15.954589770191005 -0.0159824554903969 0.3647135469087196 0.489929153154612 -0.4899291531546089 other +Cells_Correlation_Costes_GFP_DAPI 0.0725736544592836 1.1102230246251563e-16 15.954589770191005 -0.0026318368579147 0.1147914535206022 0.3059586759868545 -0.3059586759868531 other +Cells_Correlation_K_DAPI_GFP 0.0136828962432701 1.0305116863840167e-05 4.986947078796643 -0.0069037054251493 0.1448310793092576 0.1593185470532011 -0.1593185470532 other +Cells_Correlation_K_GFP_DAPI 0.0233561640861427 6.641966976417281e-10 9.177703288026311 0.0106113349978678 -0.2105839322869011 -0.195371542857158 0.1953715428571565 other +Cells_Correlation_Manders_DAPI_GFP 0.0293732964052595 1.4205303600078878e-12 11.8475494799678 0.0023528815075556 -0.0006521660881691 0.1461827762937873 -0.1461827762937869 other +Cells_Correlation_Manders_GFP_DAPI 0.0288016347820732 2.556732603409273e-12 11.592314690407887 0.0054258488493823 -0.0601105034701542 0.095882513249416 -0.0958825132494161 other +Cells_Correlation_Overlap_DAPI_GFP 0.0035468255613133 0.1196052879976049 0.9222496188642316 0.0036878280018297 -0.0762026908630778 -0.0803167972663356 0.080316797266335 other +Cells_Correlation_RWC_DAPI_GFP 0.0360072716847414 1.4432899320127037e-15 14.840646417884166 -0.0131163880035569 0.2607626491605052 0.2434094004456848 -0.243409400445683 other +Cells_Correlation_RWC_GFP_DAPI 0.0308150581209546 3.2163161023390785e-13 12.49264127498724 0.0046841958972369 -0.0440597428885552 0.1150220960993887 -0.1150220960993887 other +Nuclei_Correlation_Correlation_DAPI_GFP 0.0055975849365728 0.020811189718118 1.6817030916651352 -0.0027688599181534 0.0668973607803623 0.1001597794611853 -0.1001597794611848 other +Nuclei_Correlation_Costes_DAPI_GFP 0.0917366799612873 1.1102230246251563e-16 15.954589770191005 -0.0148851988489012 0.3298836164767035 0.415994629305783 -0.4159946293057804 other +Nuclei_Correlation_K_DAPI_GFP 0.016042896725708 1.0133859220662345e-06 5.994225133212328 -0.0052536283477645 0.1225024387479125 0.1718156460245471 -0.171815646024546 other +Nuclei_Correlation_K_GFP_DAPI 0.0285387179234908 3.3494318429916348e-12 11.475028855166148 0.0073553503104055 -0.1690029733733402 -0.2302331058924449 0.2302331058924435 other +Nuclei_Correlation_Manders_DAPI_GFP 0.0053512008800226 0.0258562561936261 1.5874343577291878 0.0030718253235459 -0.0405363457199373 0.0275093511301013 -0.0275093511301014 other +Nuclei_Correlation_Manders_GFP_DAPI 0.0007620641436754 0.8138200273288702 0.0894716267813413 -0.001236679277527 0.0275934027714221 0.0353280921836848 -0.0353280921836845 other +Nuclei_Correlation_Overlap_DAPI_GFP 0.0184591607692713 9.185241267584132e-08 7.036909431649485 -0.0044333294839917 0.0498850683396133 -0.0751730352919696 0.0751730352919697 other +Nuclei_Correlation_RWC_DAPI_GFP 0.0189891962562755 5.4080493305797006e-08 7.266959355546295 -0.0038782622974246 0.1027773274118242 0.1776477320738891 -0.1776477320738881 other +Nuclei_Correlation_RWC_GFP_DAPI 0.0233397702291401 6.753302361772738e-10 9.170483805424231 -0.003969839060068 0.1083301098895058 0.1947086060182426 -0.1947086060182416 other diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv new file mode 100644 index 0000000..2d6580a --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv @@ -0,0 +1,278 @@ +feature r2_score p_value -log_p cell_count_coef plate_coef Null_coef WT_coef compartment +Cytoplasm_Granularity_10_RFP 0.0037617125664257 0.1003252629539767 0.998589693300464 0.0035668092075674 -0.0547577118054782 0.0002926773188153 -0.0002926773188156 actin +Cytoplasm_Intensity_MaxIntensityEdge_RFP 0.0041062181249255 0.0753599505618121 1.1228593951241392 0.0045262376910301 -0.0756843357731082 -0.0251370088643017 0.0251370088643013 actin +Cytoplasm_Intensity_MaxIntensity_RFP 0.0006338886393454 0.8601030189140192 0.0654495279815697 0.000867179392635 -0.0101453541758644 0.0131088137209483 -0.0131088137209483 actin +Cytoplasm_Location_CenterMassIntensity_X_RFP 0.0007866405503756 0.804708211979182 0.094361566712008 0.0017169009877283 -0.0269326316982328 -0.0022247178774269 0.0022247178774267 actin +Cytoplasm_Location_CenterMassIntensity_Y_RFP 0.000656748031542 0.8520359307733876 0.0695420904356676 0.0018676072589494 -0.0362763848469504 -0.0311478870516149 0.0311478870516147 actin +Cytoplasm_Location_MaxIntensity_X_RFP 0.0006511994566603 0.854002759221398 0.068540726134541 0.0017044759477919 -0.0277609512892808 -0.006420169001206 0.0064201690012059 actin +Cytoplasm_Location_MaxIntensity_Y_RFP 0.0005174445711179 0.8994334515678626 0.0460309642651058 0.0017716198893725 -0.0322825850893986 -0.020782759203354 0.0207827592033538 actin +Cytoplasm_RadialDistribution_FracAtD_RFP_1of4 0.0033651275081684 0.1385223330158672 0.8584802026020388 0.0029849522202331 -0.0434724530061837 0.0099280259285686 -0.0099280259285688 actin +Cytoplasm_RadialDistribution_FracAtD_RFP_2of4 0.002975672842688 0.1885793123074293 0.7245059523384797 0.0030269409493874 -0.0455550859462327 0.0040126553094279 -0.0040126553094281 actin +Cytoplasm_RadialDistribution_FracAtD_RFP_3of4 0.0016069979523801 0.5063546830315826 0.295545169084659 0.0026992343448554 -0.0441222182551672 -0.0108237713132133 0.010823771313213 actin +Cytoplasm_RadialDistribution_FracAtD_RFP_4of4 0.0021831595011518 0.3417661919858089 0.4662709005392059 -0.0029029977041593 0.0458248976215361 0.0049397765203576 -0.0049397765203573 actin +Cytoplasm_RadialDistribution_MeanFrac_RFP_1of4 0.0041730961569144 0.0712474758032654 1.1472305174827089 0.0042402008201553 -0.0685295849438199 -0.0137860330167952 0.0137860330167948 actin +Cytoplasm_RadialDistribution_RadialCV_RFP_1of4 0.0026260879754109 0.2466609527386866 0.6078995953513914 0.0004574287005823 -0.0210658753236876 -0.0577644151212342 0.0577644151212339 actin 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0.0246815908186427 0.0329931187023357 -0.0329931187023355 other +Cytoplasm_Correlation_Manders_GFP_DAPI 0.0017588441984072 0.4583991703870796 0.3387561770280998 -0.0019730427438016 0.0442960425345264 0.0574854399610166 -0.0574854399610163 other +Cytoplasm_Correlation_RWC_GFP_DAPI 0.0040815277114809 0.0769340543175093 1.1138813802448684 0.0045670669000405 -0.0768384367681438 -0.0273039563786379 0.0273039563786374 other +Cells_Correlation_Overlap_DAPI_GFP 0.0035468255613133 0.1196052879976049 0.9222496188642316 0.0036878280018297 -0.0762026908630778 -0.0803167972663356 0.080316797266335 other +Nuclei_Correlation_Manders_GFP_DAPI 0.0007620641436754 0.8138200273288702 0.0894716267813413 -0.001236679277527 0.0275934027714221 0.0353280921836848 -0.0353280921836845 other From 5587dbbb479d2e0e7765336d89a3f3979aa99239 Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Tue, 13 Jun 2023 16:10:57 -0600 Subject: [PATCH 04/12] Creates feature importance plots --- .../figures/combined_plates_lm.png 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n<8g1r-{<=OPx1fYwU|b;+T(s*evB(|v(L-OOGls5zW=`ffwbG- literal 0 HcmV?d00001 diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/visualize_linear_model.r b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/visualize_linear_model.r new file mode 100644 index 0000000..da80658 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/visualize_linear_model.r @@ -0,0 +1,69 @@ +# This notebook is adapted from Dr. Way at: +# https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/visualize_linear_model.ipynb + +suppressPackageStartupMessages(library(ggplot2)) +suppressPackageStartupMessages(library(dplyr)) + +input_dir <- "../../1.train_models/linear_reg_plate_1_2_norm_data/data" +lm_file <- file.path(input_dir, "model_properties.tsv") + +output_fig_dir <- "figures" +lm_fig <- file.path(output_fig_dir, "combined_plates_lm.png") + +# Load and process linear model data +lm_df <- readr::read_tsv( + lm_file, col_types = readr::cols(.default = "d", feature = "c") +) + +# Arrange by absolute value coefficient +# Split out components of feature name for visualization +lm_df <- lm_df %>% + dplyr::arrange(desc(abs(Null_coef))) %>% + tidyr::separate( + feature, + into = c( + "compartment", + "feature_group", + "measurement", + "channel", + "parameter1", + "parameter2" + ), + sep = "_", + remove = FALSE + ) %>% + dplyr::mutate(channel_cleaned = channel) + +# Clean channel for visualization +lm_df$channel_cleaned <- + dplyr::recode( + lm_df$channel_cleaned, + "DAPI" = "nuclei", + "RFP" = "actin", + "GFP" = "ER", + .default = "other", + .missing="other" + ) + +print(dim(lm_df)) +head(lm_df, 10) + +lm_fig_gg <- ( + ggplot(lm_df, aes(x = cell_count_coef, y = WT_coef)) + + geom_point(aes(size = r2_score, color = channel_cleaned), alpha = 0.7) + + geom_vline(xintercept = 0, linetype = "dashed", color = "red") + + geom_density2d(color="black", show.legend = FALSE) + + theme_bw() + + guides( + color = guide_legend(title = "Channel\n(if applicable)", order = 1), + size = guide_legend(title = "R2 score") + ) + + ylab("WT genotype contribution (LM beta coefficient)") + + xlab("Cell count contribution (LM beta coefficient)") + + ggtitle("How CellProfiler features contribute\nto NF1 genotype and cell density") +) + +# Output figure +ggsave(lm_fig, lm_fig_gg, dpi = 500, height = 6, width = 6) + +lm_fig_gg diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/visualize_sig_linear_model.r b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/visualize_sig_linear_model.r new file mode 100644 index 0000000..28eb8f3 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/visualize_sig_linear_model.r @@ -0,0 +1,69 @@ +# This notebook is adapted from Dr. Way at: +# https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/visualize_linear_model.ipynb + +suppressPackageStartupMessages(library(ggplot2)) +suppressPackageStartupMessages(library(dplyr)) + +input_dir <- "data" +lm_file <- file.path(input_dir, "significant_feature_compartments.tsv") + +output_fig_dir <- "figures" +lm_fig <- file.path(output_fig_dir, "combined_plates_sig_lm.png") + +# Load and process linear model data +lm_df <- readr::read_tsv( + lm_file, col_types = readr::cols(.default = "d", feature = "c") +) + +# Arrange by absolute value coefficient +# Split out components of feature name for visualization +lm_df <- lm_df %>% + dplyr::arrange(desc(abs(Null_coef))) %>% + tidyr::separate( + feature, + into = c( + "compartment", + "feature_group", + "measurement", + "channel", + "parameter1", + "parameter2" + ), + sep = "_", + remove = FALSE + ) %>% + dplyr::mutate(channel_cleaned = channel) + +# Clean channel for visualization +lm_df$channel_cleaned <- + dplyr::recode( + lm_df$channel_cleaned, + "DAPI" = "nuclei", + "RFP" = "actin", + "GFP" = "ER", + .default = "other", + .missing="other" + ) + +print(dim(lm_df)) +head(lm_df, 10) + +lm_fig_gg <- ( + ggplot(lm_df, aes(x = cell_count_coef, y = WT_coef)) + + geom_point(aes(size = r2_score, color = channel_cleaned), alpha = 0.7) + + geom_vline(xintercept = 0, linetype = "dashed", color = "red") + + geom_density2d(color="black", show.legend = FALSE) + + theme_bw() + + guides( + color = guide_legend(title = "Channel\n(if applicable)", order = 1), + size = guide_legend(title = "R2 score") + ) + + ylab("WT genotype contribution (LM beta coefficient)") + + xlab("Cell count contribution (LM beta coefficient)") + + ggtitle("How CellProfiler significant features contribute\nto NF1 genotype and cell density") +) + +# Output figure +ggsave(lm_fig, lm_fig_gg, dpi = 500, height = 6, width = 6) + +lm_fig_gg From 9b17e332ee45f38dcce29376c7d83a9c89b73bcb Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Tue, 13 Jun 2023 16:15:35 -0600 Subject: [PATCH 05/12] Created volcano plot of genotype significance --- .../feature_importances/make_volcano.ipynb | 231 ++++++++++++++++++ .../nbconverted/make_volcano.py | 126 ++++++++++ 2 files changed, 357 insertions(+) create mode 100644 3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/make_volcano.ipynb create mode 100644 3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/make_volcano.py diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/make_volcano.ipynb b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/make_volcano.ipynb new file mode 100644 index 0000000..e98ef61 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/make_volcano.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "rJWC1AmHfC" + }, + "source": [ + "# Here we understand the relationship between WT contribution and model significance\n", + "\n", + "Each linear model is regressed to each morphology feature according to covariate data" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "SyZ3qa8iz3" + }, + "source": [ + "# %%--%%| \n", + "r\"\"\"°°°\n", + "## Imports\n", + "°°°\"\"\"" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "\n" + }, + { + "output_type": "execute_result", + "execution_count": 1, + "data": { + "text/plain": "'°°°\\n## Imports\\n°°°'" + }, + "metadata": {} + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "FKd1dIo45E" + }, + "source": [ + "import sys\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "from sklearn.metrics import (\n", + " confusion_matrix,\n", + " ConfusionMatrixDisplay,\n", + " precision_score,\n", + " accuracy_score,\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "from collections import Counter\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import itertools\n", + "\n", + "from joblib import dump, load" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "ItCxTfECNg" + }, + "source": [ + "## Find the git root Directory" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "zjh0SKYFgl" + }, + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "CbTwVL3qAB" + }, + "source": [ + "output_path = Path(\"figures\")\n", + "\n", + "output_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "KChOdgSroB" + }, + "source": [ + "## Import the model data as a dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "aYwhtTNAgx" + }, + "source": [ + "concatenated_df = pd.read_csv(\n", + " root_dir\n", + " / \"3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data\"\n", + " / \"feature_compartments.tsv\",\n", + " sep=\"\\t\",\n", + ")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "LKIM4ZTgdx" + }, + "source": [ + "## Plot the WT contribution for our features vs the Morphology Feature" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "KxriUeQ8di" + }, + "source": [ + "# Set significance threshold and fold change threshold\n", + "significance_threshold = -np.log10(0.05)\n", + "fold_change_threshold = 1.0\n", + "colors = [\"red\", \"green\", \"blue\", \"black\"]\n", + "\n", + "# Set the figure size\n", + "plt.figure(figsize=(15, 10))\n", + "\n", + "# Create the scatter plot with size and color differentiation\n", + "sns.scatterplot(\n", + " data=concatenated_df,\n", + " x=\"WT_coef\",\n", + " y=\"-log_p\",\n", + " size=\"r2_score\",\n", + " hue=\"compartment\",\n", + " palette=colors,\n", + ")\n", + "\n", + "# Add threshold lines\n", + "plt.axhline(\n", + " significance_threshold, color=\"cyan\", linestyle=\"--\", label=\"Significance Threshold\"\n", + ")\n", + "\n", + "# For adding contours:\n", + "# sns.kdeplot(data=concatenated_df, x='WT_coef', y='-log_p', levels=3, color='yellow', alpha=0.5)\n", + "\n", + "# Set plot labels and title\n", + "plt.xlabel(\"WT_coef\")\n", + "plt.ylabel(\"-log10(p-value)\")\n", + "plt.title(\"Significance of WT Contribution per Morphology Feature\")\n", + "\n", + "# Show the plot\n", + "plt.savefig(\n", + " output_path / \"feature_wt_contribution_and_significance_with_correlation.png\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Installed tk event loop hook.\n" + }, + { + "data": { + "image/png": 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Ki8iXwpOt74X5Idq/J0QvDgvRrJn+jrp40UVW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\n+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/Qy8faJN7zN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sgqyV5EBGzblvP8jRuwcqVxd5SSxtbWlsqVKzN16lROnz7N5cuX2bJlC3379i1wk8y1a1mb\nZG46fRoefNB44pFZWFmBp2fedU5Ops8iIiJym8TERN555x1q1KiBo6MjgYGBDBkyhCtXrhR4rejo\naEaNGkXFihVxcHCgYsWKjB49mpiYmByvOXXqFIMHD6ZixYrY29vj5uZGs2bNmDZtGikpKXfxyERE\nzEs7yoiYSeXKlamsV5iIiIiIiIiIGCUkwJdfwltv/Te2cCH8/DOsX2/saMnOlCng73/Ht7W1hfLl\njSem5Mbd/Y5vYXbhCeHMPzg/15rQG6FEJUYR6KYX6xS2vXvzrlmxAgYNKl6fVwVx84m6u3H5cu4n\nr50/D0ePmukIJl9fGD4cXnst55oqVXTsUjEWFRVFXFwcGRkZODs74+fnZ+lIIiJ5SkpKon379uzZ\ns4eAgAB69uzJ+fPnWbhwIevWrWPPnj1Uyef2axEREbRs2ZIzZ85QpUoVevXqxbFjx5gxYwYbNmxg\n9+7deP5P0+iuXbvo0KEDCQkJ1K5dm169ehEbG8uOHTt4+eWXWb16NZs3b8bWVk8/i0jRpx1lRERE\nRERERETE/CIjYcKErGOzZ8OPP2Y998jFxdhI07r1Xd3W1xfGjs29pmHD4vX8d0ZGBnEpcXnWJaYm\nmiFN6ZOfk8Du4rSwUmPt2rxrvvvOTDvz2NhA//7GrrrsWFnBvHl31bQnlhEXF8fevXvp1asXlSpV\nokqVKtx///0sXryYyMhIS8cTEcnVBx98wJ49e2jZsiWnTp1iyZIl7N27lylTphAeHs6QIUPyvdbo\n0aM5c+YMffr0ITg4mCVLlnD06FFGjhzJqVOnePnll7NcM2LECBISEpg0aRLHjx/n559/ZuPGjbea\nbbZv3873339fmA9ZRMRkrAwGg8HSIURKiu++++6urn/66acLKUnRULduXQCOHTtm4SQiIiIiIiJS\n5KxfD927Zz/XqhW88orxzxERUK4cNGhgbJ5xcLjrW1+9Cr16Zb8TiKMj7NoF99xz17cxm5ikGB5b\n9hibzm7KscbaypqzL52lkkcl8wUrJc6cgerVc6+ZPx+eecY8eYqrd96BiRNzr3niCfj+e7A218s/\nL1yAkSON369uduhUrw5ffGE8Ls7V1UxBpDCkpKTwyy+/0KdPH7J7WmT06NGMHz8+yw4KImJeGRkZ\nBAcHA1CzZk2szfRNPz4+noSEBJydnXFxcTHLPQsiJSUFX19fYmNjOXjwIPf8zz9WGzZsyJEjR9i/\nfz9NmjTJda3Q0FCCgoKwtbXl4sWLmXbVSk5Opnz58kRFRRESEoKvry8AN27cwM3NDWdnZ+Li4rL8\nf5k2bRovv/wyL7zwAnPmzCmkR128FORzV8+fiVie9r4SKUSDBg3CysqqwNcZDAasrKxKXKOMiIiI\niIiISI5yO/vozz+Nb126wE8/Ffp5Nf7+sGoVzJ0LM2dCVJTxiffu3eHjj6Fq1UK9ncl5OHow/v7x\nuTbKdK/enbKOxWibnGLEy8vYeLVqVfbz3t7QqZM5ExVPXbvm3Sjz+ONmbJIBqFjR2JkTFQXXrxsb\n9Tw8tJNMMRUWFsbTTz+dbZMMwPTp03n22WfVKCNSity4cYMNGzawbNkyzp49e2u8WrVq9O3bly5d\nuhSZppk///yT2NhYqlatmqVJBqBv374cOXKEtWvX5tko8+uvv5KRkUGbNm2yHD3n4ODAww8/zIIF\nC/jll18YNGgQAHZ2dvlqWvLy8sr/gxIRsSA1yogUonfeeeeOGmVERERERETMISMjg6SkJBwcHLDR\nOSBiaXXq5F1TowY4OZnk9v7+8NZbMHQoJCWBnZ2xH6eQe3LMpo5PHca1GsfHf36cZa6aZzVmdZ2F\nu2MxfXBFXNmyMGcOJCTApv/pVQoMhN9+M26KJLmrVAlq1YKTJ7Of9/eHxo3NGsmoOH9jkEwOHz5M\nXFzux9TNmTOHGTNmYGurp05ESrr9+/fz+uuvExsbm2XuzJkzfPzxx3zxxRdMnjyZpk2bWiBhZocP\nHwagcQ5/Gd4cP3LkSKGstWDBgkxrOTg4cP/997Nt2zY++eQTxo0bd2suJCSE2bNnY2dnx1NPPZW/\nByQiYmH6155IIXrvvfcsHUFERERERCSLmJgYrl69ypdffklwcDCVK1fmhRdeICAgQK+aFssJCICa\nNeH/tyfP1gsvgL29ySLY2ZWcBgZPJ09eu+81+tTuw6Sdkzhy7Qjuju6MvHckHat2JNAt0NIRS7SA\nAPjhB7h2zbizTEICPPigsfEjUB/6fPH3hw0boEMH43FWtwsIgK1bS87Xq1jG7btF5OT06dMkJSXh\nqmO1REq0/fv3M3LkSFJTU3Oti42NZeTIkcycOdPizTIXL14EICgoKNv5m+MXLlww2Vpz586lQ4cO\nvPHGG3z33XfUq1eP69ev88cffxAQEMD69eupUaNG/h6QiIiFqVFGREREREREpASLjIxk6tSpfPTR\nR5nGZ8+ezQsvvMCECRPw9va2UDop1fz9YeVKaNMGIiOzzn/1lToMCsjT2RNPZ0++7fUt8anx2FrZ\n4u2ir29z8fY2vtWta+kkxVelSvDHH3DihPHEo/R0eOwxaNRITTJy9ypVqpRnTZUqVXB0dDR9GBGx\nmPj4eF5//fU8m2RuSk1N5fXXX2fNmjUWPYbpxo0bADg7O2c7fzNbXjtn3c1aNWvWZOfOnfTu3ZuD\nBw9y4sQJAKysrHjggQeoq38EiUgxYs4TXUUEWL16NRMmTLB0DBERERERKSV27NiRpUnmpi+++II1\na9ZgMBjMnErk/9WqBX//DR9/bPxzpUowYAAcPgyPPgpubpZOWCy5Objh7+qvJhkplgICoH17WLAA\nvv0WunVTk4wUjsaNG+f5JPeLL76oY5dESrgNGzZke9xSbmJjY9mwYYOJEhUfW7dupWHDhqSmprJ1\n61auX7/OuXPneOutt1i4cCGtWrUiPDzc0jFFRPJFjTIiZrZq1Sref/99S8cQEREREZFSICwsjPHj\nx+daM2HCBK5evWqmRCL/w8oKypeHV1+Fbdtg926YNw8aNIAyZSydTkQsyMrK+FYUGAwG4lPiSUpN\nsnQUuQs+Pj7Mnz8/x/lhw4YRqJ3MREq8ZcuWmfW6wnLzSLiEhIRs5+Pj4wFwy0ej+Z2sFRUVRb9+\n/UhNTWXDhg088MADuLm5UalSJSZOnMiLL77I+fPn+eyzz/L/oERELEiNMiIiIiIiIiIlVGJiIkeP\nHs215sKFC7d+ESpiMTY24OdnPI7Jglvai4jcLjU9lUuxl/hi/xf0XdqXx5c/zvpT6wmNC7V0NLkD\nDg4OdO3ale3bt9O0adNb4xUrVmT+/Pl88MEHeHl5WTChiJhafHw8Z86cuaNrz5w5Y9GfmypUqADA\n5cuXs52/OV6xYkWTrLV+/XqioqJo0aIF5bLZ6q1fv34A/PHHH3neX0SkKNAegiIiIiIiIiIllFU+\nX4pvba3X0YiIiNwuNT2Vv0L+osP3HUhI/e8V96uDV1O1bFW2PL2Fih55PxkpRUuZMmW4//772bBh\nA3FxcWRkZODs7Iy/v3++/90kIsVXTjuoFOT6vI5wM5WGDRsCcPDgwWznb443aNDAJGvdbJ5xd3fP\n9pqb49HR0XneX0SkKNBvwkRERERERERKKDc3N9q1a5drzT333JOv7blFRERKk9AboVmaZG46G32W\nPj/3ISw+zALJpDB4e3tTuXJlqlatSkBAgJpkREoJZ2dni15/N1q1aoW7uztnz57l0KFDWeZvHg31\n8MMP57lW586dsba2ZseOHYSFZf67LDk5mbVr12JjY0PXrl1vjfv7+wPw999/k56enmXNv/76C4BK\nlSrl9yGJiFiUGmVEzMxgMGAwGCwdQ0RERERESoGyZcvyySef5Prkz2effYaPj48ZU929qKgogoOD\nmTBhAiNGjOCnn37i0qVLpKSkWDqaiIiUAAaDgTXBa7JtkrnpYOhBrt24ZsZUIiJyt1xcXKhWrdod\nXVutWjWL7SYDYG9vz4gRIwB48cUXMx0DNXXqVI4cOULbtm1p0qTJrfFZs2ZRq1Yt3njjjUxrBQQE\n0L9/f1JSUhg+fDhpaWm35saOHUt4eDhPPvkkvr6+t8Y7d+6Mg4MD586dY/z48WRkZNyaCw4O5p13\n3gGgb9++hfvARURMRI0yImb2zTffZPoHhIiIiIiIiCnVrl2blStXZtk1xtnZme+++y7TL1KLg7Cw\nMMaMGUOtWrV49913mT17Nk888QS1a9fmzz//JDk52dIRRUSkmItPjWftqbV51v0V8pcZ0hQvqalw\n+TJs3AgzZsCKFXDpEiQmWjqZiIjRnTZyFIUGkLfffpvmzZuza9cuqlevzmOPPUaLFi145ZVX8PHx\nYcGCBZnqIyIiCA4OJjQ0NMta06dPp2rVqixfvpxatWrx+OOPU79+fT7//HOqV6/O1KlTM9UHBATw\n2WefYWVlxaRJk6hevTp9+/blgQceoGHDhoSEhNC1a1cGDRpkyg+BiEihUaOMiIiIiIiISAnm6upK\nly5dOH78OGvXrmXq1KmsWLGCkydP0q9fvxzPmC+KkpKSmD59Ot99912Wufj4eDp37sylS5cskExE\nREoSGysbHGwc8qzLT01pkpAA27ZB/frQuTOMHg2PPAI1asAPP0BMjIUDiogAXbp0KfDPQO7u7nTp\n0sVEifLP0dGR33//nfHjx+Ps7MyqVau4cOECgwYN4uDBg1SpUiXfa3l7e7Nv3z5GjhxJSkoKK1eu\nJDY2lpdeeol9+/bh6emZ5ZoRI0awdetWevXqRUJCAqtXr+bgwYPcc889zJ49mzVr1mBra1uYD1lE\nxGSsDDoDRkRMpG7dugAcO3bMwklERERERKQkuHz5MjVr1iQhIeejMF566SU++eQTHBz05KWIiNy5\n1cGr6bW4V47zVlhx9qWzVC5b+dZYeno6165d499//+X8+fMEBQVRvXp1fH19sbOzM0Nqyzp6FBo1\ngvT07OfXr4euXc0aSUSKoYyMDIKDgwGoWbMm1taF/5r//fv3M3LkSFJTU/OstbOzY+bMmTRt2rTQ\nc0jJUpDPXT1/JmJ52lFGRERERERERIqF8PDwXJtkAFavXk1UVJSZEomISEnVLLAZlTwq5Tj/eL3H\nSc9IJyYpBoDExET++OMP7rnnHtq0acNTTz3FAw88QIMGDdiwYQM3btwwT3ALuX4dxo/PuUkG4NVX\n4do182USEclJ06ZNmTlzZp47y7i7u6tJRkSkhFKjjIiIiIiIiIiUKFZWVpaOICVdbs8Ei0iJEOgW\nyO8Df6ehX8NM41ZY8WjdR+lfrz91v6jL3P1ziU2K5cyZM3Ts2JGwsLBM9VFRUfTq1Yt//vnHnPHN\nLi4O1q7NvebECWNDjYhIUdC0aVPWrFnDuHHjqFatWqa5atWqMW7cONasWaMmGRGREkoHxYmIiIiI\niIhIseDt7Y2Liwvx8fE51vTs2ZOyZcuaMZWUGvHxEBEBy5bBvn1Qrhw88wwEBICnp6XTiYgJVPKo\nxC9P/MKpqFPsurQLB1sHmgQ0Yeu5rfRd2peU9BTe2PIGPSr14M033yQtLS3bdQwGAy+//DLr1q3D\ny8vLzI/CPAyG/PUQGgymzyIikl8uLi707duXvn37Eh8fT0JCAs7Ozri4uFg6moiImJgaZURERERE\nRESkWPD19WXUqFF89NFH2c7b29szcuRIHBwczJxMSry4OFi9GgYNyvxM8LRp8PTT8Omn4OtrsXgi\nYjpphjT6/tyX8u7lSctIY9zmcaRlZG6IiYqO4pdffsl1nT179hAXF1diG2WcnKBFC9izJ+eagABw\ndTVfJhGRgnBxcVGDjIhIKaJGGREREREREZFSKCEBIiPh8GGIjoa6dSEwEPz9LZ0sZw4ODowaNYor\nV67w7bffZppzdXVl7dq1lC9f3kLppEQ7cwaeeir7ue++g9q14ZVXwM7OvLlExORikmKITIwkMjEy\nx5qE1AQyMjLyXCu9BB/b5uUFH30E7dvnXPPGG+DnZ75MIiIiIiI5sbZ0ABERERERERExr8hI+Pxz\nqFEDHn7YuCFGkybQti2cOFG0j0Xw9fVl6tSpBAcH8+GHH/LSSy+xZMkSTpw4wX333afdZKTwXb8O\n77+fe82nn0JYmHnyiIhZ2Vrn/VrTa0nXqFOnTq41QUFBODk5FVasIqlRI5g8Ofu5IUPg8cfBxsas\nkUREREREsqUdZURERERERERKkdRU+Okn46u6/9epU9C6NRw6BEV5YxZPT088PT158803LR1FSoMb\nN2DLltxroqIgNhbKlTNPJhExGw9HD8qXKc+l65dyrIm0juT1t19n4BMDc6x566238C/K27YVgrJl\nYdgw6NMHFi6Ef/6BChXg+eeNxy6V0FOnRERERKQY0o4yIiIiIiIiIqVIWBi8807O81FRxkaaEnw6\nhBSmuDi4cMF4hteJExAaWrS3JLpTtvl4rZmVlelziIjZBbgG8FnHz3Kc93LyonFAY/zr+TN69Ohs\na55++mkeeeQRrK1L/q/j3d2hWjX44AP4+WfjDnb16qlJRkRERESKlpL/L3MRERERERERuSUiAqKj\nc6/59ltjnUiurlyBoUONz4g2agR16sB998GGDcbjikoKDw/o3Tv3mnLljM8Oi0iJY2VlRevyrZnd\ndTaeTp6Z5hr4NeDnfj8z+tfRBPoFMn78eI4cOcKzzz5Lu3btePrppzlw4ABTpkzBx8fHQo/AMqys\nwNERSkFvkIiIiIgUQzp6SURERERERKQUSU3NuyY5uWRuCiKFKDQUOnQw7iJzu/PnoVs3WL0auncv\nGc+QOjvDuHGwaFHOX0AffAAl/EgVkdIssEwgXat3pbJHZRJSE4hJjqF8mfKciTrDs2ue5YWmL1C+\nTHncHd3x9PRk9uzZJCYm4ujoiIODg6Xji4iIiIjI/1CjjIiIiIiIiEgp4uNjPEUmLS3nmgce0OYY\nkofff8/aJHO74cOhaVMIDDRfJlOqWBE2bTLuLBMT89+4rS1MnAgPP1wymoJEJEcV3StiY2XD0mNL\nWX5iOZfjLnOP/z0s6buE6l7VcXf87y9Oe3t77O3tLZhWRERERERyo0YZERERERERkVLEywv694fv\nv89+3soKXn4ZnJzMm0uKkago+Pzz3GuuXIHIyJLTKOPgAK1bw9GjcOSI8c3fH9q1M35RubpaOmGJ\nkZBgPPotLAxsbIzNfb6+oJ4DsTQrKyvKu5dnVItRPNHgCTIMGbjZu+Hm4GbpaCIiIiIiUkBqlBER\nEREREREpRVxdYfJkOH0a9uzJPGdtDT/+CBUqWCabFBOpqZl3VclJXJzJo5iVrS2UK2d869LF0mlK\npKtX4d134ZtvICXFOFamDLz9NgweDN7eFo0nAoCNtQ3+rjpqTURERESkONOesCIiIiIiIiKlTEAA\nrFoF27dDv37QoYPxyenTp6F7d3BxyedCGRmmjClFlasrNGyYd52fn+mzSIkRHg5PPw1ffvlfkwzA\n9eswdixMn17yeq9ERERECioxMZF33nmHGjVq4OjoSGBgIEOGDOHKlSsFWmf79u28//77dOvWDR8f\nH6ysrKhUqVKO9ampqWzatIkRI0ZQr149nJ2dcXJyonbt2rz66quEh4ff5SMTETEvK4PBYLB0CBEp\nmerWrQvAsWPHLJxERERERERykpRk3CDE2dl4zEmeYmONz2j/8AMcPw516sCAAcbzUdzdTZ5XiogD\nB6Bp05zn27WD5cvB09NskaR4278fmjXLed7WFs6cgYoVzZdJREREzC8jI4Pg4GAAatasibW1XvN/\nU1JSEg888AB79uwhICCANm3acP78efbt24ePjw979uyhSpUq+VqrUaNGHD58ONNYxYoVOX/+fLb1\nmzdvpkOHDgBUqlSJxo0bk5qayu7du4mIiMDf359t27ZRs2bNu3qMxVlBPnf1/JmI5elvFxERERER\nEZFSzNER3Nzy2SQTFQWzZkH16vDee/Dzz8b/Vq8OM2dCdLSJ00qRUaWKcRui7JQrB19/rSYZybeM\nDJg7N/eatDTYtcs8eURERESKog8++IA9e/bQsmVLTp06xZIlS9i7dy9TpkwhPDycIUOG5Hutjh07\n8sEHH7Bx48Z8NWtYW1vz6KOPsnfvXs6dO8fy5ctZs2YNZ86coVOnTly9epXBgwffzcMTETEr7Sgj\nIiajjlgRERERkRJm3Tp4+OHc57t1M18esazoaAgOhg8/hIMHjR1Xw4YZz/MKCrJ0OilGUlPh8cdh\nxYrc62bPhuHDzZNJRERELMOcO8qkpaWxY8cOVqxYwenTp0lMTMTJyYnq1avTp08f2rRpg62trcnu\nXxApKSn4+voSGxvLwYMHueeeezLNN2zYkCNHjrB//36aNGlSoLWvXr1KQEBArjvK5CYkJIRy5coB\ncP78eSqW0i0AtaOMSPFSNL67i4iIiIiIiEjRFhYGb76Ze82bbxrPTvH1NU8msayyZaFFC1i0COLj\njdsS+fhAUd8ePyYGIiNh/Xrj53WLFtCoEfj7G8/3kbt2I+UGkQmR/HHhD8Liw2jo35Da3rXxd/XH\nxjrr9lV2dtC2bd6NMv/zfJCIiIjIHdu0aRPTp08nLCws03h8fDwRERHs3r0bPz8/Ro0aRceOHS2U\n8j9//vknsbGxVK1aNUuTDEDfvn05cuQIa9euLXCjzN0KDAzEx8eH8PBwQkJCSm2jjIgUL/rpX0RE\nRERERETylpgI//yTe82RI8Y6KV3c3Y1vAAYDhIZCbKzxPJ0yZYyNU/b2ls14U3g4TJxoPELs9k2W\ny5Y1Ns40aVJ0shZTkQmRfLH/C97f/j5pGWm3xr2dvVnbfy2NAxpjb5P1Y9yzJ7z6qnF3mexUqACV\nKpkotIiIiJQqixYtYvr06XnWXbt2jTfffJPw8HAGDBhg+mC5OHz4MACNGzfOdv7m+JEjR8yW6aaY\nmBii//8YXn9/f7PfX0TkThTxl/iIiIiIiIiISJFgZVW4dVLyREfD8uVw331QuzbUrWt8mzgRrl2z\ndDpISYGvvoKZMzM3yYAxe/v2cPmyZbKVEOkZ6aw4sYLxv4/P1CQDEJEQwQPfPsDl69l/jP38YNWq\n7Df18fAwnuym511EMsvIyCAkJIQTJ05w4sQJQkJCSE9Pt3QsEZEi7eZOMgUxbdo0Nm3aZJpA+XTx\n4kUAgnI44vTm+IULF8yW6abZs2eTlpZG/fr1qVy5stnvLyJyJ9QoIyIiIiIiIiJ5c3WFNm1yr7n/\nfmOdlD5JScYmmX794Pz5/8avX4cPPoDnnjPu5mJJYWHwySc5zyclwbx5xoYauSPX4q8x/vfxOc4n\npSXx+d7PSU5LzjLn6AgPPADHj8OoUVCzJtSrB5MmGTerqltXfXgit4uMjGThwoU0a9aMOnXqUKdO\nHRo1asTMmTOJiIiwdDwRkSIpLS2twE0yN82YMYO0tLS8C03kxo0bADg7O2c77+LiAkBcXJzZMgH8\n/ffffPDBBwBMnjzZrPcWEbkbapQRERERERERkbx5esKnn+b8TLWVlbEJwdPTvLmkaIiIgNGjc55f\nswb+/1WwFhMVZTwSKjcrVhjr5I7EJMVwLT733YOWn1hOVGL2H2MnJ6heHSZPhu3bYcsWeO01KF8e\nrPVbTJFb4uLimDNnDs8++ywhISG3xsPDwxkzZgzvv//+rSMwRETkPzt27CAsLOyOrr127Ro7d+4s\n5ETF27Vr1+jTpw9JSUmMHj2aLl26WDqSiEi+6UdMEREREREREcmfOnVg6dKsu8a4uhrH69SxTC6x\nvGPHID4+95rZs8GCr8LNl/89kkkKxJCPj9//HsmUHQcH41FMvr5gY1MYyURKlqioKN5///0c52fN\nmqVdZUREsrFixYq7un758uWFlKTgXP//Z7CEhIRs5+P//9/ibm5uZskTFxdH165dOX/+PP369WPK\nlClmua+ISGFRo4yIiIiIiIiI5I+bG3TvDidOwOrVMHWq8b8nThjHzfRLWSmC8nOs0rVrkJpq+iw5\n8fTM+3O0Z08oW9Y8eUogD0cP3B3cc63pVLVTnjUikrstW7aQnp6ea83ixYvNlEZEpPg4ffr0XV1/\n5syZQkpScBUqVADg8uXL2c7fHK9YsaLJsyQlJdGjRw8OHjxIx44dWbRoEdba/k9Eihl91xIRERER\nERGR/HNwgKAg6NEDxowx/jcoyDgupVeNGnnXNGtm2c8TX1945ZWc5+3tYfhwfS7fBV8XX16979Uc\n562trBnbaizO9s5mTCVS8tx+3FJOrly5YoYkIiLFS2Ji4l1dn9NuLubQsGFDAA4ePJjt/M3xBg0a\nmDRHWloajz32GNu2beO+++5jxYoV2Nvbm/SeIiKmoEYZERERERERESkRoqOjCQkJISQkhOTkZEvH\nKV3Kl4dKlXKet7KCp58GS77S9GYjzDPPZJ1zdYVNm4xNX3LH7GzseK7JcwxsODDLnL2NPSsfW0kl\n90rmDyZSwtxzzz151tx7771mSCIiUrw4OTnd1fXOzpZr9m3VqhXu7u6cPXuWQ4cOZZlftmwZAA8/\n/LDJMhgMBgYPHsyaNWto1KgR69evx8XFxWT3ExExJTXKiIiIiIiIiEixFh0dzc6dO+nXrx81atSg\nYcOGvP7665w/f560tDRLxysd/P1h5Upjw0l2vv7auKOLpfn4wCefQHAwfPghvPQSLFliPD7svvu0\nm0wh8HXxZUrHKRwffpzXW73Ok/WfZG63uZweeZoOVTpoNxmRQtCwYUPc3XM+wszBwYEOHTqYMZGI\nSPFQvXr1u7q+WrVqhZSk4Ozt7RkxYgQAL774IvHx8bfmpk6dypEjR2jbti1NmjS5NT5r1ixq1arF\nG2+8USgZRo8ezaJFi6hVqxabNm3Cw8OjUNYVEbEEW0sHEBERERERERG5UzExMXz55ZeMGzfu1lh8\nfDwzZsxgwYIF7Ny50+TbjwvGHWPq1YMjR2DOHPjpJ0hOhrZt4a23oFq1nJtozM3T0/j25puWTlJi\neTl74eXsxccPfWzpKFKCRSZEEp9qfJLQ1c4VT2dPCycyH39/f1avXk3Hjh1JSUnJNGdjY8PSpUvx\n8fGxUDoRkaKrT58+7N69+46vf+SRRwoxTcG9/fbbbN68mV27dlG9enXatGnDhQsX2Lt3Lz4+PixY\nsCBTfUREBMHBwYSGhmZZa/78+cyfPx+A1NRUAEJDQ2nRosWtmjlz5tC4cWMAVq9ezeeffw5A+fLl\nee2117LNOG7cOGrVqnX3D1ZExMTUKCMiIiIiIiIixdbVq1czNcncLi4ujkcffZTtGzfiFxQENjZm\nTlfK2NpC5crGnVpefhkMBmNzTJkylk4mIiVIbFIsh68d5vXNr7Pn8h4A7it/H5889An1fetTxrHk\nf8+xtbWlRYsWHD16lClTprBy5UoyMjLo2rUrr7/+OpUqVcLR0dHSMUVEipw2bdrg6+tLWFhYga/1\n8/OjdevWJkiVf46Ojvz+++9MmjSJH3/8kVWrVuHp6cmgQYOYOHEiQQU4RvTy5cvs3bs301hKSkqm\nsevXr9/6c3R09K0///bbbzmuO2jQIDXKiEixYGUwGAyWDiEiJVPdunUBOHbsmIWTiIiIiIhISZSa\nmsorr7zCzJkzc607snEj9V1doUkTHa0jUkwkJUFUFGRkgL194Z/cdf36dRITE3FycqKMhZq5YpNi\nSU1PxdXBFUfb4t3UkJyWTHxqPHbWdrg5uOVYl5SWRFRiFAaDASdbpwLvAhOfEs9PR39i6Nqh2c5/\n2+tb+tbpi7Nd6TniKzEx8daTl+7u7ri4uFg4kYjI3cnIyCA4OBiAmjVrYm1tXajrb9q0iTfvYGfB\nSZMm6Vg7yVVBPnf1/JmI5RXu3y4iIiIiIiIiImaSmJjI0aNH86y7cOECPPkknD9v+lAiclfS0+Hc\nOeOmRNWrQ/ny8OCDsHw5RETc/fphYWFs27aNxx57jAceeIBHH32U33///Y5eWX6nQuNCWR28mt5L\netPph06M2jCK4+HHuZ50Pe+Li5jrSdc5EX6Clze+TJcfutBvaT82ntnI1RtXM9WlZaRxLvocY38b\nS7059aj6eVV6L+nNtvPbiE6MzmH1rCITIxm+fniO88+tfY7IhMg7fjzFkZOTE4GBgQQGBqpJRkQk\nHzp27Mjo0aMLdM2YMWPUJCMiUsLo6CURERERERERKZbs7e3x9/fPs87H29v4DPuHH8IXX4CeSBQp\nsk6dgpYtITb2v7GjR6FvXxgyBCZPBm/vO1s7LCyM4cOHs3z58ltjJ06cYOPGjfTp04cvvvgC38Le\nuuZ/hMSF0PWHrhy+dvjW2MHQg3x58EtmdZ3Fk/WfxN3R3aQZCktsUizfH/mekRtGZhrfeHYjzcs1\nZ+VjKwlwCwDgRPgJWi1oRVxK3K26Py7+wQPfPsCEByYw8t6ReDh65HnP387+RmpGao7zyenJbLuw\njacaPHVnD0pEREqFJ598El9fX2bMmMG1a9dyrPPz82PUqFF07NjRjOlERMQctKOMiIiIiIiIiBRL\njo6OjBo1KtcaX19fygPExcHSpRATY45oInIHIiJg4MDMTTK3W7AATp++s7UNBgNr1qzJ1CRzuxUr\nVrBq1SpMeUr9jeQbjP1tbKYmmduN+GUEF2IvmOz+uTEYDISGhnL+/HkuXbrE9et5725zLuZcliaZ\nm/Ze2ct7294jMTWRazeu8eiyRzM1ydzund/fISQuJF85T0aczLPmdOQdfpKIiEip0rFjR1avXs1n\nn31Gy5Yt8fHxwcXFBR8fH1q2bMlnn33G6tWr1SQjIlJCqVFGRERERERERIqtKlWq0KtXr2znrKys\n+Orjj/GbOdM4kJrzLgQiYnkxMfDXX7nXTJ4MN24UfO2rV6/y4Ycf5lrz0UcfcfXq1Vxr7kZkYiRL\nji3JteazXZ+RkJJgsgzZCQ8PZ+HChdx3331UrlyZSpUq8fTTT3PixAlSUlKyveZGyg0+3vlxrut+\ne/hbwhPCCU8Iz7PBZfa+2aSkZ3+v29X0rplnTXWv6nnWiIiIANja2tKuXTtmzpzJhg0b2L59Oxs2\nbGDmzJm0a9cOW1sdzCEiUlLpO7yIiIiIiIiIFFs+Pj7MmzePpk2aMG36dCIjIwFo1KgR0996i8ab\nN2Pz++/G4vvvBycnC6YVg8HA1RtXORdzjiPXjuDt7M295e7Fy8kLF3sdiVXa5WfDp7NnISEBXF0L\ntnZKSgrnz5/PtebChQs5NoYUhtjkWNIy0nKt2XtlL3EpcTjbO5skQ0RCBJEJkZyOOo2LnQtVHKrw\n9utvs2jRols1GRkZrF69ml9//ZUdO3bQrFmzLOvcSLnB3it7c71Xcnoy8SnxXL5+Oc9cx8KPkZia\niL2Nfa51Hat2xNbaNsePo72NPW0rts3zfiIiIiIiUrqpUUZEREREREREijVfX19ef/11BnbvTsLV\nq9hYW1Pm9Gl83nsPjh37r3DiRPD0tFjO0i49I52jYUfp/lP3TE+c29vYM6HdBIY2Hoqns/7/lGZu\nbnnXlCsHjo4FX9smPR07OztSc9lZys7ODhsbm4Ivnk921nZ51rjau2JjbZoMF2IuMGDFAP689CcA\nvi6+fHnPl5maZG6XnJzMU089xfbt2/Hz88s0Z401LnZ5N7dZW1nj4+yTZ12gWyAOtg551nk5eTGn\n6xyeW/dctvPzus/D29k7z3VERERERKR009FLIiIiIiIiIlLs2drZERQURI1ffqFqp074jBjxX5OM\nlRV8/jnUq2fZkKXc5euXuf+b+7PsLpGSnsK4LeNYGbyS9Ix0C6WToqBsWahbN/easWOhTJkCLhwS\nQtlFi3isd+9cyx599FHKli1bwMXzz8PRg4ruFXOtGd50uEkaPULiQmj/XftbTTIA/ar347u53+V6\nXXBw8K2dum7n7eLN0CZDc722atmquDu4E+AWQIBrQK61o1uMxtE27w4oF3sXHq37KNsGbqN5uea3\nxlsGteSPQX/Qu1ZvnO1MsxuPiIiIiIiUHGqUEREREREREZGSwdsb3nsPjh+HV16BPn3g/ffhzBkY\nNAjc3S2dsNRKTU/lm0PfcD35eo41b299m2vx18yYSooaX1/45htwyGFjka5d77Dfbft2XKZO5d3h\nw3HN4cwmFxcX3n33XVxcTHcEmL+rP7O6zspxvpJHJTpX62ySe++6tIt/o//NNOZt783li3kfi5Rd\no4y1lTWP1H6EQLfAHK+b2WUm/m7++Lv6s7DnQqytMv8q2gorHqrUgW87raWcbT3S89kn5+7oTttK\nbVn3xDrOjzrPhdEXWNt/LW0qtsHdUd/nRUREREQkb2qUEREREREREZGSw9MTateGzz6DxYvhnXeg\nSpX8nekiJhOVGMXiY4tzrbl64yoxSTHmCWQicXFw4QIcOQInT8LVq5ZOVPw0aAAHDkC3bsbNoAD8\n/Ixf0gsXGptpCiQ6GmbPhthYKo0fz+4VK2jatGmmkiZNmrB7xw4qV65cOA8iB1ZWVrSp0IaVj63M\n0mDSoUoHtg3cRrky5Qr9vnHJcXx54Mss49eSr1Gxcu473AB4e2e/w01QmSB2DN5B6wqtM40HuAaw\n8rGVtKrQCjA21bSu0Jqdg3dyb7l7AWge2JLtff7loYg1fDGmO317OTNtGly8CGlp+Xtc3s7eVPSo\nSAX3Cng5e+XvIhEREREREcDW0gFExPLCw8OZPHkya9eu5eLFizg5OVGpUiUefPBBPv30U0vHExER\nERERuTN2dpZOUGSkZ6QTkRCBAQNu9m642Jtu14zsGDCQlpH3s98ZhgwzpDGNy5fh1Vdh2TJu7YxR\nrRp88QU0b65erfyytzcev/TDDxATY2yacHQEf3+wsbmDBdPS4LpxJyPbHTuo9/zz/DJyJLGffEJc\nQgKuTk54HDmCj50d2Jr+V6Xuju70qNmDe8vdS2RCJDdSbuDn6kdZx7KkZ6QTEheCrbUtvi4F7QjK\nWbohnaS0pCzjS08tZcGwBSxdsjTHa+vWrYunp2eO81XKVmHVY6uIToomNC4UNwc3fJ198XP1w8b6\nv/9hLvYutCzfkvVPrCcxNYnwc/60b2NLbOx/a+3ZAxMmwPbt0LAhWOslniIiIiIiYiJqlBEp5Q4c\nOECnTp2IjIykbt269OzZk+vXr3P8+HGmTZumRhkREREREZFizGAwcCXuCj/98xMLDy0kMS2R+yvc\nz2utXqOie0XcHMzTveHh6EGnqp04E3Umx5oyDmXwcPQwS57CFhoKHToYd5G53Zkz0LEjbNxonJf8\nc3cvpNPS3NygRQv45x/j+//+i8+YMfiAsRMjI8P437NnC+Fm+WNtZU2gW+CtXWWu3bjGlnNbmLRz\nEueizxHgFsArLV+ha7Wu+Lv53/X93Ozd6FClAzsu7sg0HpkYyZmMMzw5+EkWLVyU5TpnZ2e+//57\n/Pz8cl3fy9kLL2cvqnlWyzOLt7M3ISHQvSuZmmRuioszfs0cOgTlCn9zHREREREREUBHL4mUauHh\n4XTu3JnExERWr17N0aNHWbx4Mb/88gvnz59n165dlo4oIiIiIiIid+FM1BmafNmEsZvHciLiBOdj\nzvPdke9o8EUDVp5cyY3kG2bJ4WjryKjmo7C1zvk1W2NajCnUXTTMadu2rE0yNxkMMHy4jmGyGEdH\nGDUq+7mM/9/BqHdv47FtFhAWH8agVYPot7QfB0MPEp0UzfHw4zyz5hm6/9Sd0LjQu76HjbUNTzZ4\nEnsb+yxzY/8cywPPPMCX335JrVq1ALCzs+PJJ5/k0KFD1KtX767v/7+Cg43NZTmJiDAeXyYiIiIi\nImIqapQRKcXeffddIiIi+PTTT+nRo0eW+XvvvdcCqUREREREpCjLyMjgypUrbN68mQ8++IA5c+bw\n77//Epvd1gBiUeHx4Ty+/HHC4sOyzBkwMGjVIK7FXzNbnvLu5VnXfx0ONg5Z5gbUH8DwZsOzfSK/\nqIuKgpkzc685c8Z4jJBYSPny8OWX2c/Vrg3Tp0OZMmaNdNMvp3/h17O/Zjt3IPQAcw/MJTU99a7v\nE+gWyPon1mf5Gks3pDPr6Cx69unJtm3buHDhAufOnWPu3LlUr14dOxMcYZef12Xt2JF3jYiIiIiI\nyJ3S0UsipVRiYiKLFi3CxcWFwYMHWzqOiIiIiIgUA2lpaRw5coQuXboQFvZf84WVlRUjRoxg/Pjx\n+Pj4WDCh3C4yIZKDoQdznDdgYOGhhbzf7n1srG1MnsfR1pF2ldpxauQpNp7ZyJ+X/sTPxY+BjQbi\n5+KHl7OXyTOYQlpa9kfI/K+UFNNnkRyUKQOPPQatW8Pnn8PevcYjmUaOhFatICDAIrGu3rjKRzs+\nyrVm5t6ZPNf4OcqVubtziBxsHWhToQ2nRpxi2fFlbD63GVc7V4Y3G05tn9r4uvqC613dIt88PPKu\nKVvW5DFERERERKQUU6OMSCm1f/9+4uLiaN26NU5OTmzYsIHffvuNpKQkatSowaOPPkpgYKClY4qI\niIiISBESEhJCu3btiIuLyzRuMBiYOXMm/v7+vPrqq9jbF79dQUqi87Hn86z5K+QvElMTcXUwzzPk\nDrYOVHCvwNAmQxnaZKhZ7mkqBoOB8IRwMuwcaNq0DMePW+VYa2NjsQ1L5KYyZYxv06bBjRtga5u/\njg0TSs9I53TU6VxropOiSU5PLpT7Odg6UNGjImNajuH5ps9ja22Lg23WHZ5MrXPnvGt69jR9DhER\nkeIoMTGRSZMmsXjxYi5evIinpyedO3dm4sSJlCuX/8ba7du3s23bNvbt28e+ffuIiIigYsWKnD9/\nPs9rb9y4wZQpU1i+fDn//vsvNjY2lC9fnrZt2zJ58mRcXc3UfSsichfUKCNSSh0/fhwAX19fevXq\nxerVqzPNv/nmm3z99df079/fEvFERERERKSISU9P58cff8zSJHO7Tz75hKeffpqgoCAzJpOclHXM\ne0sGLycv7GwK/2gVs4qJgehoiI8HZ2dj84Onp0lvGRIXwrpT65i5byYZhgxmD/+D777LeUecRx4B\nr2KwYU5ICBw+DMuXg50dDBgA1aqBv7+lkxUiR0fjWxFgZWWFt7M3EQkROdbYWdthZ124X6PWVta4\n2LsU6poF4eUFw4bBvHnZzz/9NGhzMhERMZfLly+zZs0aLl26REJCAs7OzpQvX54ePXoUuZ9rkpKS\naN++PXv27CEgIICePXty/vx5Fi5cyLp169izZw9VqlTJ11qjRo3i8OHDBc5w7tw5HnzwQc6dO0eV\nKlXo0qULycnJBAcHM2fOHN544w01yohIsaBGGZFSKjo6GoA1a9ZgY2PD7Nmz6devHwkJCcyaNYvP\nPvuMgQMHUrt2bRo1apTrWnXr1s12/OzZs1StWrWwo4uIiIiIiAVER0ezdOnSXGtiY2OJjo4ucr9Q\nLq2CygTh5eRFZGJkjjUvNX/JIjtKFAqDAc6ehRdfhN9+M74PcP/9MHcuVK9u3DWkkF2KvUT779pz\nJurMrbEN4fN496MXef9N9yz1NWvClCnGk36KstOn4YEH4MqV/8bmzoV69eCXX6B8ectlK6l8nH14\noekLTPxjYo41j9d7HE8n0zZ+mZuHB0ycCN7exg1+EhKM405OMGIEvPqqjl4SERHT27t3Lz/88AO7\nd+/GcPPfkbdZuHAhLVu2ZMCAATRv3twCCbP64IMP2LNnDy1btmTTpk23GlKmTp3KK6+8wpAhQ9i2\nbVu+1urYsSP9+vWjWbNmBAUF5fg8z+2Sk5Pp0qULFy9eZO7cuQwbNizT/NGjR/E0ccO6iEhhsbZ0\nABGxjIyMDADS0tKYMGECw4cPx8fHh4oVK/Lpp5/Sr18/UlNT+fTTTy2cVEREREREipPsfsksluHn\n4secbnNynO9YpSNVyubvFadF0qVL0LIlbNr0X5MMwB9/QPPmkI9t4wsqLjmO1357LVOTDMAnB94i\nsf5sNuy8wpNPZVC7tjHajz/C779DUe8dCwmB9u0zN8ncdPQoPPYYhIebP1dJZ2djx3NNnqN8mey7\nkMo6luW9du9ZdPcXU/HxgfHj4eRJ2L8f/voLgoNhwgTw9bV0OhERKckMBgNff/01L774Irt27crx\n5xeDwcCuXbt48cUXWbBggcV/zklJSWHWrFkAzJ49O9OuLS+//DINGjRg+/btHDhwIF/rffLJJ7z1\n1lt07Ngx380tM2bMIDg4mJdffjlLkwxAvXr1cHZ2ztdaIiKWpkYZkVLq9n9EDR48OMv8zbHt27fn\nudaxY8eyfdNuMiIiIiIiJUfZsmV55JFHcq0pU6ZM8XkFYUQEXLhgbKYooR0Atja2dKraibX911K1\n7H8/n7nau/Lafa/xXe/v8HUpps9IJycbt6KIyOHImrg4eO8943FMhSgqMYplx5dlO/fJgbd4fEdd\n7nvhO37bksratdC/PwQEFGoEkzh2DC5fznl+924ICzNfntIkqEwQO4fs5OkGT986YsnayppetXqx\nb+g+KnlUsmxAE3JwMO5U1KQJNG1q/HMRORVLRERKsIULF/LFF18U6Jo5c+awcOFCEyXKnz///JPY\n2FiqVq3KPffck2W+b9++AKxdu9ZkGb766isARo4cabJ7iIiYi45eEimlKlasCICzszM+2Rz8XKlS\nJQDC9JswEREREREBbGxsGDBgAJMmTeLGjRvZ1rz66qv4FvWtAK5fh0OHjGd7/PWXcaxRI5g8Ge69\n13gmSBEWHx9PZGQkwcHBpKWlUbNmTTw9PfHIIbe7ozvda3SnaWBTYpNiSctIo4xDGXxdfIvvkUsA\nkZHw9de51/z8M3z8MbgU3m4ccSlxpBvSc5yPTY5l2v6P6F2/M16u/oV2X1NbtSrvmt27IR878ssd\nqOBegTnd5jCx/UQSUxNxtHWkrFNZyjiUsXQ000hNhWvXYO9e4ydWQAD07Gk8i6mIfw8WEZHibe/e\nvcyZk/OOi7mZM2cOdevWtdgxTIcPHwagcePG2c7fHD9y5IhJ7n/p0iXOnDlDUFAQ5cuX588//2TN\nmjXExsZSuXJlHnnkEapVq2aSe4uImIIaZURKqZsdx4mJiSQnJ+PgkPkXpFFRUUDmnWdERERERKR0\nK1euHNu2baNz585E/M9OHi+88ALPP/889vb2FkqXD4mJsHo1PP105vFDh6BTJ/j8cxg8GIroz0Fh\nYWFMnjyZmTNnkpqaCoCVlRW9evVi1qxZBAYG5nitv6s//sWocSNPGRnGXWNyk5oKaWmFelsHm7yb\ni7ycvW7tDFJc5OfL1q54PaRix8XepUQesZRFcjLs2wfdumX+Gn71VRg1Ct5+29gwIyIiYgI//PDD\nXV3/448/WqxR5uLFiwAE5XCm583xCxcumOT+x48fByAwMJAXX3wxS8PR22+/zccff8wrr7xikvuL\niBQ2Hb0kUkpVqFCBhg0bYjAYsj1e6eZYdlv4iYiIiIhI6WRra0ujRo04dOgQGzZsYPz48UyfPp0z\nZ87w0UcfZbtbZZESEQHDhuU8//LLxp1KiqDr16/zwQcfMHXq1FtNMgAGg4GVK1fy+OOPl64dQe3s\noFat3GsCA/PXAVIAHo4eNPJvlGvNmBZj8HL2KtT7mlr//nnXtG5t+hxSCly+DB06ZN/oNmMGfP89\npOe8a5OIiMidunz5Mrt3776rNXbt2sXl3M6rNKGbu3o6OztnO+/y/7soxuXVTH6HoqOjATh48CBz\n587lvffe49KlS4SGhjJ58mTAuMPo+vXrTXJ/EZHCpkYZkVJs7NixgPEfL6GhobfGDx06xJQpUwB4\n/vnnLZJNRERERESKJhsbG8qVK0fnzp2ZMGECo0aNomrVqjke/VOk7N1r3FUmJ2lpsHGj+fIUQFRU\nVK7bxO/YsePWq0xLBT8/eOed3GvGjgX/wt1Fx8fFh3nd52Frnf0mzQ39GtKmQptCvac5VKoETZrk\nPN+/P3h6mi2OlFQpKfDFF8ZdZXLy0Udw9ar5MomISKmxZs0aDAbDXa1hMBhYu3ZtISUqXjIyMgBI\nS0tj2LBhvPvuuwQFBeHv78/YsWMZM2YMAB999JElY4qI5JsaZURKsSeeeIKBAwfyzz//UKdOHbp1\n60b79u1p0aIFUVFRDB06lH79+lk6poiIiIiISOHIzzbkZ8+aPscd+Ouvv0jPY5eFRYsWmSlNEfHQ\nQ/D449nPdepk7O6wLvxffdX3rc+uIbtoGtj01pijrSPPNX6OXwb8QoBbQKHf09R8fY2nkrVvn3nc\nygoGDICpU6FsWctkkxIkOhrWrcu9JiICYmPNk0dEREqVS5cuFco6lmpOd/3/42ETEhKynY+PjwfA\nzc3NpPcHGDx4cJb5m2N79+4lKSnJJBlERApT9i9/EZFSY+HChbRq1Yp58+axbds2rKysaNy4McOG\nDWPgwIGWjiciIiIiIsWdwWDcHeDcOfj7b/D2hpYtjdtT3PbLVrOoWTPvmrp1TZ/jDuTnl803t2Mv\nNXx8jEe1PPccfPwx/PsvBAXB66/DPfcYuz9MwMnOiWblmrHhiQ3EJseSkp6Ci70L3s7eONtlvxV+\ncVCuHCxZYuxT+OsvsLWF5s2NX6rFYcMoKSasrAqnRkREpIByajCx1DoFVaFCBYAcj366OV6xYkWT\n3P/2dStVqpRl/uZYeno6UVFRBAYGmiSHiEhhUaOMSClnZWXF0KFDGTp0qKWjiIiIiIhISZORAceO\nQbducPsrOO3s4N134fnnwcvLfHkaNQI3N4iLy37ewQHatTNfngK455578qzp3r27GZIUMb6+xrfG\njY3Hajk6mq2rw9vFG28Xb7Pcy1y8vY1vtWpZOomUSJ6e0K8fTJyYc01AALi7my+TiIiUGs7OhdPQ\nXFjrFFTDhg0BOHjwYLbzN8cbNGhgkvvXqlULR0dHkpKSiI6OxsfHJ9N8VFTUrT+7mvsFESIid0BH\nL4mIiIiIiIiIaVy+DG3aZG6SAUhNhbffhuXLIY/jhAqVr69xywwbm6xzVlbw3XfGXUqKID8/P5o1\na5bjvLe3d67zJZ67O/j7a+sTkaLMzg6GDMl9N7GJE41fyyIiIoWsfPnyhbLOzZ1dzK1Vq1a4u7tz\n9uxZDh06lGV+2bJlADz88MMmub+DgwOdOnUCYNu2bVnmt2/fDkCVKlUoU6aMSTKIiBQmNcqIiIiI\niIiIyF2LiIjgzJkz7Nmzh+PHj3M1NBS2boXY2JwveucduHbNfCHt7aFtW+MRUH36GN+3tTXueLN/\nP3TtCk5O5stTAD4+Pixbtozq1atnmfP09GTr1q3a3lxEir6gINi+PWszjK0tfPgh9OoF1vqVtYiI\nFL4ePXpgdZfH+1lZWZmsESUv9vb2jBgxAoAXX3yR+Pj4W3NTp07lyJEjtG3bliZNmtwanzVrFrVq\n1eKNN94olAxjx44FYOLEiZw6derW+Llz5xg/fjwAzz//fKHcS0TE1HT0koiIiIiIiIjcMYPBwOnT\npxk4cCB79uy5NV6tWjUWTp1Kk0cewWn58uwvvnYNYmLAnA0ezs5Qvz588w1cv24cc3UtFkd9VKhQ\nge3bt3PkyBG+/vpr0tLSeOSRR2jbti2BgYF3/Yt/ERGTs7U1HoN34ACcOgWHDxt38mrd2ng0k45q\nEBEREwkKCqJly5bs2rXrjte47777CAoKKsRUBfP222+zefNmdu3aRfXq1WnTpg0XLlxg7969+Pj4\nsGDBgkz1ERERBAcHExoammWt+fPnM3/+fABSU1MBCA0NpUWLFrdq5syZQ+PGjW+9f9999/HOO+8w\nYcIE7rnnHlq1aoWNjQ1//vkncXFxdOnShZdfftkUD11EpNCpUUZERERERERE7tjly5dp1aoVERER\nmcbPnDnDA336sG/tWu756y+4eDH7BQwGM6TMhpub8a2YCQgIICAggPbt25ORkYGDg4OlIxV7BgOE\nhRlPAXNwAC8vSycSKeGsrY0NkoGB0K6dpdOIiEgpMmDAgLtqlBkwYEAhpik4R0dHfv/9dyZNmsSP\nP/7IqlWr8PT0ZNCgQUycOLFATTyXL19m7969mcZSUlIyjV2/+cKC27z//vs0bNiQ6dOns2fPHtLS\n0qhZsyYDBw5kxIgR2GR3zK2ISBFkZTBY6jdSIlLS1a1bF4Bjx45ZOImIiIiIiJhCamoqEydOZOLE\niTnWtGvbluUdO+L51ltZJ8uWhX/+gXLlTJhSJGchIbBuHUybBuHhUL06jB8PzZoZN7kQEREREfPK\nyMggODgYgJo1a2JdyEfyLViwgDlz5hT4uuHDhzNkyJBCzSIlS0E+d/X8mYjl6cBXEREREREREbkj\nERERLFy4MNeabdu3c71p0+wnx44FX18TJBPJW0gIdOwIw4bByZMQGQl79kC3bjB6tLFxRkRERERK\nlsGDBzN8+PACXTN8+HAGDx5sokQiImIJapQRERERERERkTtiMBiIj4/Psy4tu81shw2DZ54BOzsT\nJBPJXXIyfPop5PQCzh9/hL/+Mm8mERERETE9KysrhgwZwuzZs2nVqhVWVlY51rVq1Yo5c+YwZMiQ\nHOtERKR4srV0ABEREREREREpnpydnbn33nvZuHFjjjX+/v4416gBy5fDn38az7Pp2xe8vIxHL4lY\nQHg4fPVV7jUffADNmxs/VUVERESkZGnevDnNmzfn8uXLrF27losXL5KQkICzszMVKlTg4YcfJigo\nyNIxRUTERNQoIyIiIiIiIiJ3xMPDg3feeSfXRpmxY8fiV6ECVK4MffqYMZ1IzhITIa/NkE6dMu48\nY1JRUXDtGnz3HYSGQqtW0KkT+PmBg4OJby4iIiIiQUFBvPDCC5aOISIiZqajl0RERERERETkjtWp\nU4ePP/4427m+ffsyYMAAbGxszJxKJHcODpDXp6Wvb941dyU8HMaOhTp14OOP4dtv4bnnoEYN+P13\nYzePiJnFJMVwPuY8wRHBXIq9RFJqkqUjiYiIiIiIFDo1yoiIiIiIiIjIHfPw8GDYsGGcOHGCkSNH\n0rZtW5544gn27t3LnDlz8PX1tXREkSzKloWePXOvGTPG2CxjEmlp8M038PXXWeeSk+Hhh+HyZRPd\nXCSrtIw0giOCGbB8AFVmVKHW7FrUnFWTVza9wpXrVywdT0REREREpFBZGQwGg6VDiEjJVLduXQCO\nHTtm4SQiIiIiImIOqampJCQk4ODggKPj/7F33+FRlVsbh3+TSe+9kQDSuwoiYMMCFsAKB7uigr0X\nVD4bx4YgiCCIXVTsHcRCU1BRUFCkCNJLeiM9kzLfH++hxGQmIWUm5bnPlUvY7zt7r0zaIfuZtXzd\nXY6IU1u3Qv/+kJNTda1nT1j0TTlxZXtMZxd/fwgPh6Cghrn4vn1w9NGQmel4z223wZQpB0cw2e12\nUlJSyMnJoaysjJCQECIjI/H392+YmqRV25K5heNePo48W16VtR5RPVh85WLiguLcUJmIiLQ2FRUV\nbN68GYCuXbvi4aHX/EvzcCSfu7p/JuJ++ukiIiIiIiIiIg3Cy8uLkJAQhWSkWTjqKFi9GkaOPDRi\nKTAQ7rrLzncLbMQ9OAY6djSjkTp2hDFjYMeOhrl4Xp7zkAzAt98eTPHs37+fBQsWMGjQIHr06EGf\nPn3o3Lkzt99+O8nJyQ1Tk7Ra+4v3c99391UbkgHYmL6Rr/75ysVViYhIa2WxWA7+uaKiwo2ViByZ\n8vLyg38+/PNYRJomBWVERERERERERKTVsVqhUyd4/XXYtg22bIGNG+HpezOJP/EoeOcdOHBzprwc\nPv0UTjwR9uyp/8Vr88pob2+wWKioqGDZsmWcd9557Nq16+CyzWbjtdde46KLLiI1NbX+NUmrlVOc\nw4J/FjjdM/2X6aQVpLmoIhERac0sFgve3t4AFBQUuLkakdrLzc0FwMfHR0EZkWbA090FiIiIiIiI\niIiIuEtwsHkDzJilux6CpKTqNycnw4svwmOPmSBLfS7aqZOZ/+TIVVdBRAQpKSnceuutDrf98ssv\nbNq0iZiYmLrXI62ardxGhd35K/YzCjMoqyhzUUUiItLaBQUFkZmZeTAMHBAQoBFM0iTZ7XZKSkrI\ny8sjKysLgLCwMDdXJSK1oaCMiIiIiIiIiIgIQFYWzJ3rfM9LL8Gtt0J8fN2vExsLU6fC+edXvx4R\nAZdcAlYrWVlZ7Nu3z+npZs6cycCBAzX2TOrEz8uPIO8gh6OXAHpE9cDP08+FVYmISGsWERFBQUEB\nxcXFJDkKMIs0QaGhoYSEhLi7DBGpBcUvRUREREREREREwIxYKi52vic7G+z2+l/rlFNMKOdgO5v/\n6dYNVqyAhAQAioqKajzV/v37KS0trX9N0ipF+0dzQ78bnO556JSHCPPTq6NFRMQ1rFYrbdu2JSIi\n4uAYJpGmymq1EhwcTJs2bYiNjVX3I5FmQh1lREREREREREREALy8oG1b2L3b8Z4ePcy++goNNV1j\nTj8d/vnHdLPp1Ml0mzlsjFJUVBQeHh5UVDgejXPSSSfh7+9f/5qkVfL29ObuQXezdMdS1qSsqbJ+\n43E30iemjxsqExGR1sxqtRIdHU10dDR2ux17QwSVRRqYxWLBYrG4uwwRqQMFZURERERERERERMCE\nVB58EG66yfGeRx6B6OiGuZ63t+kc87/uMdUJCwvj3HPP5Ysvvqh23Wq1ctVVV2G1WhumJmmV4oLi\nWHDZApbtXMa0ldNIK0ijW2Q3Jpw8gV7RvYj0j3R3iSIi0oopjCAiIg1NQRkREREREREREREAiwUu\nuggWLoT586uuX3opnHaaS0sKCQnhhRdeYMuWLWzatKnSmtVq5dNPPyUuLs6lNUnLFBcUx2W9L+PM\nDmdSWlGKn5cfob6h7i5LRERERESkwSkoIyIiIiIiIiIickB0NLz6Kvz5J0yaBHv3Qvv2ptNMz54Q\nFeXykhISEliyZAm//vors2bNIj8/n1NPPZXrrruO+Ph4/Pz8XF6TtFyRAeoeIyIiIiIiLZvFrqF+\nItJIevbsCcCGDRvcXImIiIiIiIhIHeTkQHEx+PlBSIi7qwEgNzeXsrIygoKC8PLycnc5IiIiIiJy\nhHT/TMT91FFGRERERERERESkOqGh7q6giuDgYHeXICIiIiIiItKsebi7ABERERERERERERERERER\nERERV1BHGRERERERERERERdLTzdvW7dCWBh06ADR0aBpSiIiIiIiIiKNS0EZERERERERERERF9q6\nFUaPhrVrDx0LCYHnn4fzz2+SE5+kmSouNuErq9XdlYiIiIiIiDQdGr0kIiIiIiIiIiLiIvv2wSmn\nVA7JAOzfD2PGwPffu6MqaUmKimDnTpg8GS6+GK6/Hn77zXQwEhEREREREXWUERERERERERERcQm7\nHb76CpKTHe+55x4YOBBiY11Xl7QchYWwZAlcdBGUlR06/vrrMGIEvPoqxMS4rz4REREREZGmQB1l\nRERERERERESkSSsshN274c034aGHYN482LPHdM5oTrKzzfvgzPbtpruMSF0kJcGFF1YOyRywYAHM\nnAk2m+vrEhERERERaUoUlBERERERERERaSAVFRXYynQXuiHt3w8ffACdOsF118H69RAWZoIz//xj\nQjTNRUVF7UIKFRWNX4u0PDYbzJkD5eWO98ycCWlprqtJRERERESkKdLoJRERERERERGRekrLT2Nb\n9jbm/DaHPFseZ3c6m3M6nUNsYCxeVi93l9esrV8P115rgjKffgrFxWZ8UX4+DBpk9iQkQHi4e+us\njZAQOOcc+P13x3vCwyEoyHU1ScuRmwtLl9a8Jy/PNfWIiIiIiIg0VQrKiIiIiIiIiIjUQ3JeMhd/\nfDErdq84eOyzvz8jwCuARVcu4rj44xSWqaPMTHjgAQgOhoUL4f/+Dz76qPKetm1Nx5kuXZp+WMbL\ny4R+pkyBkpLq99x7L8TEuLYuaRmsVvD3r3mfp34jLCIiIiIirZxGL4mIiIiIiIiI1FG+LZ8JSydU\nCskcUFBawJC3h5Ccn+yGylqGoiL48UeYPh2ef75qSAbMCKYLLoD0dFdXVzdt2sB330FAQNW1MWNg\n7FgTqBE5UmFhcOONzvf06GE6G4mIiIiIiLRmev2AiIiIiIiIiEgdZRZmMm/dPIfrhaWFzN88n5v7\n34zFYnFhZS2HxWJGLN10k+M9qamwYgXExZnuM02ZtzcMHAibNsEPP5i32Fi4/HKIjm76XXGkaTv9\ndOjQAbZvr7pmscDMmebzTEREREREpDVTRxkRERERERERkTrKLMqktKLU6Z6vt35NcVmxiypqZoqK\nYM8e+OknkxjZtQvy8g4uBwbCueeabjGORhUd8PXXYLM1cr0NxNsbEhPhiivglVfg8cehWzeFZKT+\n4uNh6VI488zKxxMSYMEC6N/fPXWJiIiIiIg0JeooIyIiIiIiIiJSR95W7xr3+Hv5Y7VYXVBNM5Oe\nbtpbPPusCcwAWK1w5ZXw9NMQG0toKEycCIWFNZ/OatXIIhGAdu3g/fchOxvS0kzgLDzcdC7y0Msm\nRUREREREFJQREREREREREamrcN9wYgJiSC1IdbjnpuNuwtuz5kBNq5KfD9OmwaRJlY+Xl8Obb0JG\nBrzxBkRG0qULJCWZm/35+Y5PefnlEBLSqFWLNBthYeatQwd3VyKtWVpaGnv37mXRokV4enpy9tln\nExMTQ2RkpLtLExEREZFWTq8hEBERERERERGpo5jAGKadNc3h+rGxx9I9qrsLK2omsrJg6lTH6wsW\nmFYYgL8/tGkD48c73t6hAxx7bAPXKCIidbZnzx7OPvts+vXrxwMPPMC9995Lr169uPTSS0lOTnZ3\neSIiIiLSyikoIyIiIiIiIiJSR1YPK+d0Ooe3L3ybKP+og8c9LB6M7D6SBZctIDYw1o0VNlHr1kFp\nqfM9n30GQIGtgMzSvYwZW8T48XY8/9UfuW9f+O47aNu2kWoVEZEjkpqaynnnncfatWurrC1evJgb\nbriBrKwsN1QmIiIiImJo9JKIiIiIiIiISD2E+YVxaa9LOa39aWQUZlBUVkRsYCzhfuEE+wS7u7ym\nqaSkxi3l4aHszNrGxB8m8v769ymrKOPRMybz1/W3sG6NL0WFFo4+GuLjITraBTWLiEit7N27lz/+\n+MPh+vz588nKyiI8PNx1RYmIiIiIHEZBGRERERERERGRerJ6WGkT3IY2wW3cXUrz0KuX8/W4OLae\ndzLHv9yX3JLcg4cfW3kfE1eO55b+tzBx+ETC/XWTVUSkqfn2229r3PP777/TqVMnF1QjIiIiIlKV\nRi+JiIiIiIiIiIhrRUbCqac6XM5++D5uXXpPpZDMAXbsvLD6BbZmbSUnB7Ztg/XrYetWSEtrvJJF\nACgvB5sN7HZ3VyLSZHl41HzboTZ7REREREQai/7fqIiIiIiIiIiIuFZEBLz9Nhx9dNW1Nm3IGTmc\nxdsXO3z4gDYDCLf15bbb7PToAb17Q9eucM01sGkTlJU1Yu3S+pSVwc6d8NZbcMMN8H//B7/+ao4V\nFrq7OpEmZ9iwYU7XLRYLxx13nIuqERERERGpSqOXRERERERERETE9RIS4NtvTSuYt96C0lIYNQqO\nOYZiy36nD33r9O85+0xPtm07dKyiAhYuhJUrTYahc+dGrl9ah9JSWLUKzj4b8vMPHX/2WRg9Gh55\nBNq3h4AAt5Uo0tTExcUxePBgfvjhh2rXr7jiCsLDNTpPRERERNxHQRkREREREREREXGPmBjzduKJ\nlQ7755Ti6eFJWUXV1jB3H38fX37qXSkkc7jsbHj8cZgxA0JDG6FmaV327YOhQ6GoCPr2hY4doaAA\nvv8ePvwQOnSAG29skKBMTo65jKcnREXV+3QibhMVFcW7777LddddxzfffHPwuMVi4fLLL2fKlCmE\nhIS4sUIRERERae00eklERERERERERJqUCL8IRnYfWe3ayITbeeUV57/S+uADE5gRqZfycnjvPRg0\nCBYtgiuuMIGYLl3g88/hqafg1VchKckkXOooI8PkbkaNgv794cwz4Z13IDm5+v0VFXW+lIjLxMfH\nM2/ePDZv3szbb7/Ne++9x9atW5k5cyYxMTHuLk9EREREWjl1lBERERERERGRJqO0tJS0tDSKiorw\n8vIiNDRUrzpvhQJ9Apk8dDK/7P2FXft3VVrztvrWGIKx2UzGQaRe8vIgMxNuvRUuuMB0kjlg+nS4\n5BKYOdMcz8sDP78jvkRGBjz2GMyadejYvn1w5ZXQrx/Mnw9xceb0GRnw/vvw55+msc3VV5uGTPoW\nKU1VeHg44eHhdOnSxd2liIiIiIhUoo4yIiIiIiIiItIkpKam8tRTT9GnTx86d+7MUUcdxWWXXcam\nTZsoLS11d3niYm1D2vLTtT/x3FnP0Sm8E1H+UQztMJTQAB9697Y7fWxiohlfI1IvHh7wn//AZZdV\nDskc8P77sHGjmZNktdbpEps2VQ7JHO7332H2bNi/31yqY0eYMMF0THrqKejaFaZNg6ysOl1aRERE\nRESk1bLY7Xbnv1kQEamjnj17ArBhwwY3VyIiIiIiIk1damoql19+OUuWLKmy5u/vz6pVqw7+G0Na\nlwp7BWkFaVTYK/D39CfUL5TFi2HoUMePee45uP12k3MQqbOyMnj8cfjvfx3viYyEn34y45iO0P79\npinNN9843hMaCr/+akIxjsyfDyNGHPHlRURERMRNdP9MxP306wIRERERERERcbsNGzZUG5IBKCws\n5LbbbiO7pnk7Qmp+Kjuyd7ArZxeZhZnuLqdBeFg8iA2MJT4onlC/UAB69IC77qp+/4gRpgmIQjJS\nb3l5sGiR8z0ZGVDHjldFRbB5s/M9OTmQmup8z8MPQ3p6nUpwKDXVjIBKSjKjzERERERERFoS/cpA\nROPzxlUAAQAASURBVERERERERNwqPz+fadOmOd2zbNkyBWWcyCrK4vO/P+fUuafSYUYH2j/fnhHv\njWDlnpXkl+S7u7wGFx8P994LK1eajhx9+8Lw4fDdd2ZUTZs27q5QWgQPD/DxabTTe3lBTIzzPd7e\nUFjofM8ff0BxccPUlJ5uRjudfjokJEDPniaIs2dPw5xfRERERESkKdC0ZhERERERERFxK5vNRkZG\nRq32SVV5JXm88vsrPLDkgUrHf9n7Cye+fiJfXPIFwzoPw+phdVOFjSM+3rx16wb5+eDnBxER7q6q\nZbLZIC0NCgpMuCMkpJU81yEhMG4cfP+94z3du8O2bSadFRp6RKePiDCBr1GjHO+5/HJYtsz5eTw9\nG6aDUno63HMPvP32oWM5OTB5sjn288/Qvn39ryMiIiIiIuJu6igjIiIiIiIiIm43c+ZMFi5cyLff\nfsstt9xCUFBQpXU/Pz8CAgLcVF3TllmUyYSlE6pds2Pn2i+vJSU/xcVVuU5oqOl80SqCG26QmgqP\nPmo6i3TrBh07mvFWa9Y0XBeTJu2kk5ynQx5+GCZOhP3763T6E0+Ek0+ufi06Gh55BHx9nZ9j1CiT\n6amvf/6pHJI5XHIyTJhgQmkiIiIiIiLNnYIyIiIiIiIiIuIWdrudbdu2cc011zBw4ECGDRvGueee\nS1ZWFvPnz6ddu3YH944ZM4bIyEg3Vtt0ff3P11TYKxyuZxRmsHv/bhdWJEeqvByyskz3jqYkNRWu\nuAImTYLc3EPHf/kFBg6E9evdV5vL+PnBu+/CKadUPh4dDa+8YtqsrFkDpaV1On1sLHz4ITz1FERF\nmWO+vqaRzapV0K4dXHMNhIc7Lm/iRAgMrNPlD8rPhylTnO/56CPzeSoiIiIiItLcafSSiIiIiIiI\niLjF7t27GTBgAJmZmQeP2Ww23nvvPX799Vdee+01hg4dSp8+fXjooYfw8/NzY7VN197cvTXuSS9M\nd0ElcqRKS2HfPpg3DxYsAG9vuO46OOMMM8nH3bZsgcWLq18rLYUbb4Svvz4U8GiRAgLggw9g2DB4\n7DHTWiU42DwBs2bBkiUQEwP+/nW+RGwsjB8PV18NJSVmvFV4+KFTJibCTz/BVVfB6tWHHtejB7z1\nVsOMQyouhl27nO8pK2slXYRERERERKTFU1BGRERERERERFyuuLiYSZMmVQrJHG779u2sXLmSpUuX\n0rVrV+Li4lxcYfPRN75vjXvah7Zv/EKkdkpKACiz+vDbbyYUU1R0aHn5cmjbFn74oWECEHVVXAwz\nZjjf8/vvZuJQiw7K+PvD9ddDr15gsZiQTFHRwY8jYFIuMTH1uozVCvHx1a95eJixVwsXQna26eoS\nEmLCNNHR9brsQX5+ZqzW2rWO93h71zwGSkREREREpDnQ6CURERERERERcbnMzEzmzp3rdM/cuXPp\n1q2bQjI1GNBmAIHejueudIvsRkxA/W7iSwPYt8/M2LnkErjsMlL+yePssyuHZA7YvRsuuwwc5Mhc\nwmar3fVttsavxe0SE+HNN8FuN/OxDg/JjBpl5lNZrY1eRmQkdO4MAwaY4ExDhWTANM657z7ney69\nFCIiGu6aIiIiIiIi7qKgjIiIiIiIiIi4XEVFBUXVJQQOk5OTQ0VFhYsqar6iA6KZf+l8vK3eVdbC\nfMP4ePTHxAQqKONWW7bAccfBxRfD55/D5s38sqyI3FzHD1m50r1BmYAAOOEE53t8fCDQcUar5QgK\ngosuMh/H//s/OOssMydp9WqYPbthEytu1LEj3Hxz9Wvt2sF//2s+L0RERERERJo7BWVERERERERE\nxOW8vLzo2LGj0z1HH300/v7+Lqqo+fK2ejMoYRAbb97InQPupEtEF3pG9eSp05/ijxv/oHtkd3eX\n2LolJ8PZZ0NKyqFjHTrw019BNT50z55GrKsGVitcc40Z++PI1Ve38LFLhwsMhE6dYOJE+PhjeOUV\nE35qQU9ARIR59xYsgOOPN1On2rSBJ5+En34yI8FERERERERaAk93FyAiIiIiIiIirU9sbCwPPfQQ\n11xzjcM9Dz/8MKGhoa4rqhnz8fShY3hHJg2ZxAMnPYAFCxH+EVg9Gn8cjNRgxw7zdrj8fGLCbICf\n04eGhDReWbURGwvvv2+mRf27uVO/fvDoo+Dn/F1oeazWFt1GJzIShg83451sNrBYTMMcF0yWEhER\nERERcRl1lBERERERERERtxg+fDiXXXZZtWsTJkygd+/eLq6o+fPx9CEmMIbowGiFZI6U3Q5lZQ1/\n3l9+qXrsp58YNXS/04dFRUF8fMOXcyQCAmDYMPj7b7jnHhg4EM48E776ynQdcXd90ngiI83HNy5O\nIRkREREREWl51FFGRERERERERNwiKiqK6dOnc9ttt/Hss8+yd+9eunXrxl133UViYiLh4eHuLlFa\ng/R0M+PolVcgIwNOP9201IiNBW/v+p+/urYwNhuRSz7kjrE38fyrAdU+bM4ciImp/+XrKyAAOneG\np56C/Hzw8oKgmqdG1ai0vJT0wnTKKsrw9PAkJiBG4S4RERERERFxCYvdbre7uwgRaZl69uwJwIYN\nG9xciYiIiIiINHWFhYUUFxfj7++Pr6+vu8uR1iI1FcaNg/nzKx/39YVvvzUtVOobltm5Ezp2rDq7\nyMODjFc/48PUU3hiRijJyeZwr14wfTr07w/BwbW7RIW9AluZDW9PbzwsTb+BdFJeEi///jIzV80k\nqyiLKP8o7h50N2OOGUNsYKy7yxMRERERaVS6fybifgrKiEij0Q96ERERERERabJsNvjvf+HJJ6tf\n9/GBzZuhXbv6XWf/fnOdadOqXS5//0NST7iI/CIrHh4mHBMdXctTF+8ntSCVV9e8yt8Zf9Mlogvj\n+o4jJiCGUL/Q+tXdSJLykhj69lA2pm+sstYvrh/zL51PXFCcGyoTEREREXEN3T8Tcb+m/xITERER\nERERERGRhpaWBjNnOl4vKYEPP6zaCeZIhYTAAw/AlCkQFnboeHQ0vPQS1iGnE59opUsX6NSp9iGZ\n7KJsXvztRbq+0JUpP09h/pb5TF05lW6zujFz9Uyyi7LrV3cjKK8o560/36o2JAPwe/LvzN8yH72u\nT0RERERERBqTOsqISKNRIlZERERERFojux1SUmDvXti+HWJjDwUgvLzcXZ0ctGkT9OjhfM/QofDp\npxAYWP/rlZaacE52NlgsJjQTEwNWa51Ot3zXcga/Odjh+pKrlnD6UafXtdpGkZyXTL+X+5Gcn+xw\nT5eILiwfs5yYwBgXViYiIiIi4jq6fybifp7uLkBEREREREREpKUoK4MNG+CCC2DnzkPHw8Jg7lw4\n7bSGyVxIA/Csxa/F/P3rHGSpwssL2rQxb/WUXZTNI8secbrnkWWPcHTM0UT4R9T7eg3Fjp2U/BSn\ne5LzkimrKHNRRSIiIiIiItIaafSSiIiIiIiIiEgD2bcPTj65ckgGTBOR8883TUykiQgJgV69nO+5\n9Vbw83NNPUegsLSQ1Umrne5ZnbSa4rJiF1VUOx4WDzqEdXC6p2N4R7yt3i6qqJ4yMiA5Gfbvd3cl\nIiIiIiIicgQUlBERERERERERaQClpfDqq5CXV/263Q7jx5vQjDQB0dEwa5YZg1Sdvn1rDtK4icVi\nIdDbeWuiQO9ALI7eNzeJDYzlwZMedLrnoVMeIiogykUV1VFKCrz/PpxzDgwcCBdfDCtWQGamuysT\nERERERGRWlBQRkRERERERESkAWRlwYcfOt/z/feOgzTiBn37wuLF0KnToWOennDFFTB/PsTGuq82\nJ6L8o7j2mGud7rnmmGuI9It0UUW1N6LLCM7qeFa1ayO7j+Tktie7uKIjlJJiZqtdein89hvs3g3f\nfgunnAJPPKGwjIiIiIiISDOgoIyIiIiIiIiISAOx291dgRyRwEA4/XTTDWTTJvjzT9i6FV58EeLj\n3V2dQ15WL27ufzNR/tV3Xonwi+D2Abfj7dn0RhjFBMYw94K5fDL6E45vczxxgXEMShjEgksXMHv4\nbKIDot1domNlZfDKK/Drr9WvT58OW7a4tCQRERERERE5cha7Xb/CEZHG0bNnTwA2bNjg5kpERERE\nREQan80GEybA1KmO9wwcCAsWQESE6+qSlslut7Mtexs3f3Uzi7YvOnj89PanM2fEHDqFd2pyo5f+\nLaMwA1u5DR+rDxH+zeCLYt8+OPpo511jzj8f3n4bgoJcV5eIiIiINCu6fybifp7uLkBERERERERE\npCXw9oabb4ZZs6C4uPo9kycrJCMNw2Kx0Cm8Ex+M+oCc4hwKSgvw9/In1DeUcL9wd5dXK5H+TW80\nlFM2W82jlf7+GwoLFZQRERERERFpwhSUERERERERERFpIAkJsHQpnHtu5fvpfn4wZw706eO+2qRl\nCvMLI8wvzN1ltA5eXuDvb4IwjsTFgY+P62oSERERERGRI6agjIiIiIiIiIhIA/H2hv794c8/YdMm\n2LjRhGf694eoKPD1dXeFIlJnERFwzTWmbZQj48dDaKjLShIREREREZEjp6CMiIiIiIiIiEgD8vSE\nNm3M25Ah7q5GWqScHLDbITgYrFZ3V9N6+PnB/ffDF1/A3r1V1888E/r2dX1dIiIiIiIickQ83F2A\niIiIiIiIiIiI1EJSErzzDlxwAQwfDs88A7t2QVmZuytrPRIT4eef4e67ITDQHEtIgBkz4K23ICbG\n4UMzM2HfPkhOhvJyF9UrIiIiIiIiVVjsdrvd3UWISMvUs2dPADZs2ODmSkRERERERESauZ074dRT\nTTDmcP7+sHQp9Otn2hnJkbPZICPD/DkgAEJCan5MSYl5THk5eHmZgIxH9a9JzMiAVavgv/+FDRvM\nBKdbb4XLLoP4+AZ8P0RERESkWdD9MxH3U0cZEREREREREZHmqLwc0tIgNdXc6JeWKzMTrrqqakgG\noLAQzj4bUlJcX1dzV1FhAkgTJpiRST16wJVXwu+/Q16e88f6+Jj5am3bQlycw5BMZiY8+qhpAPTr\nr5Cfbz6M990HgwdXP8FJREREREREGpdeZiIiIiIiIiIi0pzY7ebu+nvvwdtvQ2mpuQt/881mBIyP\nj7srlIaWnQ0rVhz6+/DhMHYs+PqavxcWmvRFQoJ76muutmyBQYMgJ+fQsfnzzdvcuTBypOkwUw/b\ntsHs2dWvbd1qpmdNmXLoQykiIiIiIiKNT0EZEREREREREZHmZMsWOOmkQ6NiADZvhlmz4LvvzI1/\nLy/31ScNLynp0J9nzzbBjuuvh/R0cywmBv7v/6BdO4iN1Qim2sjIgKuvrhySOdy115qWL/UIyhQW\nwtSpzve8/jqMHw+JiXW+jIiIiIiIiBwhjV4SEREREREREWku0tLg4osrh2QOKCmBc881o5ikZYmP\nh4ULYflyOPZY8/d27Q6tp6bC7bfDa6+ZUUJSs5wcWLXK8Xp5OSxYUK9LFBbC9u0179HkNBERERER\nEdfSy0tERERERERERJqLzEz480/H67m5sHZtqxjBk1uSS1ZRFgW2Avy9/An1DSXML8zdZTW81FQT\ngHnhBcjPN8c6d4bHHoMff4QXXzy0d9IkOOMMCAmBqCi3lNts7N9f856NG+t1CX9/6NABfvvN+R5N\nSxMREREREXEtdZQREREREREREWkuDh/B44izIE0LsTNnJ1d9dhUdZ3Sk14u96DCjA6M/Hs0/mf9Q\nYa9wd3kNJyMDbrnFBGAOhGQA/vkHLr8cTjgBBg48dLy42IzmOnyvA7ZyG1lFWeSW5DZC4c1ASEjN\ne7p3r9cl/P3h3nud77nmGoiMrNdlRERERERE5AgpKCMiIiIiIiIi0lyEh9e85/CRPC3Qvtx9nPzG\nyXyx+YtKoZjF2xcz8LWB7Nm/x43VNbDkZPjkE/D1hauugs8+MyOYXnoJjjsOHn4Ybr218mPy86Go\nyOEpC22FbM3ayoQlEzhn3jlc9MFFfLn5S5Lzkhv5nWliQkLMc+iI1QojRtT7Mh06wM03V7/WqRM8\n8ID58IqIiIiIiIjraPSSiIiIiIiIiEhzERsLiYmwx0EYxMsLBg92bU0uVF5Rzrt/vcve3L3VrmcV\nZTH9l+lMGjIJH88WMM/mnXegRw8zXumdd+CKK6CgALp2NZ1mgoMhJqbyY7p2BT+/ak9XWFrId9u/\nY9SHoyi3lx88vmTHEo6NPZYFly0gPii+Md+jpiMqCubONV15qhvD9PLLDdLqJSICJk6EYcMqmDjR\ngw0bzGlvvdU0BYpvJU+3iIiIiIhIU6KOMiIiIiIiIiIizUVsrAlMeDp47dPMmebOfAuVXpjOy2te\ndrrnzT/fJLMos97XSspLYtnOZTyx/AlmrZrF9uzt5BTn1Pu8tWa3Q//+8MEH8MQT8MorJiQDsHkz\n3H47fPedCXwc0L+/CX2EhlY+V1oarF5N2p6/q4RkDlibspb//vBfikodd6Npcbp2hT/+gDvuMF83\nAQFw9tnw668wahQEBtb/GsXFRGZuZvjyB1l4+Ty2vLOKX17+k7sv3kt8REn9zy8iIiIiIiJHTB1l\nRERERERERESaC4vFhCHWrIEJE+Crr0ygYsAAmDQJjjnG3Oxvoex2O/m2fKd78m352O32el3j74y/\nGfr2UPbl7Tt43PK1hTsG3MH/nfx/RAbUv9OIQxUVsG8ffPEFfPqpCUWNGAF3323akGzbdmjvu++a\nzjLh4eZt2jRISICwsEN7kpMPhj7ev6NftSGZA+b+OZcJJ0+gbUjbxnv/mhKrFdq3h2eegfHjzTF/\n/6pBo7oqKzOhm6FDobSUSp813t6waBGceKKpQ0RERERERFxGQRkRERERERERkebEzw969zadZfLy\nTFDGz69BxsQ0KenpkJMD2dkm+BEaSkBwAAMTBvL53587fNhx8cfh6+lb58sm5SUx+M3BpBemVzpu\nx870X6dzVNhR3NL/FqwejRBusNthwwY45RTzvh+waJEZufX223DZZZCUdGht9mwTqvHzM3N84uIO\nre3fb7ql/PwzxbfcwC+5G51evrismAJbQcO+T82Bj0/jzEBKTTUhpdLSqms2G/znP7B2reYviYiI\niIiIuJhGL4mIiIiIiIiINHU5ObB9OyxeDD/9BHv3gq+v6R6SmNiyQjIVFbBpEwwbBl26mG45XbrA\n2WcTvCOJh09+2OnDHz/tcSL86z5+6oddP1QJyRzuyRVPkpKfUufzO5WcbEb/HB6SOWDPHrj/frj3\n3srH9+2DTp1MN6HDQzJgQkaffAKAd3YusT41Py8+nj51q70lSkmBVatgxgx44w3YscOEj2pr2zbI\nyHC8npZmzikiIiIiIiIupaCMiIiIiIiIiEhTlpQEY8aYMMTQoXDSSdC9O7z0EmRlubu6hrdnD5xw\nAvz2W+Xja9bAgAF0skby0oiXsGCp8tD/nvZf+sb1rfOlbWU2Ptn0idM9aQVp5Nny6nwNp/75p3K3\nmH/79Vfo08eMYzqgd2+IiKh+fM/evSZ4FB6OR6fO3NT3BqeXP6XtKYT6htat9sOUV5STlJfErpxd\n7MvdR0lZSb3P6XLbtsGpp5qg1h13wLXXmq/BiROdh18Ot3NnzXt27apPlSIiIiIiIlIHGr0kIiIi\nIiIiIs1XXp4Ji2RkmPEp4eEQGwseLeS1QWlpMHIk/PLLoWMDB1J0fF8sWzbiu3ixWa8uJNFUlZSY\nTidgAh5eXofWbDZ48cXqO6oA5OYSPOk5Ln3iEYZ0GMK8v+axLnUdncM7M+aYMUQHRNcr6OHh4YG3\nh3fN+yyN9Pm1Zk3Ne/buNZ/naWnm7zffXPk5PJyvL3TtakJVU6eSsDiKq7pfylub3quy1d/Ln1nD\nZxHuFw5Aan4qRWVFWC1Wgn2CCfENqdW7kJqfyrvr32XKT1NIzk8mwCuAsX3Hcs+ge0gMSazVOdwu\nORnOOKNqiKWiAp57znTuufNOx8/7Ae3b13yt2uwRERERERGRBmWx2+12dxchIi1Tz549AdiwYYOb\nKxERERERkRYpOdmMonnvPSgrM8fatIFZs+C00yA42L31NYTVq+H44wGoOK4fSbMmsSjvDz5N+wEv\nDy/GJV7AMV0HExfRzs2F1oLNZrrFzJgBX35pwkwXXww33GA+bp6e5mN63HHOu6pERcEff0B8PABl\nFWV4ejTca8GW7FjCkLeGOFzvEtGFH8b8QGxgbINd84CiX34knQLyrRX4eHgRuvovgv7ZRcbQE7H7\n+eGzaQuRnfrABRdAQQFMnQrXXQchDkIs+/aZkV2XXw633gp9+pAe4ccnhb/x1Jrn2ZO7Bw+LB+d2\nOZdnhjxDh7AO5JbksnjHYh5Z9ghbMrfgYfFgeOfhPDPkGTqGd8Tb6jhIlJqfyhWfXcHi7YurrLUN\nacuP1/zYPMIy33wD55zjeD08HNatM5+3ziQlwbHHHgo1/VtMjAlH/e9zWURERERaB90/E3E/BWVE\npNHoB72IiIiIiDSatDS46CL46afq1xcudH6ju7m47z549lnsvXuz4Z1pDP7mYrKKKo9b6hbZjUVX\nLiIhOMFNRdZCeTmsWgWnnw7FxZXXgoJg+XI45hgTLOjTBzIzHZ8rKAg2bao5pFBHKfkpDH17KOvT\n1le7vvCyhZzTueE/t5Lzkpn4/UTmrptLcVkxvaJ78eq5r/L++vd488+55Jbk0jeuL/894SEG7Coj\nPDIRunSB0FDHJy0pgSefNCODvvsO5s0DoOL000i5cywlUWFY4xMJDY0hODiKnOIcnvnxGSb9NKnK\nqXysPqy8biXHxh3r8HJf//M1w94d5nD97oF389QZT+Hj6VPr58UtrrkG3nzT+Z716+F/v/dwqLwc\nfv4ZhgwxQbHDeXvD0qUwcGDz6gglIiIiIvWm+2ci7tdC+hCLiIiIiIiISKuyfbvjkAzA7bdDSorr\n6mks/7u5njxtImcuuqpKSAbg74y/GTd/HDnFOS4u7gikpMD551cNyYAZnzVypNkTFGS6ATlz2mkQ\nGNg4dQKxgbF8ffnXDO88HAuWg8ej/KP4cNSHnJB4QoNfMyUvhfPeO4+X1rxEcVkxkf6RvHDOC5z7\n3rlM//V5copzqLBX8FvSbwz7+AKe9fqN7N6dnYdkwIwl27gR0tMPhmQAPJYuI/68yzlq0DDaHnU0\nwfsyAMgozKg2JANQUl7CdV9eR3pBerXrOcU5TPl5itNyXl7zMhmFGc5rbgq8ax6/Vavxblar6Qj1\n118wdqzphhQVBePGmaBN//4KyYiIiIiIiLiBgjIiIiIiIiIi0vy8/rrz9a1bYf9+19TSmC68EBIS\n2OxfRHJ+ssNt3279ttoQTZPxzz8mrOHI9u2HgjIPPwwWS/X7LBZ47DHHo4YaSEJwAu9c9A5bb9/K\n8jHLWXP9Gv644Q8u6n4RIb4Nf+21qWv5Lfm3g38f23csz658lvTC6p+zp3+eRGp+LYJgnp7w4IPQ\nowfMmQMnVBPyqaiAl1+G0lLeX/++8zpT1joMZNnKbaQVOBgx9D/5tnzK7eU11+1uV17pfD0+vvaf\ngz4+pvPP88+bkWF//GH+3Llz7QI5IiIiIiIi0uAUlBERERERERGR5qWiAgoLa95X3gxuyNeka1c4\n5RRWZv3pdJsdO8l5joM0brd1a817du82/+3QAT78sGqIwMsL3nnHjBFygVDfUDqEdeDkdidzbNyx\nxAfHY/Vo+O4f+bZ8Zv46s9Kx09ufzsJ/Fjp93EtrXqbCXlH9ot1uns8PP4SLLzbdfKZOhQsugM8+\nq9qR58cfobiY3ft311hvcVk1XYGAAK8AekY5H0XUJqgNXh5eNV7D7Tp3hmMdj5hi8mSIjT2yc/r7\nm4BNfDz4+dWvPhEREREREakXBWVEREREREREpHnx8DA3/J0JDW30riMuERcHU6YQ6RdR49ZA78Yb\nR1RviYk17zkQPAgMhOHDTbjmzTdh/Hh47TXz9wsuMF1nWpCyijLybflVjjkMwfzPjuwd2Mpt1S/u\n2gWXXgoffwwPPQSff2469SxaZLqZvPFG5f3BwWCxMChxkNNrWi1Wgnyqf/4DvAMYf+J4p4+//8T7\niQmMcbqnSYiJgfnz4ZxzKh8PDjadeYYNq93oJREREREREWmSPN1dgIiIiIiIiIjIERs0CKKiHI/z\nufdeiI52bU2NJT6eM31G4vHTgw7DE3GBcUQFRLm4sCPQs6cJLjkah9WmDSQkHPq7n58J1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t61H4kbN6WPHz9HO6x8/TDwuWul0gL89035k3D/76C7p2hauvhqgoCAmp2znFCAyEYcNg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\nj4iI4OWXXwbgpZdeynV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+ }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "text": "\n" + } + ], + "execution_count": 2 + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/make_volcano.py b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/make_volcano.py new file mode 100644 index 0000000..b2b6356 --- /dev/null +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/make_volcano.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Here we understand the relationship between WT contribution and model significance +# +# Each linear model is regressed to each morphology feature according to covariate data + +# In[1]: + + +#%%--%%| +r"""°°° +## Imports +°°°""" + + +# In[ ]: + + +import pandas as pd +import numpy as np +from pathlib import Path +from sklearn.preprocessing import LabelEncoder +from sklearn.linear_model import LogisticRegression + +from sklearn.metrics import ( + confusion_matrix, + ConfusionMatrixDisplay, + precision_score, + accuracy_score, +) +from sklearn.model_selection import train_test_split + +import matplotlib.pyplot as plt +import seaborn as sns +import itertools + +from joblib import dump, load + + +# ## Find the git root Directory + +# In[ ]: + + +# Get the current working directory +cwd = Path.cwd() + +if (cwd / ".git").is_dir(): + root_dir = cwd + +else: + root_dir = None + for parent in cwd.parents: + if (parent / ".git").is_dir(): + root_dir = parent + break + +# Check if a Git root directory was found +if root_dir is None: + raise FileNotFoundError("No Git root directory found.") + + +# In[ ]: + + +output_path = Path("figures") + +output_path.mkdir( + parents=True, exist_ok=True +) # Create the parent directories if they don't exist + + +# ## Import the model data as a dataframe + +# In[ ]: + + +concatenated_df = pd.read_csv( + root_dir + / "3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data" + / "feature_compartments.tsv", + sep="\t", +) + + +# ## Plot the WT contribution for our features vs the Morphology Feature + +# In[2]: + + +# Set significance threshold and fold change threshold +significance_threshold = -np.log10(0.05) +fold_change_threshold = 1.0 +colors = ["red", "green", "blue", "black"] + +# Set the figure size +plt.figure(figsize=(15, 10)) + +# Create the scatter plot with size and color differentiation +sns.scatterplot( + data=concatenated_df, + x="WT_coef", + y="-log_p", + size="r2_score", + hue="compartment", + palette=colors, +) + +# Add threshold lines +plt.axhline( + significance_threshold, color="cyan", linestyle="--", label="Significance Threshold" +) + +# For adding contours: +# sns.kdeplot(data=concatenated_df, x='WT_coef', y='-log_p', levels=3, color='yellow', alpha=0.5) + +# Set plot labels and title +plt.xlabel("WT_coef") +plt.ylabel("-log10(p-value)") +plt.title("Significance of WT Contribution per Morphology Feature") + +# Show the plot +plt.savefig( + output_path / "feature_wt_contribution_and_significance_with_correlation.png" +) From 6c85cb27a0b26ef79ebb3355e0f4f2520e86dd8a Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Tue, 13 Jun 2023 16:58:51 -0600 Subject: [PATCH 06/12] Added the -log p_value column to dataset --- .../data/model_properties.tsv | 2324 ++++++++--------- .../nbconverted/train_model.py | 6 +- .../train_model.ipynb | 508 ++-- 3 files changed, 1423 insertions(+), 1415 deletions(-) diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv b/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv index 9a238e8..b5996e9 100644 --- a/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv +++ b/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv @@ -1,1162 +1,1162 @@ -feature r2_score p_value cell_count_coef plate_coef Null_coef WT_coef -Cytoplasm_AreaShape_Area 0.18474653920991335 1.1102230246251565e-16 -0.014366895583179384 0.16455192415455927 -0.23170835918151744 0.23170835918151778 -Cytoplasm_AreaShape_BoundingBoxArea 0.17189193243211054 1.1102230246251565e-16 -0.016323544414939307 0.20781946368794818 -0.17741893610840856 0.1774189361084092 -Cytoplasm_AreaShape_BoundingBoxMaximum_X 0.0006686522828195374 0.8477981783245556 -0.00020982393246391827 -0.002611737414587152 -0.02402534842193505 0.024025348421934985 -Cytoplasm_AreaShape_BoundingBoxMaximum_Y 0.004623520471195297 0.04861314077390888 -0.0009847817393303932 0.0012232505317791575 -0.05727241082936399 0.05727241082936387 -Cytoplasm_AreaShape_BoundingBoxMinimum_X 0.006643417228757387 0.008157038547171647 0.0038512013186171377 -0.0539343420742183 0.02167513974965026 -0.021675139749650487 -Cytoplasm_AreaShape_BoundingBoxMinimum_Y 0.0065068094079506755 0.009230214384759461 0.00481213451282194 -0.07467209079560387 -0.0028819431758143124 0.0028819431758139308 -Cytoplasm_AreaShape_Center_X 0.0008253840774573584 0.790237195752141 0.0017318404680637819 -0.026991759598672196 -0.00152286257861017 0.0015228625786100278 -Cytoplasm_AreaShape_Center_Y 0.0006476256396724667 0.8552666776527195 0.0018503618710664477 -0.03598224063554693 -0.03102832323811212 0.031028323238111867 -Cytoplasm_AreaShape_Compactness 0.0037210957930179367 0.1037319371043417 0.004257913124454014 -0.08431896166431146 -0.07765357386835678 0.0776535738683562 -Cytoplasm_AreaShape_ConvexArea 0.17982532454241573 1.1102230246251565e-16 -0.015633316704335737 0.19129038444518412 -0.20178093241798328 0.2017809324179838 -Cytoplasm_AreaShape_Eccentricity 0.004410350060662371 0.05830630055513919 -0.004982139383815284 0.0952028627189675 0.07662923769481841 -0.07662923769481776 -Cytoplasm_AreaShape_EquivalentDiameter 0.1494028496298394 1.1102230246251565e-16 -0.013932887661676883 0.16805131059321238 -0.1898453524123195 0.18984535241231992 -Cytoplasm_AreaShape_EulerNumber 0.001101561345625779 0.6857490404406357 0.0024104555849951514 -0.044024588888653085 -0.028692996185100357 0.028692996185100066 -Cytoplasm_AreaShape_Extent 0.010328495037530416 0.00026280138242185114 -0.0020499702462758434 0.012781197306802189 -0.07709512873755808 0.07709512873755797 -Cytoplasm_AreaShape_FormFactor 0.0051660518090178575 0.03040574483600289 -0.005589818784788794 0.10276991017516758 0.06932684755362556 -0.06932684755362487 -Cytoplasm_AreaShape_MajorAxisLength 0.12473749755865948 1.1102230246251565e-16 -0.01623183353864438 0.22430666790882142 -0.10375595998225381 0.10375595998225472 -Cytoplasm_AreaShape_MaxFeretDiameter 0.13582702830372229 1.1102230246251565e-16 -0.015693436821142704 0.208634455148764 -0.13419750390414664 0.13419750390414742 -Cytoplasm_AreaShape_MaximumRadius 0.1168327128685529 1.1102230246251565e-16 -0.013228504768089028 0.16673326314309245 -0.15070398435580012 0.15070398435580062 -Cytoplasm_AreaShape_MeanRadius 0.11640369081620916 1.1102230246251565e-16 -0.013266275613819548 0.1676720889026541 -0.14922959482847814 0.14922959482847867 -Cytoplasm_AreaShape_MedianRadius 0.11775107386397887 1.1102230246251565e-16 -0.013351006949437685 0.16880369669042444 -0.1499329201906602 0.14993292019066073 -Cytoplasm_AreaShape_MinFeretDiameter 0.14570799655408517 1.1102230246251565e-16 -0.012903500759889842 0.14898974125779602 -0.20317230066823588 0.2031723006682362 -Cytoplasm_AreaShape_MinorAxisLength 0.14568815204182783 1.1102230246251565e-16 -0.01171497300810986 0.1256130097232725 -0.2241913595616944 0.2241913595616945 -Cytoplasm_AreaShape_Orientation 0.00011232417337903033 0.993789681225094 -0.0008252930461758963 0.015078370132820918 0.00984536023256886 -0.009845360232568757 -Cytoplasm_AreaShape_Perimeter 0.13717574847089586 1.1102230246251565e-16 -0.010833583190902659 0.111447221849869 -0.2267313332606949 0.22673133326069494 -Cytoplasm_AreaShape_Solidity 0.01637981962202517 7.261393928370552e-07 -0.0044779741052228105 0.05295541845348736 -0.0653603140505073 0.06536031405050742 -Cytoplasm_AreaShape_Zernike_0_0 0.011439088937936548 9.074874710390812e-05 -0.0004049954243073187 -0.01934675273346631 -0.10525426512650221 0.10525426512650188 -Cytoplasm_AreaShape_Zernike_1_1 0.005596581406647094 0.020829660673047035 -0.00030829971541755294 -0.013072592528001317 -0.07331222170092558 0.07331222170092534 -Cytoplasm_AreaShape_Zernike_2_0 0.027911489803901013 6.3754557189099614e-12 -0.0037495390128448793 0.02827167255498375 -0.1208690817252653 0.12086908172526517 -Cytoplasm_AreaShape_Zernike_2_2 0.001321453572769915 0.6045612850898732 -0.002818920183897007 0.052186560187874535 0.036443620720813766 -0.03644362072081342 -Cytoplasm_AreaShape_Zernike_3_1 5.495999858551315e-07 0.9999998395215051 -4.9533755058258953e-05 0.0009974538229946088 0.0009714589025408957 -0.0009714589025408884 -Cytoplasm_AreaShape_Zernike_3_3 0.00031945520938991834 0.9563082435525218 -0.0013852221551404306 0.02567059924298695 0.01801561614894007 -0.0180156161489399 -Cytoplasm_AreaShape_Zernike_4_0 0.0027762270130549105 0.22003256070437405 0.0040300380514377235 -0.07091489295095138 -0.03690084874195006 0.036900848741949606 -Cytoplasm_AreaShape_Zernike_4_2 0.004051373007551873 0.07889856864035405 -0.004465972918278303 0.07442874211648193 0.023782013299101415 -0.02378201329910099 -Cytoplasm_AreaShape_Zernike_4_4 0.003137681995709074 0.16605046030928194 -0.0030562606489673567 0.06566326453041352 0.07689643374231767 -0.07689643374231717 -Cytoplasm_AreaShape_Zernike_5_1 0.0010675082288147397 0.6985940560612804 0.0024913675211216168 -0.047039312956034855 -0.03598212694359602 0.03598212694359569 -Cytoplasm_AreaShape_Zernike_5_3 0.0011295617537078195 0.6752267302181656 -0.00209974631145622 0.043262931116291176 0.04521666277577188 -0.04521666277577155 -Cytoplasm_AreaShape_Zernike_5_5 0.0005966185987893446 0.8730362010730379 0.0018503474668604045 -0.03511948725382365 -0.027478189453467274 0.02747818945346703 -Cytoplasm_AreaShape_Zernike_6_0 0.0022850810579421177 0.3175609369441468 0.0033749300870345154 -0.06651657454357736 -0.060246362267949514 0.06024636226794903 -Cytoplasm_AreaShape_Zernike_6_2 2.970658701462181e-05 0.9995404325247685 0.0001860767220049336 -0.002163602814667448 0.002867824038275239 -0.0028678240382752434 -Cytoplasm_AreaShape_Zernike_6_4 0.007918293552767297 0.0025352718563537158 -0.006839721077140726 0.12816145103295765 0.09475557652661619 -0.09475557652661531 -Cytoplasm_AreaShape_Zernike_6_6 0.0011181228145985544 0.6795205828221265 -0.0017575224654418577 0.03818270864745717 0.04595928648884736 -0.04595928648884706 -Cytoplasm_AreaShape_Zernike_7_1 0.005385854279391866 0.02508114297550923 0.0030574462643623455 -0.07119669627524755 -0.09959671835650799 0.09959671835650744 -Cytoplasm_AreaShape_Zernike_7_3 0.0019179086405771706 0.4116481021941131 0.003210530594643503 -0.06217246391482098 -0.052767816344329845 0.0527678163443294 -Cytoplasm_AreaShape_Zernike_7_5 0.001032442676744627 0.7118637178323952 0.0014998235947499192 -0.0337508111855445 -0.04402247611497913 0.04402247611497885 -Cytoplasm_AreaShape_Zernike_7_7 3.5766377674884e-05 0.9993365740153008 0.00033478746114242304 -0.007137653175124886 -0.008196147972625776 0.00819614797262572 -Cytoplasm_AreaShape_Zernike_8_0 0.0014528502275646948 0.5581676915178619 0.001034380278109718 -0.027776660335313008 -0.04888184873812218 0.04888184873812193 -Cytoplasm_AreaShape_Zernike_8_2 1.461736388130852e-05 0.9998875728891521 0.00029765458734849645 -0.005447373387437035 -0.003588450730478407 0.0035884507304783705 -Cytoplasm_AreaShape_Zernike_8_4 0.001596521357308922 0.5097792329261129 -0.0015606591041984036 0.037071909930880734 0.05384286566879661 -0.0538428656687963 -Cytoplasm_AreaShape_Zernike_8_6 0.0024511655886019224 0.2811452603365874 -0.0022950321101087743 0.03184765053510717 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-Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928875 0.03426765582386959 -0.005507817489283557 0.09900261499000954 0.059009099631753326 -0.059009099631752694 -Cytoplasm_Correlation_Correlation_DAPI_RFP 0.01647709493797045 6.594567361073445e-07 -0.006627974205122522 0.09667752833493706 -0.021433259287394056 0.0214332592873945 -Cytoplasm_Correlation_Correlation_GFP_RFP 0.00199587704315185 0.39006033120792405 0.0018136544577989986 -0.04256969265267932 -0.06046439275583843 0.06046439275583807 -Cytoplasm_Correlation_Costes_DAPI_GFP 0.12668875220153752 1.1102230246251565e-16 -0.007711576775701956 0.22597520296845497 0.4421891625863014 -0.44218916258629926 -Cytoplasm_Correlation_Costes_DAPI_RFP 0.058169420512800385 1.1102230246251565e-16 -0.012945726387770452 0.27949855271431157 0.33132311992936303 -0.3313231199293609 -Cytoplasm_Correlation_Costes_GFP_DAPI 0.07171136556320179 1.1102230246251565e-16 -0.0027105789835034006 0.11578817655987528 0.3050791104063639 -0.30507911040636265 -Cytoplasm_Correlation_Costes_GFP_RFP 0.016622614806175218 5.709150631139082e-07 -0.0014483839868720961 0.05829414008342612 0.14829604795815957 -0.14829604795815893 -Cytoplasm_Correlation_Costes_RFP_DAPI 0.19802434686833292 1.1102230246251565e-16 -0.019314341764655584 0.4442044078430728 0.6063003461727692 -0.6063003461727656 -Cytoplasm_Correlation_K_DAPI_GFP 0.008743794398715199 0.001175018238829817 -0.007234226804901091 0.1290649328631211 0.07351383959747056 -0.07351383959746972 -Cytoplasm_Correlation_K_DAPI_RFP 0.006214930262296137 0.012005574295978816 -0.0012012309935377358 0.0025386693453899184 -0.06555298649074703 0.0655529864907469 -Cytoplasm_Correlation_K_GFP_DAPI 0.005581927725120228 0.02110119235699781 0.005338620229683115 -0.08979893093517345 -0.03183237383401087 0.031832373834010344 -Cytoplasm_Correlation_K_GFP_RFP 0.018068858300495982 1.3558242606404036e-07 0.0069266749065762115 -0.1513700131877782 -0.18477915531346473 0.18477915531346353 -Cytoplasm_Correlation_K_RFP_DAPI 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0.00014556354045824893 0.9898029487911398 -0.0003623708894618319 0.009384543768966584 0.015699018868684318 -0.015699018868684234 -Cytoplasm_Correlation_Manders_RFP_GFP 0.04845038492229414 1.1102230246251565e-16 0.012828051473727224 -0.1971131896075793 0.00032516819538896957 -0.00032516819538996877 -Cytoplasm_Correlation_Overlap_DAPI_GFP 0.005221386256011873 0.02897080599445301 -0.0027778261739751877 0.03487271771463569 -0.03221917381704655 0.032219173817046656 -Cytoplasm_Correlation_Overlap_DAPI_RFP 0.044700483277687386 1.1102230246251565e-16 -0.010406894851813729 0.14855634474260931 -0.04699562327442729 0.046995623274427925 -Cytoplasm_Correlation_Overlap_GFP_RFP 0.021379891039952637 4.900020389442261e-09 0.004200186245216157 -0.1104968116943455 -0.18905254048020215 0.1890525404802012 -Cytoplasm_Correlation_RWC_DAPI_GFP 0.025089935195876323 1.1402523369952178e-10 0.010217635563995615 -0.1638920903800971 -0.028099911153720125 0.02809991115371921 -Cytoplasm_Correlation_RWC_DAPI_RFP 0.0003289541781874572 0.9539638694053856 -0.0012842748998345597 0.02150149694848574 0.007242765248701214 -0.0072427652487010875 -Cytoplasm_Correlation_RWC_GFP_DAPI 0.004081527711480959 0.07693405431750933 0.004567066900040596 -0.07683843676814384 -0.027303956378637917 0.027303956378637466 -Cytoplasm_Correlation_RWC_GFP_RFP 0.027099958811750424 1.4641954315663952e-11 0.011337522020371674 -0.22572106743274456 -0.2117292249742364 0.2117292249742348 -Cytoplasm_Correlation_RWC_RFP_DAPI 0.0011901726515266553 0.6526040608105218 -0.002486853537587508 0.048560798433762334 0.042530077114726075 -0.04253007711472573 -Cytoplasm_Correlation_RWC_RFP_GFP 0.038262913928817754 1.1102230246251565e-16 0.013669864639138789 -0.22737150139842816 -0.0709547615938646 0.07095476159386327 -Cytoplasm_Granularity_10_DAPI 0.0008700166154410605 0.7734418488461786 -0.0009939826604942938 0.011451554098633802 -0.015755423018955862 0.015755423018955883 -Cytoplasm_Granularity_10_GFP 0.00013034106753262442 0.9917393246479322 -0.0008710848509915131 0.01643817208107074 0.012544977529985825 -0.012544977529985712 -Cytoplasm_Granularity_10_RFP 0.003761712566425768 0.1003252629539767 0.003566809207567474 -0.05475771180547825 0.00029267731881536113 -0.0002926773188156387 -Cytoplasm_Granularity_11_DAPI 0.005180989848801887 0.030011757301464215 0.005159648740914257 -0.08693348556675273 -0.03136190394869354 0.03136190394869302 -Cytoplasm_Granularity_11_GFP 0.002334802229619015 0.3062712118045383 0.0036441557354518323 -0.0693886760335573 -0.05503383838597352 0.05503383838597304 -Cytoplasm_Granularity_11_RFP 0.008314081070003199 0.001755350747339346 0.002034919438439572 -0.015209613918705886 0.06614763428265089 -0.06614763428265082 -Cytoplasm_Granularity_12_DAPI 0.002751908698306993 0.22416741203247903 0.0015234576059579715 -0.015570811666440941 0.03230072143294328 -0.03230072143294329 -Cytoplasm_Granularity_12_GFP 0.0041811951465038355 0.0707640952162436 0.004441508030217465 -0.07326958888922164 -0.02055889880146168 0.020558898801461258 -Cytoplasm_Granularity_12_RFP 0.017034681919072536 3.7936129460014456e-07 -0.008626578937455836 0.17438701176770877 0.17196274333597086 -0.1719627433359696 -Cytoplasm_Granularity_13_DAPI 0.00011316907939706855 0.9936995207044121 -0.0002066133200880411 0.006340149354769783 0.013023257545315216 -0.013023257545315154 -Cytoplasm_Granularity_13_GFP 0.007443841776081039 0.0039280912043984095 -0.0011146522823604738 0.04158486225826788 0.10063682516764824 -0.1006368251676478 -Cytoplasm_Granularity_13_RFP 0.016534364292089876 6.230874355361848e-07 -0.0013168894588963963 0.05587144662175819 0.14664404819273713 -0.14664404819273652 -Cytoplasm_Granularity_14_DAPI 6.74822963525612e-05 0.9976889446982755 -0.0006301956910982547 0.011832348481753779 0.008828744669552321 -0.00882874466955224 -Cytoplasm_Granularity_14_GFP 0.003157653441450581 0.16344816049010946 0.0012739510257342985 -0.009754647163901897 0.040453425349254675 -0.04045342534925463 -Cytoplasm_Granularity_14_RFP 0.009850189170258372 0.0004140812515598391 0.004883378246378869 -0.0696969351968968 0.022103120120776937 -0.022103120120777253 -Cytoplasm_Granularity_15_DAPI 0.001494568548114028 0.5438506026940519 -0.0018671589740721943 0.04159361108728209 0.0530616588214852 -0.053061658821484864 -Cytoplasm_Granularity_15_GFP 0.0020953718002516553 0.36378156158444064 0.0008180119208344385 -0.0037533449524093307 0.03630713868305872 -0.036307138683058654 -Cytoplasm_Granularity_15_RFP 0.034125533803847086 1.0436096431476471e-14 -0.011784498254099763 0.24114273394955688 0.24692371787686065 -0.24692371787685888 -Cytoplasm_Granularity_16_DAPI 0.009731631679994024 0.0004633240051676868 0.007226632721418378 -0.12324129960333816 -0.05002456605624575 0.050024566056245 -Cytoplasm_Granularity_16_GFP 0.01260736521877992 2.9356292132742823e-05 -0.008452697569135218 0.14675097611746143 0.0692158431896126 -0.06921584318961169 -Cytoplasm_Granularity_16_RFP 0.005325210338640973 0.02645273951490723 -0.0011101117449085602 0.03812163510971825 0.08666965428945714 -0.08666965428945675 -Cytoplasm_Granularity_1_DAPI 0.07647272368069058 1.1102230246251565e-16 -0.010752801276124446 0.13590485293829288 -0.12095399806963847 0.12095399806963889 -Cytoplasm_Granularity_1_GFP 0.15862501658920913 1.1102230246251565e-16 -0.007985903521542373 0.04917729569929702 -0.3028579142581592 0.3028579142581588 -Cytoplasm_Granularity_1_RFP 0.07976624478880545 1.1102230246251565e-16 0.012888393237553602 -0.2919905507118198 -0.38636696621633326 0.3863669662163309 -Cytoplasm_Granularity_2_DAPI 0.013783964245781544 9.336330784925906e-06 -0.0037042151263353305 0.04061425434821721 -0.06720005161457258 0.06720005161457264 -Cytoplasm_Granularity_2_GFP 0.006351163769724888 0.010621467992987355 0.003635692360195843 -0.05005999181607998 0.02398647681280286 -0.02398647681280306 -Cytoplasm_Granularity_2_RFP 0.026576123872976476 2.502187346209439e-11 0.005561917567722708 -0.06464110395108821 0.08584389442409998 -0.08584389442410012 -Cytoplasm_Granularity_3_DAPI 0.004861761699844425 0.03960374614767348 0.004423543232503832 -0.07044940311189657 -0.010087804927984799 0.010087804927984417 -Cytoplasm_Granularity_3_GFP 0.01887264431169 6.076666880794335e-08 0.006594393828064341 -0.09304280763759606 0.03426893446642305 -0.03426893446642344 -Cytoplasm_Granularity_3_RFP 0.024745318885012302 1.6195356167258979e-10 0.005818268563442486 -0.07133928172161376 0.07449391961705294 -0.07449391961705314 -Cytoplasm_Granularity_4_DAPI 0.015940070831693798 1.121782617685696e-06 0.006771419980777344 -0.10042848334601694 0.015070365217135381 -0.01507036521713586 -Cytoplasm_Granularity_4_GFP 0.022766489263839063 1.207181798967838e-09 0.0041116028593136435 -0.03952672763139582 0.09745171526141748 -0.09745171526141747 -Cytoplasm_Granularity_4_RFP 0.023750876205265503 4.4501646812022955e-10 0.00641299082068573 -0.08421325965461741 0.05913347799036496 -0.05913347799036526 -Cytoplasm_Granularity_5_DAPI 0.043594997218054043 1.1102230246251565e-16 0.008305024593644456 -0.10635608847387819 0.08770399290039052 -0.08770399290039087 -Cytoplasm_Granularity_5_GFP 0.029088368086080063 1.904143509534606e-12 0.0014414739012570178 0.016158379708807152 0.15770919852702148 -0.15770919852702106 -Cytoplasm_Granularity_5_RFP 0.015266931525493521 2.179214255049189e-06 0.0056152854233033475 -0.07717134890778499 0.037646689832150526 -0.03764668983215083 -Cytoplasm_Granularity_6_DAPI 0.0607240321011564 1.1102230246251565e-16 0.006749081233866624 -0.06521650352978633 0.158587549293555 -0.158587549293555 -Cytoplasm_Granularity_6_GFP 0.03022343115870063 5.919709167301335e-13 0.0030332732801674907 -0.012862294939943203 0.1389750198000551 -0.13897501980005486 -Cytoplasm_Granularity_6_RFP 0.01355835037552533 1.163737040010826e-05 0.00502437230617644 -0.06726114823369321 0.04104939308216804 -0.041049393082168296 -Cytoplasm_Granularity_7_DAPI 0.03495369952127292 4.440892098500626e-15 0.005885226583762701 -0.06441084938277948 0.10724740428558964 -0.10724740428558972 -Cytoplasm_Granularity_7_GFP 0.019948292873705586 2.068551185896439e-08 0.0019686691251815432 -0.0010951178259406558 0.12005045438879482 -0.12005045438879455 -Cytoplasm_Granularity_7_RFP 0.0068161411470623046 0.006973486447476085 0.0014019186773225332 -0.005377735306957954 0.0665650786828842 -0.06656507868288407 -Cytoplasm_Granularity_8_DAPI 0.006686116364260197 0.007847358169824181 0.005700215976006792 -0.09469842241273377 -0.029120209237943326 0.029120209237942778 -Cytoplasm_Granularity_8_GFP 0.012264198298360895 4.093536880866999e-05 0.00021515460100190924 0.0237754941544423 0.11147147107162804 -0.11147147107162766 -Cytoplasm_Granularity_8_RFP 0.009177395448536685 0.000781989528564675 0.0013785958797820626 -0.001557026306560086 0.08081526953198814 -0.08081526953198798 -Cytoplasm_Granularity_9_DAPI 0.0018006015906264494 0.4457760730461524 0.001381631956859259 -0.03480658833294152 -0.05584599351166265 0.055845993511662355 -Cytoplasm_Granularity_9_GFP 0.0002442501560000476 0.9731392690021424 0.0011424626512177264 -0.019459156032257653 -0.007809078746241701 0.007809078746241585 -Cytoplasm_Granularity_9_RFP 0.011876434659947144 5.95448596415471e-05 0.0020014519906748373 -0.009960290523731069 0.08563606979716416 -0.08563606979716402 -Cytoplasm_Intensity_IntegratedIntensityEdge_DAPI 0.09074831220092539 1.1102230246251565e-16 -0.004444088342414468 0.006948341648111311 -0.25257893861879144 0.25257893861879094 -Cytoplasm_Intensity_IntegratedIntensityEdge_GFP 0.04079393274337284 1.1102230246251565e-16 -0.000759580225869157 -0.03663110868606562 -0.19883033401424585 0.1988303340142452 -Cytoplasm_Intensity_IntegratedIntensityEdge_RFP 0.04104147950050285 1.1102230246251565e-16 0.0024254421868306125 -0.09425013639702673 -0.23447036441487235 0.23447036441487135 -Cytoplasm_Intensity_IntegratedIntensity_DAPI 0.1554375053275413 1.1102230246251565e-16 -0.012157030169902303 0.1308535288442052 -0.2305906753237856 0.23059067532378574 -Cytoplasm_Intensity_IntegratedIntensity_GFP 0.11956541883270178 1.1102230246251565e-16 -0.012227964537080806 0.14564272803999587 -0.17420717518530124 0.17420717518530157 -Cytoplasm_Intensity_IntegratedIntensity_RFP 0.07838382358195373 1.1102230246251565e-16 -0.00596026842431629 0.04118740161995041 -0.20758173416850012 0.20758173416849984 -Cytoplasm_Intensity_LowerQuartileIntensity_DAPI 0.014553303920371352 4.3953341651858935e-06 0.0036484134858484566 -0.03863974943131641 0.07179642895304558 -0.07179642895304561 -Cytoplasm_Intensity_LowerQuartileIntensity_GFP 0.03770666943969847 2.220446049250313e-16 0.010200064641846982 -0.14991194752441986 0.028329868568107014 -0.02832986856810772 -Cytoplasm_Intensity_LowerQuartileIntensity_RFP 0.011707491316389373 7.008198051117187e-05 0.008057327154158675 -0.13882089342803133 -0.06159118896165591 0.06159118896165505 -Cytoplasm_Intensity_MADIntensity_DAPI 0.013718651254554737 9.951470053781719e-06 0.006387903243840341 -0.09542764483193951 0.011388396072504761 -0.011388396072505226 -Cytoplasm_Intensity_MADIntensity_GFP 0.07166378293270292 1.1102230246251565e-16 0.01198346024822436 -0.16345760569313267 0.08541231375718952 -0.08541231375719019 -Cytoplasm_Intensity_MADIntensity_RFP 0.012011050592877326 5.2287397473871344e-05 0.006334518677642407 -0.09699655002308805 0.0015532221785253264 -0.0015532221785258121 -Cytoplasm_Intensity_MassDisplacement_DAPI 0.00886120762609699 0.0010525640419272175 0.007120168632143353 -0.12406916271993065 -0.060167998712748236 0.06016799871274746 -Cytoplasm_Intensity_MassDisplacement_GFP 0.00556448819325972 0.021428816484061586 -0.004990086178516455 0.08132940861131864 0.019024074373029387 -0.01902407437302893 -Cytoplasm_Intensity_MassDisplacement_RFP 0.006852608602759824 0.006745998096407746 -0.006322554247717586 0.11110228452027782 0.057262190558235324 -0.057262190558234616 -Cytoplasm_Intensity_MaxIntensityEdge_DAPI 0.06810525005759416 1.1102230246251565e-16 0.014220669949970578 -0.21236137947177486 0.025675577860318183 -0.02567557786031921 -Cytoplasm_Intensity_MaxIntensityEdge_GFP 0.03239381874950997 6.294964549624638e-14 0.005042012937953965 -0.04982142789263744 0.11394644079452246 -0.11394644079452246 -Cytoplasm_Intensity_MaxIntensityEdge_RFP 0.004106218124925509 0.0753599505618121 0.004526237691030109 -0.07568433577310824 -0.025137008864301785 0.02513700886430134 -Cytoplasm_Intensity_MaxIntensity_DAPI 0.05671586319590616 1.1102230246251565e-16 0.013632772310371214 -0.207901491607797 0.006835884994328578 -0.006835884994329633 -Cytoplasm_Intensity_MaxIntensity_GFP 0.004733757463202259 0.04422435137312897 -0.0033528450070647647 0.07449157230111961 0.09446833667475357 -0.09446833667475298 -Cytoplasm_Intensity_MaxIntensity_RFP 0.0006338886393454368 0.8601030189140192 0.000867179392635046 -0.010145354175864419 0.013108813720948324 -0.013108813720948347 -Cytoplasm_Intensity_MeanIntensityEdge_DAPI 0.0587537591589149 1.1102230246251565e-16 0.011744877813830408 -0.16644041823891756 0.058040087260296774 -0.058040087260297496 -Cytoplasm_Intensity_MeanIntensityEdge_GFP 0.08026369120292831 1.1102230246251565e-16 0.012877894806624683 -0.17699996947467123 0.08626366121681703 -0.08626366121681775 -Cytoplasm_Intensity_MeanIntensityEdge_RFP 0.021559538475623863 4.088002825142212e-09 0.01099283392349338 -0.1900924928704129 -0.08689243663200796 0.0868924366320068 -Cytoplasm_Intensity_MeanIntensity_DAPI 0.030131208203921234 6.509237593377293e-13 0.00768133632616821 -0.1041693197495304 0.05724379082078092 -0.05724379082078132 -Cytoplasm_Intensity_MeanIntensity_GFP 0.08804221384129063 1.1102230246251565e-16 0.010740424699980494 -0.12989619705322963 0.1449022256858234 -0.14490222568582373 -Cytoplasm_Intensity_MeanIntensity_RFP 0.01313440510625452 1.7591094030544596e-05 0.007751207324838775 -0.12695871211765916 -0.0321353408029062 0.03213534080290548 -Cytoplasm_Intensity_MedianIntensity_DAPI 0.015419001670353483 1.8759892819186774e-06 0.004640688618996311 -0.057269443410513834 0.057898990314582705 -0.057898990314582864 -Cytoplasm_Intensity_MedianIntensity_GFP 0.0692924608478962 1.1102230246251565e-16 0.012930697563938236 -0.18441005215685038 0.05910500716864707 -0.05910500716864789 -Cytoplasm_Intensity_MedianIntensity_RFP 0.012489363068238446 3.2914871870848295e-05 0.007782812365243682 -0.12923380328518747 -0.039499835886846246 0.0394998358868455 -Cytoplasm_Intensity_MinIntensityEdge_DAPI 0.014076177206968077 7.015665123066839e-06 0.0037061756774387916 -0.040314354801024126 0.06855846581314838 -0.06855846581314842 -Cytoplasm_Intensity_MinIntensityEdge_GFP 0.012277476052323655 4.041271649979361e-05 0.005915994006594782 -0.08756955783684221 0.01387410082605469 -0.013874100826055093 -Cytoplasm_Intensity_MinIntensityEdge_RFP 0.03341532585856355 2.1871393585115584e-14 0.014153471402013677 -0.25283874146627144 -0.14517866155352097 0.1451786615535193 -Cytoplasm_Intensity_MinIntensity_DAPI 0.01449645631975438 4.6474548969221985e-06 0.003702418802660505 -0.039761291975851 0.07059714391824534 -0.07059714391824537 -Cytoplasm_Intensity_MinIntensity_GFP 0.012555621103639836 3.086708527832993e-05 0.0057738842375740845 -0.0841428089494925 0.018987109723054577 -0.018987109723054972 -Cytoplasm_Intensity_MinIntensity_RFP 0.04590967490197184 1.1102230246251565e-16 0.01398622772945371 -0.22549653890483762 -0.04322119443259026 0.04322119443258898 -Cytoplasm_Intensity_StdIntensityEdge_DAPI 0.04264827762132595 1.1102230246251565e-16 0.010399200283436424 -0.14999298919195142 0.04059563814639207 -0.04059563814639276 -Cytoplasm_Intensity_StdIntensityEdge_GFP 0.0496578769920234 1.1102230246251565e-16 0.00637733782955769 -0.06430744530812765 0.1388089390367879 -0.13880893903678793 -Cytoplasm_Intensity_StdIntensityEdge_RFP 0.00938462223738068 0.0006432341403108666 0.005507352654024755 -0.08374299004935687 0.0037692613774840955 -0.0037692613774845188 -Cytoplasm_Intensity_StdIntensity_DAPI 0.08310467571203939 1.1102230246251565e-16 0.01689827878625026 -0.2602898423537952 -0.002182733708075535 0.0021827337080742026 -Cytoplasm_Intensity_StdIntensity_GFP 0.05037052358742766 1.1102230246251565e-16 0.0026391535480969103 0.0074315063494072575 0.19756718592420863 -0.19756718592420813 -Cytoplasm_Intensity_StdIntensity_RFP 0.012707827870779731 2.6629636778685573e-05 0.0053962153571932 -0.07603812555555857 0.02845038870206563 -0.02845038870206596 -Cytoplasm_Intensity_UpperQuartileIntensity_DAPI 0.022291565322968165 1.951829364799096e-09 0.006842550853894068 -0.09442695479363164 0.04427278205926197 -0.044272782059262335 -Cytoplasm_Intensity_UpperQuartileIntensity_GFP 0.08461426517473747 1.1102230246251565e-16 0.010122646565784125 -0.11926781409087528 0.149561175059289 -0.1495611750592893 -Cytoplasm_Intensity_UpperQuartileIntensity_RFP 0.012365036276811736 3.712815934120428e-05 0.0074269157328729 -0.12095996474460094 -0.027962791313501267 0.027962791313500587 -Cytoplasm_Location_CenterMassIntensity_X_DAPI 0.000820971422202943 0.7918911021386273 0.0017187153484721279 -0.026731761212607084 -0.0012831512912200235 0.0012831512912198813 -Cytoplasm_Location_CenterMassIntensity_X_GFP 0.0007880425228913301 0.8041867100453004 0.0016993933523018671 -0.02653326978131224 -0.0016886762440684912 0.0016886762440683524 -Cytoplasm_Location_CenterMassIntensity_X_RFP 0.0007866405503756946 0.804708211979182 0.0017169009877283459 -0.026932631698232883 -0.0022247178774269377 0.0022247178774267955 -Cytoplasm_Location_CenterMassIntensity_Y_DAPI 0.0006408890857526606 0.8576427977233153 0.0018432529914507187 -0.03581950078650076 -0.030808274578812658 0.0308082745788124 -Cytoplasm_Location_CenterMassIntensity_Y_GFP 0.000630036751665175 0.861452721114533 0.001851253218543876 -0.03573450881099232 -0.02995227712064781 0.02995227712064756 -Cytoplasm_Location_CenterMassIntensity_Y_RFP 0.0006567480315420315 0.8520359307733876 0.001867607258949424 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-0.026353401283591094 0.026353401283590868 -Cytoplasm_Location_MaxIntensity_Y_GFP 0.0005274894648467088 0.896176157872141 0.0016764339782516368 -0.03253686372015717 -0.02785177838533706 0.02785177838533683 -Cytoplasm_Location_MaxIntensity_Y_RFP 0.0005174445711179088 0.8994334515678626 0.001771619889372523 -0.03228258508939866 -0.020782759203354064 0.020782759203353852 -Cytoplasm_Number_Object_Number 0.14301412323815854 1.1102230246251565e-16 0.022307121678997385 -0.34451192107504147 -0.006618097534413614 0.00661809753441181 -Cytoplasm_RadialDistribution_FracAtD_DAPI_1of4 0.003361343947605122 0.13894394944824495 -0.0038471267348392534 0.062475444501208253 0.013737364173246239 -0.013737364173245885 -Cytoplasm_RadialDistribution_FracAtD_DAPI_2of4 0.005092860203852467 0.03240929769388312 -0.004191348130091957 0.0646134913945494 0.0007585235814501143 -0.0007585235814497812 -Cytoplasm_RadialDistribution_FracAtD_DAPI_3of4 0.005910220770351193 0.01576759506340597 -0.0029871531658937755 0.0377387103536038 -0.03366704721338188 0.033667047213382 -Cytoplasm_RadialDistribution_FracAtD_DAPI_4of4 0.005555931879330678 0.021591354432315857 0.003473504725296112 -0.048323743178682634 0.020871151654035445 -0.020871151654035643 -Cytoplasm_RadialDistribution_FracAtD_GFP_1of4 0.011669413333983969 7.270143519388927e-05 0.0026459410605297823 -0.02253950236818622 0.07463762043670237 -0.07463762043670231 -Cytoplasm_RadialDistribution_FracAtD_GFP_2of4 0.009813025873182335 0.0004289321896713272 0.002180057002818877 -0.015935431054491286 0.07234307982341287 -0.07234307982341279 -Cytoplasm_RadialDistribution_FracAtD_GFP_3of4 0.004102175777975892 0.07561556856512308 0.0013376081976134288 -0.008927092813977654 0.047887189058019246 -0.04788718905801918 -Cytoplasm_RadialDistribution_FracAtD_GFP_4of4 0.006744859303470352 0.007440038807423943 -0.0018028801838774354 0.013124923483917694 -0.06004695708079005 0.060046957080789984 -Cytoplasm_RadialDistribution_FracAtD_RFP_1of4 0.0033651275081684195 0.13852233301586725 0.0029849522202331138 -0.043472453006183714 0.00992802592856867 -0.009928025928568868 -Cytoplasm_RadialDistribution_FracAtD_RFP_2of4 0.0029756728426880708 0.1885793123074293 0.0030269409493874654 -0.045555085946232754 0.0040126553094279455 -0.0040126553094281675 -Cytoplasm_RadialDistribution_FracAtD_RFP_3of4 0.0016069979523801736 0.5063546830315826 0.0026992343448554255 -0.0441222182551672 -0.01082377131321332 0.010823771313213073 -Cytoplasm_RadialDistribution_FracAtD_RFP_4of4 0.002183159501151888 0.34176619198580893 -0.0029029977041593385 0.04582489762153613 0.004939776520357625 -0.004939776520357382 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_1of4 0.026150670356733796 3.864764064331894e-11 -0.008935160942624025 0.13422849476277818 -0.012851504006392026 0.012851504006392678 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_2of4 0.03965755680045435 1.1102230246251565e-16 -0.009920217284806404 0.1423855120796844 -0.041602288888080874 0.04160228888808151 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_3of4 0.04057281266738688 1.1102230246251565e-16 -0.010489485466232056 0.15357983609240033 -0.03154472777497755 0.03154472777497826 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_4of4 0.044791648164616116 1.1102230246251565e-16 0.009696631090553038 -0.1337942566092568 0.0628170990401274 -0.06281709904012797 -Cytoplasm_RadialDistribution_MeanFrac_GFP_1of4 0.010678140592266683 0.00018824421528584878 0.0015394158165649506 -0.0026654951419884665 0.08642796718848064 -0.08642796718848046 -Cytoplasm_RadialDistribution_MeanFrac_GFP_2of4 0.011134866653659148 0.00012155341731501235 0.001326901175847777 0.0018830671000312216 0.091703774229858 -0.09170377422985776 -Cytoplasm_RadialDistribution_MeanFrac_GFP_3of4 0.0072789316585334385 0.004570214905069592 -0.0003629546611953275 0.0279208038458012 0.09195630351671882 -0.09195630351671848 -Cytoplasm_RadialDistribution_MeanFrac_GFP_4of4 0.01655567651677259 6.100688976218294e-07 -0.002868328657810678 0.021401302950388335 -0.09339287243730349 0.09339287243730338 -Cytoplasm_RadialDistribution_MeanFrac_RFP_1of4 0.004173096156914413 0.0712474758032654 0.004240200820155366 -0.06852958494381998 -0.013786033016795228 0.013786033016794846 -Cytoplasm_RadialDistribution_MeanFrac_RFP_2of4 0.008471313552165682 0.001515980512774795 0.006151984403994113 -0.10022950091067186 -0.023302386966207497 0.023302386966206942 -Cytoplasm_RadialDistribution_MeanFrac_RFP_3of4 0.007522140000364108 0.0036550923893630127 0.00499331799321581 -0.07633307854769958 0.001745020723683162 -0.0017450207236835436 -Cytoplasm_RadialDistribution_MeanFrac_RFP_4of4 0.012119645564204196 4.707829976413613e-05 -0.00722066434214228 0.11666121379127067 0.023318917228897035 -0.023318917228896383 -Cytoplasm_RadialDistribution_RadialCV_DAPI_1of4 0.002401462940082033 0.29165797993695763 0.0037411443341174833 -0.06571237689461594 -0.033765916075393546 0.03376591607539312 -Cytoplasm_RadialDistribution_RadialCV_DAPI_2of4 0.006110070736542683 0.013189192685102435 0.00452097499980671 -0.06924609896135869 0.0010294462891734107 -0.0010294462891737646 -Cytoplasm_RadialDistribution_RadialCV_DAPI_3of4 0.01712827214012158 3.4569358486891133e-07 0.00784965579353368 -0.12208290408175493 -0.005837940173323758 0.0058379401733231195 -Cytoplasm_RadialDistribution_RadialCV_DAPI_4of4 0.04839540997985814 1.1102230246251565e-16 0.012498783272497654 -0.19000257409730745 0.008759234781079428 -0.0087592347810804 -Cytoplasm_RadialDistribution_RadialCV_GFP_1of4 0.011130269407989113 0.00012209076773861405 -0.0024151842398791143 0.018762993670042555 -0.07558151324053974 0.07558151324053966 -Cytoplasm_RadialDistribution_RadialCV_GFP_2of4 0.016127338183120177 9.322154010948935e-07 -0.005472142301477483 0.07320256097219707 -0.04492532109517687 0.04492532109517715 -Cytoplasm_RadialDistribution_RadialCV_GFP_3of4 0.016924168451469157 4.2334329963811257e-07 -0.007715235917192165 0.11942476826161046 0.0034019597920738193 -0.0034019597920731948 -Cytoplasm_RadialDistribution_RadialCV_GFP_4of4 0.006645751936781519 0.008139801255365509 -0.0024673834148694032 0.06372873136196072 0.10619232572701309 -0.10619232572701252 -Cytoplasm_RadialDistribution_RadialCV_RFP_1of4 0.0026260879754109734 0.2466609527386866 0.00045742870058239923 -0.021065875323687644 -0.05776441512123422 0.05776441512123398 -Cytoplasm_RadialDistribution_RadialCV_RFP_2of4 0.0012068486611087614 0.646422970814643 -0.0016614681402573766 0.02340192912164893 -0.00880021641889303 0.008800216418893128 -Cytoplasm_RadialDistribution_RadialCV_RFP_3of4 0.0013550008166336447 0.5925396420711423 -0.002666811533978616 0.04519152108807726 0.017276357146072112 -0.01727635714607184 -Cytoplasm_RadialDistribution_RadialCV_RFP_4of4 0.012738008394038824 2.586076535893156e-05 0.0005067883890415495 0.018955631460489786 0.11008488301064019 -0.11008488301063984 -Cytoplasm_Texture_AngularSecondMoment_DAPI_3_00_256 0.09569356714442645 1.1102230246251565e-16 -0.013920218730819242 0.19037222442155236 -0.09717214265505131 0.09717214265505206 -Cytoplasm_Texture_AngularSecondMoment_DAPI_3_01_256 0.09485010992171183 1.1102230246251565e-16 -0.013716551461081151 0.18662095033391413 -0.0997261298813547 0.09972612988135543 -Cytoplasm_Texture_AngularSecondMoment_DAPI_3_02_256 0.09526264367884263 1.1102230246251565e-16 -0.01368954021441983 0.18586489719436267 -0.10112899837006975 0.10112899837007047 -Cytoplasm_Texture_AngularSecondMoment_DAPI_3_03_256 0.09471150790249017 1.1102230246251565e-16 -0.013446594938145665 0.18117652724582461 -0.10505489673690367 0.10505489673690435 -Cytoplasm_Texture_AngularSecondMoment_GFP_3_00_256 0.0015539840935540061 0.5238322122229426 -0.0030306825847071185 0.05362171842532772 0.028952423594964236 -0.028952423594963896 -Cytoplasm_Texture_AngularSecondMoment_GFP_3_01_256 0.0016403898172766551 0.49553774602228473 -0.0031473946322612125 0.057997883808018694 0.03958009466668632 -0.03958009466668594 -Cytoplasm_Texture_AngularSecondMoment_GFP_3_02_256 0.0016103149702413333 0.5052734757840045 -0.0031165794659438403 0.05601221517822998 0.03335687130494836 -0.03335687130494801 -Cytoplasm_Texture_AngularSecondMoment_GFP_3_03_256 0.000706398015977272 0.8342112496858456 -0.002054900562885847 0.03822718024370798 0.027327186202057496 -0.027327186202057235 -Cytoplasm_Texture_AngularSecondMoment_RFP_3_00_256 0.005699803137333648 0.019010658450448803 -0.00524803517522993 0.10410034500466953 0.09642752788114206 -0.0964275278811413 -Cytoplasm_Texture_AngularSecondMoment_RFP_3_01_256 0.005885513984607571 0.016118554219621495 -0.00515116560670079 0.10354286804125384 0.10026193117016238 -0.1002619311701616 -Cytoplasm_Texture_AngularSecondMoment_RFP_3_02_256 0.006699273138841555 0.00775427597175804 -0.005311282390629104 0.10805749273343634 0.1087132709422475 -0.10871327094224673 -Cytoplasm_Texture_AngularSecondMoment_RFP_3_03_256 0.004709866331965462 0.045142087727487956 -0.004269122394951391 0.08808405936719238 0.09244129580272178 -0.09244129580272115 -Cytoplasm_Texture_Contrast_DAPI_3_00_256 0.033444156977756045 2.120525977034049e-14 0.01067380684863425 -0.16411444488264215 -0.00015310571718855914 0.00015310571718774035 -Cytoplasm_Texture_Contrast_DAPI_3_01_256 0.04056962405599762 1.1102230246251565e-16 0.011683334549137241 -0.17916782548867072 0.0017609351505564752 -0.0017609351505573911 -Cytoplasm_Texture_Contrast_DAPI_3_02_256 0.03883606642421755 1.1102230246251565e-16 0.010064531256282658 -0.14608871305406396 0.0354908786275078 -0.035490878627508465 -Cytoplasm_Texture_Contrast_DAPI_3_03_256 0.03751838487547976 3.3306690738754696e-16 0.010416510844897075 -0.15466946407544913 0.022442767517185103 -0.022442767517185824 -Cytoplasm_Texture_Contrast_GFP_3_00_256 0.05565206358283081 1.1102230246251565e-16 0.008607973855269494 -0.10464350214208798 0.11392053444652989 -0.11392053444653016 -Cytoplasm_Texture_Contrast_GFP_3_01_256 0.050211795126206216 1.1102230246251565e-16 0.007313764321265079 -0.08231274505227366 0.12394792318369242 -0.12394792318369255 -Cytoplasm_Texture_Contrast_GFP_3_02_256 0.05590805423493572 1.1102230246251565e-16 0.008355489734514344 -0.09947074256636526 0.11923663001885967 -0.11923663001885992 -Cytoplasm_Texture_Contrast_GFP_3_03_256 0.05797491329517901 1.1102230246251565e-16 0.008783176692302497 -0.10675316326145146 0.11632238769107053 -0.11632238769107081 -Cytoplasm_Texture_Contrast_RFP_3_00_256 0.010809999049754992 0.00016594234095690386 0.005264906072550761 -0.0761300086281289 0.01976425107361475 -0.01976425107361509 -Cytoplasm_Texture_Contrast_RFP_3_01_256 0.010127524835217283 0.00031820367198787114 0.004139336146166355 -0.054013436849068695 0.039579910310615536 -0.03957991031061572 -Cytoplasm_Texture_Contrast_RFP_3_02_256 0.009486016554498544 0.0005845082313530314 0.0032303225432609517 -0.03679028062173191 0.05295608306676667 -0.05295608306676673 -Cytoplasm_Texture_Contrast_RFP_3_03_256 0.011363928833196701 9.755002624434184e-05 0.004675243837840316 -0.06311439605297153 0.036027801191574005 -0.03602780119157425 -Cytoplasm_Texture_Correlation_DAPI_3_00_256 0.0035656436514082124 0.11778877754341299 -0.002480885460697431 0.03255039928347693 -0.02299023829785913 0.022990238297859244 -Cytoplasm_Texture_Correlation_DAPI_3_01_256 0.0011767970045039045 0.6575761142527337 -0.0003680393610205989 -0.0017963477435139277 -0.030679556184055358 0.030679556184055278 -Cytoplasm_Texture_Correlation_DAPI_3_02_256 0.0024564903995981657 0.2800381376694643 -0.0009588498215833472 0.0054540275038673855 -0.038218051998942335 0.03821805199894227 -Cytoplasm_Texture_Correlation_DAPI_3_03_256 0.004318966145503533 0.06300189184640037 -0.002046921696407562 0.02221314317544255 -0.03808087981471184 0.038080879814711865 -Cytoplasm_Texture_Correlation_GFP_3_00_256 0.004590268257756658 0.05001669227679906 -0.003990541041639835 0.06159178506993626 0.0010264328994650518 -0.0010264328994647395 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0.9989190347815181 0.00019973125440887144 -0.002070845077488131 0.00411353342193947 -0.004113533421939472 -Nuclei_AreaShape_Zernike_5_5 0.019830958821057676 2.3270236848560444e-08 0.0001337996109911715 0.032787439269349405 0.14341679947314426 -0.14341679947314376 -Nuclei_AreaShape_Zernike_6_0 0.0007471168274580942 0.8193316008486865 0.0013362396311054825 -0.02965988210938944 -0.03753441285281983 0.037534412852819596 -Nuclei_AreaShape_Zernike_6_2 0.029734083066550787 9.798828415341632e-13 -0.0011754957513480176 -0.021560030517760315 -0.16311362912230126 0.1631136291223008 -Nuclei_AreaShape_Zernike_6_4 0.0035536520460041077 0.11894335465255068 0.0007439301434207677 -0.02826882695079487 -0.06928438926714381 0.0692843892671435 -Nuclei_AreaShape_Zernike_6_6 2.50764301990003e-05 0.9996714873598254 -0.0003897786776107449 0.007142028724260379 0.0047348575496101285 -0.004734857549610082 -Nuclei_AreaShape_Zernike_7_1 0.003519921282360894 0.12224735474581283 6.924406406573475e-05 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5.455358387251863e-11 0.0031595675884058196 -0.01865764072562834 0.1231123514055964 -0.1231123514055962 -Nuclei_Correlation_Correlation_DAPI_GFP 0.005597584936572808 0.020811189718118017 -0.0027688599181534103 0.06689736078036237 0.10015977946118539 -0.10015977946118482 -Nuclei_Correlation_Correlation_DAPI_RFP 0.01684321135815714 4.5875122578475924e-07 0.00014205156282761847 -0.03504904594788899 -0.13527231508033608 0.13527231508033558 -Nuclei_Correlation_Correlation_GFP_RFP 0.03244978474841076 5.939693181744587e-14 0.0012480577727552586 0.022152138374648023 0.17014171885326856 -0.17014171885326806 -Nuclei_Correlation_Costes_DAPI_GFP 0.09173667996128732 1.1102230246251565e-16 -0.014885198848901255 0.3298836164767035 0.415994629305783 -0.41599462930578046 -Nuclei_Correlation_Costes_DAPI_RFP 0.06537225212561171 1.1102230246251565e-16 -0.015553756747073814 0.32322078607538285 0.3462710611474306 -0.34627106114742817 -Nuclei_Correlation_Costes_GFP_RFP 0.0027040154606116085 0.23250973868569336 -0.001439092468555444 0.03836944046694378 0.06687496308107685 -0.0668749630810765 -Nuclei_Correlation_Costes_RFP_DAPI 0.017914963514199522 1.580573694148768e-07 -0.006922633136111468 0.15111623459086815 0.18399033989712438 -0.18399033989712318 -Nuclei_Correlation_K_DAPI_GFP 0.016042896725708022 1.0133859220662345e-06 -0.0052536283477645105 0.1225024387479125 0.1718156460245471 -0.17181564602454608 -Nuclei_Correlation_K_DAPI_RFP 0.019441454867166308 3.4387600544860675e-08 -0.0011421086465465947 -0.013884442015368537 -0.12940892635109447 0.12940892635109408 -Nuclei_Correlation_K_GFP_DAPI 0.02853871792349083 3.3494318429916348e-12 0.00735535031040551 -0.1690029733733402 -0.23023310589244492 0.23023310589244353 -Nuclei_Correlation_K_GFP_RFP 0.05040048534892727 1.1102230246251565e-16 0.0043533599195334045 -0.1336791624394929 -0.27477849813696786 0.2747784981369666 -Nuclei_Correlation_K_RFP_DAPI 0.009430603278008487 0.0006159131487293301 -0.002583265883233282 0.06988113874850571 0.12418339808002987 -0.12418339808002923 -Nuclei_Correlation_K_RFP_GFP 0.03224566862605527 7.338574192772285e-14 -0.0056629689720058335 0.1442133254364107 0.23527742975558208 -0.2352774297555808 -Nuclei_Correlation_Manders_DAPI_GFP 0.005351200880022611 0.025856256193626193 0.0030718253235459073 -0.040536345719937306 0.027509351130101313 -0.02750935113010146 -Nuclei_Correlation_Manders_DAPI_RFP 0.0003548282703975669 0.9473520293555765 0.0012507589608186007 -0.02524297737417154 -0.024763048399490204 0.02476304839949002 -Nuclei_Correlation_Manders_GFP_DAPI 0.0007620641436754116 0.8138200273288702 -0.0012366792775270917 0.02759340277142216 0.0353280921836848 -0.03532809218368459 -Nuclei_Correlation_Manders_GFP_RFP 0.00015630051627069097 0.9883283746167367 0.0009610455497743142 -0.018006054342069863 -0.013306471391877578 0.013306471391877454 -Nuclei_Correlation_Manders_RFP_DAPI 0.0006703475639939782 0.8471927376411222 -0.0010841604293917255 0.02468159081864272 0.03299311870233575 -0.03299311870233554 -Nuclei_Correlation_Manders_RFP_GFP 0.005751006520582913 0.018166477203218068 0.0028870574000985994 -0.03603297382321839 0.03435469487790527 -0.03435469487790538 -Nuclei_Correlation_Overlap_DAPI_GFP 0.018459160769271343 9.185241267584132e-08 -0.00443332948399178 0.049885068339613335 -0.07517303529196966 0.07517303529196974 -Nuclei_Correlation_Overlap_DAPI_RFP 0.03332433089660414 2.398081733190338e-14 -0.005960273208160103 0.06709695127398127 -0.10093976532691251 0.10093976532691264 -Nuclei_Correlation_Overlap_GFP_RFP 0.00073429097986355 0.8240407973909194 -0.0015076284699692322 0.032202164809197985 0.03715451813273144 -0.03715451813273119 -Nuclei_Correlation_RWC_DAPI_GFP 0.018989196256275598 5.4080493305797006e-08 -0.0038782622974246196 0.10277732741182427 0.17764773207388912 -0.17764773207388818 -Nuclei_Correlation_RWC_DAPI_RFP 0.009434583039872435 0.0006136031128202157 -0.006499702588718633 0.10594837765705775 0.024840801513932337 -0.024840801513931754 -Nuclei_Correlation_RWC_GFP_DAPI 0.023339770229140133 6.753302361772739e-10 -0.003969839060068012 0.10833010988950581 0.1947086060182426 -0.1947086060182416 -Nuclei_Correlation_RWC_GFP_RFP 0.004164282860392388 0.07177703368616084 0.0020737447585981008 -0.05247387074328527 -0.08477350111511209 0.08477350111511163 -Nuclei_Correlation_RWC_RFP_DAPI 0.007795913033751156 0.0028392756370917427 -0.006734540440612779 0.1181910975788669 0.060372943591185446 -0.06037294359118471 -Nuclei_Correlation_RWC_RFP_GFP 0.0017896074019385644 0.44907556007311955 0.001771699968436085 -0.04119039602238776 -0.057441749082049326 0.057441749082048986 -Nuclei_Granularity_10_DAPI 0.03668829423261344 7.771561172376096e-16 -0.007848174440735997 0.10219245332996524 -0.07593632825175248 0.07593632825175284 -Nuclei_Granularity_10_GFP 0.003949145384552932 0.08591554937566015 -0.0028548844585144536 0.06476519799142932 0.08594113395401158 -0.08594113395401105 -Nuclei_Granularity_10_RFP 0.0018661188425411757 0.4264716484881157 0.0028639162544017364 -0.046490115866486374 -0.010150465336355344 0.01015046533635509 -Nuclei_Granularity_11_DAPI 0.014629219072743727 4.079738795015331e-06 -0.0044510786960680225 0.054402810263315574 -0.05770125420443894 0.05770125420443909 -Nuclei_Granularity_11_GFP 0.000562777541660453 0.8845137613147607 0.000489301043873812 -0.00318927900802609 0.017831231764757868 -0.017831231764757843 -Nuclei_Granularity_11_RFP 0.005601917417225266 0.020731628165466476 0.0023808982989989517 -0.026273818364219128 0.04249809972792952 -0.04249809972792956 -Nuclei_Granularity_12_DAPI 0.013958640425660085 7.87069940200169e-06 -0.004934532232575382 0.06492807077545801 -0.04496801996733666 0.04496801996733689 -Nuclei_Granularity_12_GFP 0.0020619278379977457 0.37245663764842774 0.0016479988294947291 -0.01996498357975364 0.022094289250755503 -0.02209428925075555 -Nuclei_Granularity_12_RFP 0.007634120014861412 0.0032967736864077946 -0.006094006978899385 0.12071717877030566 0.11129677210627444 -0.11129677210627356 -Nuclei_Granularity_13_DAPI 0.005521892653970961 0.022249876312908645 -0.00384454084415529 0.056126829422292746 -0.012229574337466943 0.012229574337467203 -Nuclei_Granularity_13_GFP 0.009742962256615528 0.00045837783174373925 -0.004799820279587066 0.10670736969818576 0.13551649984596947 -0.13551649984596864 -Nuclei_Granularity_13_RFP 0.012360202993951352 3.730234644860886e-05 0.00014286837896644263 0.02520510850973723 0.11278212785858613 -0.11278212785858574 -Nuclei_Granularity_14_DAPI 0.004806886792936393 0.0415249084402608 -0.0038128907342353576 0.05716419704399471 -0.005957320934663467 0.005957320934663744 -Nuclei_Granularity_14_GFP 0.001614847416495535 0.5037984675510914 -0.0013564537083510827 0.03376451321867903 0.05314990043250408 -0.0531499004325038 -Nuclei_Granularity_14_RFP 0.007009569639603308 0.005847051847339579 0.004443501875467801 -0.06560264886743451 0.011123952531007057 -0.011123952531007363 -Nuclei_Granularity_15_DAPI 0.0032526783758627387 0.15156524019880768 -0.0043385790376858375 0.07596126334452565 0.038150009775234764 -0.03815000977523429 -Nuclei_Granularity_15_GFP 0.0017140693020247788 0.47220796786015895 -0.002107203000790418 0.04621797206145567 0.056907662153437163 -0.0569076621534368 -Nuclei_Granularity_15_RFP 0.026940563786350347 1.7236212457305555e-11 -0.010847913166710122 0.21929660202160234 0.2162640838240863 -0.21626408382408466 -Nuclei_Granularity_16_DAPI 0.0027265171841915903 0.228557121652329 0.0038431383801131383 -0.06572439431939497 -0.02736220531452524 0.027362205314524837 -Nuclei_Granularity_16_GFP 0.015948990208157654 1.11193974250412e-06 -0.008908762271943848 0.14886830595906236 0.0490753947490683 -0.04907539474906743 -Nuclei_Granularity_16_RFP 0.0046561730076774 0.04727155614209422 -0.001404465102767419 0.04204230328829017 0.08418314005903807 -0.08418314005903767 -Nuclei_Granularity_1_DAPI 0.020977225940932653 7.352081721201387e-09 -0.0061993898282064475 0.08264202711359045 -0.052086027277638045 0.05208602727763834 -Nuclei_Granularity_1_GFP 0.0963245352604084 1.1102230246251565e-16 0.001185584801777664 -0.09913740068443005 -0.3330319782704106 0.3330319782704093 -Nuclei_Granularity_1_RFP 0.06840416553554762 1.1102230246251565e-16 0.010834064780548755 -0.25272625444244134 -0.35473487068954035 0.35473487068953824 -Nuclei_Granularity_2_DAPI 0.0037835117576389132 0.09854018866650816 -0.0014110090285422699 0.04040328729525307 0.07702300614062783 -0.07702300614062745 -Nuclei_Granularity_2_GFP 0.0010490319408842375 0.705581420050845 -3.86207481008494e-05 -0.007397748915126498 -0.0328922635683254 0.03289226356832529 -Nuclei_Granularity_2_RFP 0.007522411395353146 0.0036541793301019254 0.00037556581625907613 -0.028502806073779807 -0.09355388337370936 0.09355388337370901 -Nuclei_Granularity_3_DAPI 0.0036630142691364 0.10879087009653199 -0.0009752859441673324 0.03257533606741404 0.07237183690973933 -0.072371836909739 -Nuclei_Granularity_3_GFP 0.006126974751988867 0.012990982979996457 0.002565285446919489 -0.028959338073299697 0.04311084320566155 -0.04311084320566161 -Nuclei_Granularity_3_RFP 0.0029009262432447835 0.19986623753347843 0.003176551633888478 -0.06665458926080932 -0.07336584840371246 0.07336584840371195 -Nuclei_Granularity_4_DAPI 0.018279734952277638 1.0986626330922178e-07 0.00529819244598286 -0.06726882096263012 0.05834244105805221 -0.05834244105805242 -Nuclei_Granularity_4_GFP 0.014179338240160488 6.341703263856857e-06 0.003466277019498142 -0.03550343984495903 0.07318151250084318 -0.07318151250084319 -Nuclei_Granularity_4_RFP 0.0011790080782028145 0.6567533567742909 0.0026742215109904883 -0.04868499426149436 -0.031186469250526684 0.03118646925052636 -Nuclei_Granularity_5_DAPI 0.042516689939087304 1.1102230246251565e-16 0.005991619695453615 -0.061270739242511404 0.12690342688119144 -0.12690342688119147 -Nuclei_Granularity_5_GFP 0.023445687585669228 6.065499214003012e-10 0.002024653867957543 0.0008717478859589511 0.13168812518772446 -0.13168812518772416 -Nuclei_Granularity_5_RFP 0.002872508472941071 0.2043123553880668 0.004143724921592989 -0.0738538634841022 -0.04180450869043457 0.04180450869043409 -Nuclei_Granularity_6_DAPI 0.043799487731673636 1.1102230246251565e-16 0.0013639773240873428 0.027326585559566963 0.19877369888311824 -0.19877369888311766 -Nuclei_Granularity_6_GFP 0.03363937832536146 1.7319479184152442e-14 0.0026079105487608026 -0.002389021545254 0.1551696891743262 -0.15516968917432583 -Nuclei_Granularity_6_RFP 0.005177975933156831 0.0300908502968682 0.004360300195772766 -0.06810653630644306 -0.004446088364267062 0.004446088364266701 -Nuclei_Granularity_7_DAPI 0.0024421870398085854 0.28302040904447945 0.0024586282171989667 -0.03526832546405129 0.010395186563187928 -0.010395186563188087 -Nuclei_Granularity_7_GFP 0.031973775108091185 9.71445146547012e-14 0.0017541313298017874 0.012425608383020398 0.1621271862390977 -0.16212718623909728 -Nuclei_Granularity_7_RFP 0.002462688857777451 0.27875400057173083 0.0003884209953611028 0.00527667235562812 0.046293927656656256 -0.046293927656656124 -Nuclei_Granularity_8_DAPI 0.0413460504214036 1.1102230246251565e-16 -0.0011290916728731184 -0.030171380935634762 -0.19562147971534458 0.19562147971534397 -Nuclei_Granularity_8_GFP 0.02779813709890011 7.161160553437185e-12 -0.0005924556900318116 0.05245429902035775 0.17841425722185678 -0.1784142572218561 -Nuclei_Granularity_8_RFP 0.0041232129778308035 0.07429417112491299 0.0009100304628709582 -0.0007799216834936534 0.05436760684176066 -0.05436760684176054 -Nuclei_Granularity_9_DAPI 0.05589152332869196 1.1102230246251565e-16 -0.008073524251051489 0.0938935857458253 -0.124351913394571 0.1243519133945712 -Nuclei_Granularity_9_GFP 0.011553879582017368 8.125845363116113e-05 -0.0009142568325005007 0.043379245699794594 0.12070144883383602 -0.12070144883383553 -Nuclei_Granularity_9_RFP 0.005041851731814373 0.03388013992363925 0.0003900724356318687 0.01061357343525507 0.06836480374138948 -0.06836480374138926 -Nuclei_Intensity_IntegratedIntensityEdge_DAPI 0.02285422656220326 1.1045678816046234e-09 0.000489292523343947 -0.04669587733828304 -0.16124005668002597 0.16124005668002533 -Nuclei_Intensity_IntegratedIntensityEdge_GFP 0.0023987109902465775 0.2922495432543798 -0.003462057819662277 0.06819814735243711 0.06165504218963428 -0.061655042189633795 -Nuclei_Intensity_IntegratedIntensityEdge_RFP 0.010809104029136152 0.00016608451804844826 0.002731464282691316 -0.0742316998315592 -0.1327135385381045 0.1327135385381038 -Nuclei_Intensity_IntegratedIntensity_DAPI 0.04756932996739882 1.1102230246251565e-16 0.00018014970985098108 -0.05783710980842016 -0.22665625024293798 0.22665625024293717 -Nuclei_Intensity_IntegratedIntensity_GFP 0.013355539492214907 1.4182535563400656e-05 -0.006675040584157329 0.10218098592004961 -0.0017592273049611973 0.0017592273049617108 -Nuclei_Intensity_IntegratedIntensity_RFP 0.040309631815170666 1.1102230246251565e-16 -0.00039629609845409056 -0.042989667228236494 -0.2020167846076981 0.2020167846076974 -Nuclei_Intensity_LowerQuartileIntensity_DAPI 0.052524667852078655 1.1102230246251565e-16 0.013269616411657534 -0.20333810331926078 0.0026416272827719522 -0.002641627282773007 -Nuclei_Intensity_LowerQuartileIntensity_GFP 0.022775987316501878 1.1956294843074033e-09 0.003579806624708124 -0.029241019776979946 0.106140316528097 -0.1061403165280969 -Nuclei_Intensity_LowerQuartileIntensity_RFP 0.0058323511769345915 0.016899662813815164 0.00555035674617288 -0.10795651527426192 -0.09317139416509788 0.09317139416509712 -Nuclei_Intensity_MADIntensity_DAPI 0.024679779568023585 1.7312395961255334e-10 -0.0013814116132072987 0.0642069498510372 0.1768701945059239 -0.17687019450592317 -Nuclei_Intensity_MADIntensity_GFP 0.03622772971604127 1.2212453270876722e-15 0.004202126387888813 -0.030874272162024707 0.1387921757987666 -0.13879217579876643 -Nuclei_Intensity_MADIntensity_RFP 0.006173503167149708 0.012460274935090498 0.00605315901390023 -0.10736394812939844 -0.05892006924559945 0.05892006924559877 -Nuclei_Intensity_MassDisplacement_DAPI 0.013632931840414586 1.0820324182203578e-05 -0.008024636310274398 0.13241465453508822 0.03729181751934489 -0.03729181751934413 -Nuclei_Intensity_MassDisplacement_GFP 0.009674151794348163 0.0004892406931670257 -0.003433749862574461 0.08453340912588328 0.130681122375395 -0.1306811223753943 -Nuclei_Intensity_MassDisplacement_RFP 0.00038622146864686346 0.9389110031470413 0.0010518970947955217 -0.015587222195689742 0.0023974943979551654 -0.0023974943979552382 -Nuclei_Intensity_MaxIntensityEdge_DAPI 0.12175565251372122 1.1102230246251565e-16 0.016524499211421254 -0.23166722651266358 0.09197729571265716 -0.09197729571265814 -Nuclei_Intensity_MaxIntensityEdge_GFP 0.029188568371166235 1.7177370637000422e-12 0.00119903440623238 0.020725478348774556 0.16116795173558857 -0.1611679517355881 -Nuclei_Intensity_MaxIntensityEdge_RFP 0.005831210238853024 0.016916823028616745 0.005797069997396208 -0.10131675593968195 -0.050232939585836855 0.05023293958583623 -Nuclei_Intensity_MaxIntensity_DAPI 0.06886184264199835 1.1102230246251565e-16 0.012240635737565543 -0.17037321788895693 0.07321955669355541 -0.07321955669355609 -Nuclei_Intensity_MaxIntensity_GFP 0.01920179559198265 4.371626105559301e-08 0.0006578234425281075 0.02261815333293027 0.13471562362929493 -0.1347156236292945 -Nuclei_Intensity_MaxIntensity_RFP 0.004707590152641883 0.04523046766315053 0.005342517057391556 -0.0969477203429238 -0.06100984244806136 0.061009842448060715 -Nuclei_Intensity_MeanIntensityEdge_DAPI 0.13707120194929334 1.1102230246251565e-16 0.015425792704846371 -0.20270615400869024 0.14166419223139518 -0.1416641922313959 -Nuclei_Intensity_MeanIntensityEdge_GFP 0.02427477708250403 2.613653737881805e-10 0.002829115935327455 -0.013626733279023279 0.12291157765399659 -0.12291157765399638 -Nuclei_Intensity_MeanIntensityEdge_RFP 0.006907988581060565 0.006414359748106335 0.006427445579594028 -0.11459772740105469 -0.06501275448692648 0.06501275448692573 -Nuclei_Intensity_MeanIntensity_DAPI 0.07711332944498783 1.1102230246251565e-16 0.014459456536191774 -0.2116948017169039 0.04352722693710874 -0.04352722693710971 -Nuclei_Intensity_MeanIntensity_GFP 0.030928895793460365 2.859934511434403e-13 0.003869366992560177 -0.028272204903162707 0.12844841925225015 -0.12844841925225 -Nuclei_Intensity_MeanIntensity_RFP 0.006257578795469176 0.011554372155310855 0.006049364847860872 -0.11392626473247724 -0.08617025824236506 0.08617025824236427 -Nuclei_Intensity_MedianIntensity_DAPI 0.07105310980218726 1.1102230246251565e-16 0.014588776803552299 -0.21826795777344357 0.02465458728855932 -0.024654587288560376 -Nuclei_Intensity_MedianIntensity_GFP 0.028361051778871937 4.019673482957842e-12 0.0038922115938343285 -0.03065979694848328 0.12006660289770153 -0.12006660289770141 -Nuclei_Intensity_MedianIntensity_RFP 0.00601089671480326 0.014412761446894407 0.005781291741259216 -0.11089302312383201 -0.0906466168801702 0.09064661688016942 -Nuclei_Intensity_MinIntensityEdge_DAPI 0.15134865093194627 1.1102230246251565e-16 0.013515622044036087 -0.15906325087319756 0.200439269775729 -0.20043926977572935 -Nuclei_Intensity_MinIntensityEdge_GFP 0.0057440858454768096 0.018278413796128046 0.0024620572526771207 -0.027610915663210667 0.042129616667843345 -0.042129616667843386 -Nuclei_Intensity_MinIntensityEdge_RFP 0.007271445064432003 0.004601690573257988 0.006639904317373302 -0.1205023463950593 -0.07587355300626103 0.07587355300626024 -Nuclei_Intensity_MinIntensity_DAPI 0.14448932805176729 1.1102230246251565e-16 0.012691417901939657 -0.1452448744447049 0.2051674607726891 -0.20516746077268938 -Nuclei_Intensity_MinIntensity_GFP 0.011312627896791883 0.00010248017426073286 0.002673299090162532 -0.023506507101427446 0.07238842786516403 -0.07238842786516397 -Nuclei_Intensity_MinIntensity_RFP 0.008864064231874469 0.0010497474258066575 0.007331411927792399 -0.1339484649628522 -0.0874652745173281 0.08746527451732722 -Nuclei_Intensity_StdIntensityEdge_DAPI 0.019002030097055256 5.3390614818127347e-08 0.010726017441074265 -0.19683747777669125 -0.13153592734940112 0.1315359273493998 -Nuclei_Intensity_StdIntensityEdge_GFP 0.039432524316675654 1.1102230246251565e-16 0.0011169420981318686 0.029201410405197414 0.19086042988093638 -0.19086042988093582 -Nuclei_Intensity_StdIntensityEdge_RFP 0.007569144517153847 0.0035002502930955304 0.00587721670717621 -0.09621220458213293 -0.024151998287328738 0.024151998287328176 -Nuclei_Intensity_StdIntensity_DAPI 0.06975368280010819 1.1102230246251565e-16 0.01266290135377602 -0.17856747880788598 0.06620922166067006 -0.06620922166067082 -Nuclei_Intensity_StdIntensity_GFP 0.03571462397649594 1.9984014443252818e-15 0.0032422499416768667 -0.012938477487732719 0.15188343159201018 -0.1518834315920099 -Nuclei_Intensity_StdIntensity_RFP 0.006846889652472932 0.006781187064673677 0.006113728558955076 -0.10480453391363741 -0.044553420706541155 0.04455342070654052 -Nuclei_Intensity_UpperQuartileIntensity_DAPI 0.07633465132606643 1.1102230246251565e-16 0.014152427251305081 -0.20569885409544558 0.04878044075431612 -0.04878044075431706 -Nuclei_Intensity_UpperQuartileIntensity_GFP 0.03508900209042787 3.885780586188048e-15 0.004119038713541556 -0.030068662245291394 0.1368510558712558 -0.13685105587125565 -Nuclei_Intensity_UpperQuartileIntensity_RFP 0.006316113244675736 0.010961928602744697 0.006108410573041978 -0.11446377095406532 -0.08464677495793299 0.08464677495793221 -Nuclei_Location_CenterMassIntensity_X_DAPI 0.0008068595573830217 0.7971709430418058 0.0016819110405485616 -0.026017017438201364 -0.0006699178584394679 0.0006699178584393325 -Nuclei_Location_CenterMassIntensity_X_GFP 0.0008114697294212547 0.795447719046481 0.0016900938941754999 -0.0261656383322758 -0.0007639032344557879 0.0007639032344556526 -Nuclei_Location_CenterMassIntensity_X_RFP 0.0007999812744119428 0.7997388556580222 0.001675815281872552 -0.02592980615899035 -0.0006966403434338526 0.0006966403434337173 -Nuclei_Location_CenterMassIntensity_Y_DAPI 0.000662712343281413 0.8499157357423742 0.0019353611648498757 -0.03693092836544124 -0.02955515530697972 0.029555155306979466 -Nuclei_Location_CenterMassIntensity_Y_GFP 0.0006535966480188948 0.8531536843560859 0.0019190796297447227 -0.03665712552021452 -0.02945833236544155 0.029458332365441296 -Nuclei_Location_CenterMassIntensity_Y_RFP 0.0006633711879441284 0.8496811547906771 0.001940384925904876 -0.03697427814755689 -0.029415726729081576 0.029415726729081316 -Nuclei_Location_Center_X 0.000806949692387926 0.7971372674489196 0.0016800538744685689 -0.02597561060698395 -0.0006169927237823121 0.0006169927237821803 -Nuclei_Location_Center_Y 0.0006668564579445091 0.8484390056290597 0.0019437938319414301 -0.03706112003447244 -0.02955748128963295 0.029557481289632696 -Nuclei_Location_MaxIntensity_X_DAPI 0.0008768983972842204 0.7708431263607122 0.0018510127375679598 -0.02929396743762383 -0.003458580758535921 0.0034585807585357647 -Nuclei_Location_MaxIntensity_X_GFP 0.0009143033165371905 0.7566877651088701 0.0019497869393522854 -0.03126988703684144 -0.005341902463102344 0.00534190246310217 -Nuclei_Location_MaxIntensity_X_RFP 0.0007216271563180721 0.8286708975763397 0.001706728338383255 -0.027198339109296903 -0.003961979440464145 0.003961979440463996 -Nuclei_Location_MaxIntensity_Y_DAPI 0.0005778790943300471 0.879425338631542 0.0017422035479664416 -0.033932955262298316 -0.029436766394667795 0.02943676639466756 -Nuclei_Location_MaxIntensity_Y_GFP 0.0003456254824234506 0.949740735364546 0.001256741422899757 -0.025198838116360582 -0.02420286266552073 0.02420286266552055 -Nuclei_Location_MaxIntensity_Y_RFP 0.000696546131200404 0.8377782331523768 0.0019804255279468603 -0.03783770920082894 -0.030436188104585687 0.03043618810458542 -Nuclei_Neighbors_AngleBetweenNeighbors_Adjacent 0.004366045128729623 0.06053971349451748 0.0007011839528236036 -0.029145678112999993 -0.07559801598153414 0.0755980159815338 -Nuclei_Neighbors_FirstClosestDistance_Adjacent 0.08563968603294037 1.1102230246251565e-16 -0.014074114546091024 0.19875390077620902 -0.07241063106704326 0.07241063106704411 -Nuclei_Neighbors_FirstClosestObjectNumber_Adjacent 0.10045324696838065 1.1102230246251565e-16 0.020620776435548136 -0.33198929436262975 -0.06177089706469474 0.06177089706469285 -Nuclei_Neighbors_SecondClosestDistance_Adjacent 0.09941022802143107 1.1102230246251565e-16 -0.017668250992116896 0.2670204041885691 -0.01883086744058529 0.018830867440586624 -Nuclei_Neighbors_SecondClosestObjectNumber_Adjacent 0.08981250452975298 1.1102230246251565e-16 0.0189299877442896 -0.3009199271091334 -0.04086747704011506 0.040867477040113426 -Nuclei_RadialDistribution_FracAtD_DAPI_1of4 0.0522994757905505 1.1102230246251565e-16 0.0021772339110574324 0.01712402036502536 0.20823533374036926 -0.2082353337403687 -Nuclei_RadialDistribution_FracAtD_DAPI_2of4 0.04837897794760693 1.1102230246251565e-16 0.00391099001863293 -0.01766328000010689 0.1747476597904073 -0.174747659790407 -Nuclei_RadialDistribution_FracAtD_DAPI_3of4 0.026133657116026332 3.932509873294521e-11 0.007233764239697107 -0.0986513562354406 0.05163749096262592 -0.051637490962626306 -Nuclei_RadialDistribution_FracAtD_DAPI_4of4 0.05166417074378016 1.1102230246251565e-16 -0.005791859926054014 0.051758641992258714 -0.15341587116248168 0.15341587116248162 -Nuclei_RadialDistribution_FracAtD_GFP_1of4 0.00014853808637238508 0.9894033252135216 -0.0009087611678098307 0.017431866154136912 0.014251182851138529 -0.01425118285113841 -Nuclei_RadialDistribution_FracAtD_GFP_2of4 0.0012889729096122737 0.6163065720835541 -0.0012398363952466525 0.014531492272814082 -0.01863375922915501 0.018633759229155042 -Nuclei_RadialDistribution_FracAtD_GFP_3of4 0.0048969779693816 0.03841605736474474 -0.002389904140211561 0.02779531696779855 -0.03680549623847697 0.03680549623847703 -Nuclei_RadialDistribution_FracAtD_GFP_4of4 0.0029431137791315898 0.1934240928637656 0.002123906297029954 -0.026951454679861882 0.02344907569430028 -0.023449075694300367 -Nuclei_RadialDistribution_FracAtD_RFP_1of4 0.0004276207559165135 0.9271370528773311 -0.0010644262275206027 0.023268630054728158 0.028426035436166598 -0.028426035436166414 -Nuclei_RadialDistribution_FracAtD_RFP_2of4 0.0023931268651403714 0.29345299622635035 -0.0011208199263182158 0.008717870600233622 -0.03503211980616784 0.03503211980616781 -Nuclei_RadialDistribution_FracAtD_RFP_3of4 0.019442279291813302 3.43592072571397e-08 -0.0015838440823451466 -0.005660248275456609 -0.12350724697930443 0.1235072469793041 -Nuclei_RadialDistribution_FracAtD_RFP_4of4 0.007841384840180377 0.0027223478985251015 0.0015353130780121392 -0.0063674660720135084 0.070931279162484 -0.07093127916248387 -Nuclei_RadialDistribution_MeanFrac_DAPI_1of4 0.04053315067551522 1.1102230246251565e-16 -5.475484534719644e-05 0.05135961716762372 0.20792899527193734 -0.2079289952719366 -Nuclei_RadialDistribution_MeanFrac_DAPI_2of4 0.056582041028439556 1.1102230246251565e-16 0.0032911159041788657 -0.0013846081002986677 0.2025302726325578 -0.20253027263255735 -Nuclei_RadialDistribution_MeanFrac_DAPI_3of4 0.04820943898249619 1.1102230246251565e-16 0.00725993058988924 -0.0824970538368035 0.1197832526938359 -0.11978325269383605 -Nuclei_RadialDistribution_MeanFrac_DAPI_4of4 0.0702530332864928 1.1102230246251565e-16 -0.005879150163751186 0.04352041631811096 -0.19284681415504484 0.19284681415504462 -Nuclei_RadialDistribution_MeanFrac_GFP_1of4 0.007983290577341373 0.002387078776973861 -0.0027751779644353556 0.03004825076312323 -0.05190886012162178 0.05190886012162181 -Nuclei_RadialDistribution_MeanFrac_GFP_2of4 0.00900370074078205 0.0009207707953626798 -0.0026698058798551613 0.026492393514166768 -0.05987766897083224 0.05987766897083224 -Nuclei_RadialDistribution_MeanFrac_GFP_3of4 0.004996039809264663 0.03525570587813098 -0.0035679489674032847 0.05151885615648086 -0.013695740526055336 0.013695740526055565 -Nuclei_RadialDistribution_MeanFrac_GFP_4of4 0.012474621817970877 3.338850391965131e-05 0.004079400564910662 -0.04962232431144907 0.053861355926170515 -0.053861355926170654 -Nuclei_RadialDistribution_MeanFrac_RFP_1of4 0.008851841402379712 0.0010618515752998103 -0.003526273203946223 0.043593788938095485 -0.04367804845519036 0.04367804845519049 -Nuclei_RadialDistribution_MeanFrac_RFP_2of4 0.015796625787496965 1.2925329841984023e-06 -0.0033113116489351577 0.030722833622410378 -0.08305354797721215 0.08305354797721212 -Nuclei_RadialDistribution_MeanFrac_RFP_3of4 0.021551289094483228 4.1221652757883476e-09 -0.0030586685671412394 0.02032337561444736 -0.10987238736293548 0.10987238736293534 -Nuclei_RadialDistribution_MeanFrac_RFP_4of4 0.0278175468435633 7.02016222930979e-12 0.0040448579610968295 -0.03402223144119747 0.11588496014543241 -0.11588496014543231 -Nuclei_RadialDistribution_RadialCV_DAPI_1of4 0.009229560046859042 0.0007445006418670408 -0.007467633770817581 0.13450918712282353 0.08115440580916787 -0.081154405809167 -Nuclei_RadialDistribution_RadialCV_DAPI_2of4 0.013225992218184879 1.6090418980541088e-05 -0.00878869129246679 0.16561809240968825 0.125613491446237 -0.12561349144623585 -Nuclei_RadialDistribution_RadialCV_DAPI_3of4 0.008345386916710162 0.0017048944653894615 -0.0062304620530357875 0.12451067779203895 0.11827707737480715 -0.11827707737480625 -Nuclei_RadialDistribution_RadialCV_DAPI_4of4 0.0007884453301472139 0.8040368425507897 0.00045401548863186495 -0.0013980272396540289 0.022971404123038025 -0.022971404123037976 -Nuclei_RadialDistribution_RadialCV_GFP_1of4 0.0006118120305967922 0.8677986896688914 -0.0014574214007185505 0.0305905002990967 0.03369760271525593 -0.033697602715255695 -Nuclei_RadialDistribution_RadialCV_GFP_2of4 0.005267233948774264 0.027831565399127123 0.002003808852317587 -0.01951988811605787 0.046438414393055134 -0.04643841439305513 -Nuclei_RadialDistribution_RadialCV_GFP_3of4 0.0282967770263175 4.2938985700402554e-12 0.0018719405801501346 0.007553892562950276 0.14952927714452216 -0.1495292771445218 -Nuclei_RadialDistribution_RadialCV_GFP_4of4 0.027290465797175534 1.2047363107114961e-11 0.0007948371717584971 0.026769188053415577 0.1604699254441983 -0.1604699254441978 -Nuclei_RadialDistribution_RadialCV_RFP_1of4 0.0014037629264905815 0.5752769028994948 0.0018048142488950709 -0.04023602585151663 -0.051418470369395106 0.051418470369394786 -Nuclei_RadialDistribution_RadialCV_RFP_2of4 0.007500230954604725 0.0037295454986621124 0.003267676170963066 -0.040562759782270225 0.039792135639859144 -0.03979213563985926 -Nuclei_RadialDistribution_RadialCV_RFP_3of4 0.013378051905929889 1.387471595670764e-05 0.0041404259642876425 -0.04971422735735027 0.05734417139509041 -0.057344171395090536 -Nuclei_RadialDistribution_RadialCV_RFP_4of4 0.01433226901246032 5.459280971731317e-06 0.0031978788893257965 -0.030112504130760276 0.07838872795851398 -0.07838872795851397 -Nuclei_Texture_AngularSecondMoment_DAPI_3_00_256 0.031216071917666843 2.1260770921571748e-13 0.011406641327354325 -0.18303081092465373 -0.031645307669845024 0.031645307669844025 -Nuclei_Texture_AngularSecondMoment_DAPI_3_01_256 0.029142400948170333 1.801225835151854e-12 0.01095306184657588 -0.17528339706498397 -0.02845540182941364 0.028455401829412696 -Nuclei_Texture_AngularSecondMoment_DAPI_3_02_256 0.032075922313531735 8.737455203799982e-14 0.011586662735325477 -0.18608600116612542 -0.032830328448372 0.032830328448370985 -Nuclei_Texture_AngularSecondMoment_DAPI_3_03_256 0.03158948057401678 1.4455103780619538e-13 0.011490364806496374 -0.1844831808612821 -0.03232598873194173 0.032325988731940716 -Nuclei_Texture_AngularSecondMoment_GFP_3_00_256 1.70760208327847e-05 0.9998468297502371 6.129528612688106e-05 -0.00010159748373679382 0.0034599894621718547 -0.003459989462171847 -Nuclei_Texture_AngularSecondMoment_GFP_3_01_256 3.2158432515916324e-05 0.9994623438537784 0.00029590743286354247 -0.0043363098653460596 0.0008740825516439375 -0.0008740825516439575 -Nuclei_Texture_AngularSecondMoment_GFP_3_02_256 3.2451022452528733e-06 0.9999944155955318 0.00010760574908977484 -0.0016706851084058146 -6.822053472893596e-05 6.822053472892728e-05 -Nuclei_Texture_AngularSecondMoment_GFP_3_03_256 4.2305270994469346e-05 0.999075963855854 0.00047013308831691473 -0.007956716889089402 -0.0030040370611493254 0.003004037061149277 -Nuclei_Texture_AngularSecondMoment_RFP_3_00_256 0.009998736063179026 0.0003596333316335798 -0.005768361539929373 0.12186535141835553 0.13662620766995387 -0.13662620766995293 -Nuclei_Texture_AngularSecondMoment_RFP_3_01_256 0.01083951398063454 0.00016132076335484413 -0.005968570007926343 0.12633569386597718 0.1423586791873493 -0.14235867918734835 -Nuclei_Texture_AngularSecondMoment_RFP_3_02_256 0.01191209116971037 5.753050327028664e-05 -0.006425260035957367 0.13489923084990338 0.14871089796498915 -0.1487108979649881 -Nuclei_Texture_AngularSecondMoment_RFP_3_03_256 0.010422101545486728 0.00024036922341885703 -0.005971678078415003 0.12562387521622095 0.1392322251046085 -0.13923222510460753 -Nuclei_Texture_Contrast_DAPI_3_00_256 0.055302702819117755 1.1102230246251565e-16 0.013981978125827249 -0.21665872656947083 -0.00711412115459692 0.007114121154595809 -Nuclei_Texture_Contrast_DAPI_3_01_256 0.058546889348186415 1.1102230246251565e-16 0.014138142741135036 -0.2174815494630897 -0.0006202632907222894 0.0006202632907211514 -Nuclei_Texture_Contrast_DAPI_3_02_256 0.05037537180847973 1.1102230246251565e-16 0.012773346678878927 -0.1943146104291088 0.008382787094337901 -0.008382787094338873 -Nuclei_Texture_Contrast_DAPI_3_03_256 0.051232901683892274 1.1102230246251565e-16 0.013114631560588473 -0.20102266239008146 0.002365946397829999 -0.0023659463978310258 -Nuclei_Texture_Contrast_GFP_3_00_256 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0.14608705746319933 0.20929242667323059 -0.20929242667322934 +Nuclei_Texture_SumEntropy_DAPI_3_02_256 0.02264158091828894 1.3698768785985749e-09 8.863318464484538 -0.007170551698474097 0.16037071344517062 0.20639542932684118 -0.2063954293268399 +Nuclei_Texture_SumEntropy_DAPI_3_03_256 0.023821576870893257 4.1419412344367856e-10 9.382796067137612 -0.006559374321486106 0.15182119931527932 0.20987535835112897 -0.2098753583511277 +Nuclei_Texture_SumEntropy_GFP_3_00_256 0.045037307728693854 1.1102230246251565e-16 15.954589770191003 0.0035521679966113635 -0.012849580737742089 0.1718578730105756 -0.17185787301057526 +Nuclei_Texture_SumEntropy_GFP_3_01_256 0.04495078766546834 1.1102230246251565e-16 15.954589770191003 0.0034642408444688637 -0.01123898292420325 0.17292384442688868 -0.17292384442688832 +Nuclei_Texture_SumEntropy_GFP_3_02_256 0.04467019108070691 1.1102230246251565e-16 15.954589770191003 0.003515373520813778 -0.01237547932818106 0.17148125787595253 -0.1714812578759522 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1.1102230246251565e-16 15.954589770191003 0.010781879568895515 -0.15131770077436152 0.059355356517947565 -0.059355356517948196 +Nuclei_Texture_SumVariance_DAPI_3_01_256 0.04893024533144119 1.1102230246251565e-16 15.954589770191003 0.010144551849096186 -0.14004118421338435 0.06544508359139728 -0.06544508359139785 +Nuclei_Texture_SumVariance_DAPI_3_02_256 0.05132931824506104 1.1102230246251565e-16 15.954589770191003 0.010967046082679005 -0.15534693941051095 0.05448673786503894 -0.0544867378650396 +Nuclei_Texture_SumVariance_DAPI_3_03_256 0.05158422213825897 1.1102230246251565e-16 15.954589770191003 0.010415338140117273 -0.14377454049742755 0.06721147245553766 -0.06721147245553824 +Nuclei_Texture_SumVariance_GFP_3_00_256 0.027312143327572147 1.1782907982649249e-11 10.92874751388144 0.003461853243957969 -0.02323368854527033 0.12340323662167434 -0.12340323662167418 +Nuclei_Texture_SumVariance_GFP_3_01_256 0.02674649422230102 2.1021406837462564e-11 10.677338222594086 0.0033245339678202846 -0.021057748051592856 0.12367108241864697 -0.12367108241864679 +Nuclei_Texture_SumVariance_GFP_3_02_256 0.02690375943813872 1.7897683335377224e-11 10.747203180180705 0.003449812650161284 -0.023325727584142336 0.12226259942267814 -0.12226259942267799 +Nuclei_Texture_SumVariance_GFP_3_03_256 0.02714553226401717 1.397459925556177e-11 10.854660637365948 0.0034279011258513104 -0.022716056379468857 0.12338562793345617 -0.123385627933456 +Nuclei_Texture_SumVariance_RFP_3_00_256 0.004884426483319548 0.03883538901742112 1.4107723401226007 0.004737388457064355 -0.07767366742343032 -0.019965457011315677 0.019965457011315232 +Nuclei_Texture_SumVariance_RFP_3_01_256 0.005092414915655485 0.03242186765441346 1.4891619713307038 0.004846461098506129 -0.07953161708543308 -0.020711637647524955 0.02071163764752449 +Nuclei_Texture_SumVariance_RFP_3_02_256 0.005249173432684051 0.028275082906585758 1.5485961129871253 0.004907848506948155 -0.08044426238619004 -0.02058404749633111 0.02058404749633065 +Nuclei_Texture_SumVariance_RFP_3_03_256 0.004784877233536156 0.04232029068907772 1.373451357886178 0.004675717055406589 -0.07656441625526923 -0.0193017965802941 0.01930179658029367 +Nuclei_Texture_Variance_DAPI_3_00_256 0.05519169188139139 1.1102230246251565e-16 15.954589770191003 0.01180208630160943 -0.1700456841537531 0.04682059933854546 -0.04682059933854622 +Nuclei_Texture_Variance_DAPI_3_01_256 0.05492755938072991 1.1102230246251565e-16 15.954589770191003 0.011776143770125924 -0.1696870993861482 0.04665512773955438 -0.046655127739555144 +Nuclei_Texture_Variance_DAPI_3_02_256 0.054954711310022164 1.1102230246251565e-16 15.954589770191003 0.011851297898852864 -0.17124179362206887 0.0450111018431428 -0.04501110184314358 +Nuclei_Texture_Variance_DAPI_3_03_256 0.05537212942380454 1.1102230246251565e-16 15.954589770191003 0.011737751103443545 -0.16857526451775304 0.04880227050186842 -0.048802270501869166 +Nuclei_Texture_Variance_GFP_3_00_256 0.0277239279415179 7.727263273693552e-12 11.111974290762022 0.0034820001462264626 -0.023296340946126204 0.12442005939506603 -0.12442005939506587 +Nuclei_Texture_Variance_GFP_3_01_256 0.027406287527941386 1.0699774399824946e-11 10.97062537913199 0.003432858439090188 -0.022605851818019274 0.12415287352550754 -0.12415287352550737 +Nuclei_Texture_Variance_GFP_3_02_256 0.027485661748974488 9.864220551492053e-12 11.00593722602859 0.0034690787623738537 -0.0232356394161993 0.1238523662408014 -0.12385236624080123 +Nuclei_Texture_Variance_GFP_3_03_256 0.027603170084768647 8.74533778727482e-12 11.058223411306361 0.003442344409582695 -0.022632994770781373 0.12464133296710009 -0.12464133296709992 +Nuclei_Texture_Variance_RFP_3_00_256 0.004956430253377442 0.03648817770541646 1.4378478256527287 0.004839538752108734 -0.0798649616319095 -0.022521642911276202 0.022521642911275737 +Nuclei_Texture_Variance_RFP_3_01_256 0.0051424931520230865 0.031037247670725132 1.508116798187509 0.004942714940089977 -0.0816701210774374 -0.02342361115892609 0.023423611158925613 +Nuclei_Texture_Variance_RFP_3_02_256 0.005079289471157655 0.03279449873612705 1.4841990029018373 0.004878055900939448 -0.08033849712465993 -0.02203370479382112 0.02203370479382067 +Nuclei_Texture_Variance_RFP_3_03_256 0.0048366468761950054 0.04047219083871889 1.3928432857637718 0.004774401774033 -0.07874137281610233 -0.02201823689416045 0.022018236894159993 diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py index 36c63cb..359cf6c 100644 --- a/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py +++ b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py @@ -158,8 +158,11 @@ # Calculate the p-value for the F-statistic p_value = 1 - stats.f.cdf(f_value, dfreg, dfres) + # Calculate the negative log of the p value + nlogp = -1 * np.log10(p_value) + # Add results to a growing list - lm_results.append([cp_feature, r2_score, p_value] + list(coef)) + lm_results.append([cp_feature, r2_score, p_value, nlogp] + list(coef)) # In[ ]: @@ -172,6 +175,7 @@ "feature", "r2_score", "p_value", + "-log_p", "cell_count_coef", "plate_coef", "Null_coef", diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb index 4111cc8..5d171dd 100644 --- a/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb +++ b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb @@ -1,254 +1,258 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "9A4gVcqrrm" - }, - "source": [ - "# Fit a linear model on cell morphology features\n", - "\n", - "Our aim is to determine which features significantly contribute to NF1 genotype adjusted by covariates. This notebook is adapted from Dr. Way at:\n", - "https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/fit_linear_model.ipynb" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "9A4gVcqrrm" + }, + "source": [ + "# Fit a linear model on cell morphology features\n", + "\n", + "Our aim is to determine which features significantly contribute to NF1 genotype adjusted by covariates. This notebook is adapted from Dr. Way at:\n", + "https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/fit_linear_model.ipynb" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "Lq6i6b1L6q" + }, + "source": [ + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "import numpy as np\n", + "from scipy import stats" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "261w9QY97P" + }, + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "jG7YhMVm79" + }, + "source": [ + "sys.path.append(f\"{root_dir}/utils\")\n", + "import preprocess_utils as ppu" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "EwwGYfhdHG" + }, + "source": [ + "data_path = Path(\"data\")\n", + "\n", + "data_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "9bdkn1aAvb" + }, + "source": [ + "plate_path = Path(\n", + " f\"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data\"\n", + ")\n", + "\n", + "filename1 = \"Plate_1_sc_norm.parquet\"\n", + "filename2 = \"Plate_2_sc_norm.parquet\"\n", + "\n", + "path1 = plate_path / filename1\n", + "path2 = plate_path / filename2\n", + "\n", + "po1 = ppu.Preprocess_data(path=path1)\n", + "po2 = ppu.Preprocess_data(path=path2)\n", + "\n", + "plate1df = po1.df # Returns the dataframe generated by the csv\n", + "plate2df = po2.df # Returns the dataframe generated by the csv\n", + "\n", + "# Set plate column:\n", + "plate1df[\"Metadata_plate\"] = \"1\"\n", + "plate2df[\"Metadata_plate\"] = \"2\"\n", + "\n", + "common_columns = list(plate1df.columns.intersection(plate2df.columns))\n", + "plate1df = plate1df.loc[:, common_columns]\n", + "plate2df = plate2df.loc[:, common_columns]\n", + "\n", + "# Combine the plate dataframes:\n", + "cp_df = pd.concat([plate1df, plate2df], axis=\"rows\")\n", + "cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)]\n", + "cp_df.reset_index(inplace=True, drop=True)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "K57KpbjyKN" + }, + "source": [ + "pcp_features = po1.remove_meta(cp_df)\n", + "cp_features = pcp_features.columns" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "2111g4dKC3" + }, + "source": [ + "## Fit linear model" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "YeIyN5E3xy" + }, + "source": [ + "# Setup linear modeling framework\n", + "variables = [\"Metadata_number_of_singlecells\", \"Metadata_plate\"]\n", + "X = cp_df.loc[:, variables]\n", + "\n", + "# Add dummy matrix of categorical genotypes\n", + "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype).astype(int)\n", + "\n", + "X = pd.concat([X, genotype_x], axis=1)\n", + "X_arr = X.values\n", + "\n", + "dfreg = X.shape[1]\n", + "dfres = X.shape[0] - dfreg - 1" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "4gra1XWKi3" + }, + "source": [ + "# Fit linear model for each feature\n", + "lm_results = []\n", + "i = 0\n", + "for cp_feature in cp_features:\n", + " # Subset CP data to each individual feature (univariate test)\n", + " y = cp_df.loc[:, cp_feature]\n", + "\n", + " # Fit linear model\n", + " lm = LinearRegression(fit_intercept=True)\n", + " lm_result = lm.fit(X=X_arr, y=y)\n", + "\n", + " # Extract Beta coefficients\n", + " # (contribution of feature to X covariates)\n", + " coef = lm_result.coef_\n", + "\n", + " # Estimate fit (R^2)\n", + " r2_score = lm.score(X=X_arr, y=y)\n", + "\n", + " y_pred = lm.predict(X_arr)\n", + "\n", + " # Calculate the residuals\n", + " residuals = y.values - y_pred\n", + "\n", + " # Calculate the Sum of Squares Regression (SSR)\n", + " ssr = np.sum((y_pred - np.mean(y.values)) ** 2)\n", + "\n", + " # Calculate the Mean Squares Regression (MSR)\n", + " msr = ssr / dfreg\n", + "\n", + " # Calculate the Sum of Squares Residual (SSE) or residual sum of squares\n", + " sse = np.sum(residuals**2)\n", + "\n", + " # Calculate the Mean Squares Residual (MSE)\n", + " mse = sse / dfres\n", + "\n", + " # Calculate the F-value\n", + " f_value = msr / mse\n", + "\n", + " # Calculate the p-value for the F-statistic\n", + " p_value = 1 - stats.f.cdf(f_value, dfreg, dfres)\n", + "\n", + " # Calculate the negative log of the p value\n", + " nlogp = -1 * np.log10(p_value)\n", + "\n", + " # Add results to a growing list\n", + " lm_results.append([cp_feature, r2_score, p_value, nlogp] + list(coef))" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "DnyVti231X" + }, + "source": [ + "# Convert results to a pandas DataFrame\n", + "lm_results = pd.DataFrame(\n", + " lm_results,\n", + " columns=[\n", + " \"feature\",\n", + " \"r2_score\",\n", + " \"p_value\",\n", + " \"-log_p\",\n", + " \"cell_count_coef\",\n", + " \"plate_coef\",\n", + " \"Null_coef\",\n", + " \"WT_coef\",\n", + " ],\n", + ")\n", + "\n", + "lm_results.to_csv(f\"{data_path}/model_properties.tsv\", sep=\"\\t\", index=False)" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" + } }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "Lq6i6b1L6q" - }, - "source": [ - "from pathlib import Path\n", - "import pandas as pd\n", - "\n", - "from sklearn.linear_model import LinearRegression\n", - "import numpy as np\n", - "from scipy import stats" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "261w9QY97P" - }, - "source": [ - "# Get the current working directory\n", - "cwd = Path.cwd()\n", - "\n", - "if (cwd / \".git\").is_dir():\n", - " root_dir = cwd\n", - "\n", - "else:\n", - " root_dir = None\n", - " for parent in cwd.parents:\n", - " if (parent / \".git\").is_dir():\n", - " root_dir = parent\n", - " break\n", - "\n", - "# Check if a Git root directory was found\n", - "if root_dir is None:\n", - " raise FileNotFoundError(\"No Git root directory found.\")" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "jG7YhMVm79" - }, - "source": [ - "sys.path.append(f\"{root_dir}/utils\")\n", - "import preprocess_utils as ppu" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "EwwGYfhdHG" - }, - "source": [ - "data_path = Path(\"data\")\n", - "\n", - "data_path.mkdir(\n", - " parents=True, exist_ok=True\n", - ") # Create the parent directories if they don't exist" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "9bdkn1aAvb" - }, - "source": [ - "plate_path = Path(\n", - " f\"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data\"\n", - ")\n", - "\n", - "filename1 = \"Plate_1_sc_norm.parquet\"\n", - "filename2 = \"Plate_2_sc_norm.parquet\"\n", - "\n", - "path1 = plate_path / filename1\n", - "path2 = plate_path / filename2\n", - "\n", - "po1 = ppu.Preprocess_data(path=path1)\n", - "po2 = ppu.Preprocess_data(path=path2)\n", - "\n", - "plate1df = po1.df # Returns the dataframe generated by the csv\n", - "plate2df = po2.df # Returns the dataframe generated by the csv\n", - "\n", - "# Set plate column:\n", - "plate1df[\"Metadata_plate\"] = \"1\"\n", - "plate2df[\"Metadata_plate\"] = \"2\"\n", - "\n", - "common_columns = list(plate1df.columns.intersection(plate2df.columns))\n", - "plate1df = plate1df.loc[:, common_columns]\n", - "plate2df = plate2df.loc[:, common_columns]\n", - "\n", - "# Combine the plate dataframes:\n", - "cp_df = pd.concat([plate1df, plate2df], axis=\"rows\")\n", - "cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)]\n", - "cp_df.reset_index(inplace=True, drop=True)" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "K57KpbjyKN" - }, - "source": [ - "pcp_features = po1.remove_meta(cp_df)\n", - "cp_features = pcp_features.columns" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "2111g4dKC3" - }, - "source": [ - "## Fit linear model" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "YeIyN5E3xy" - }, - "source": [ - "# Setup linear modeling framework\n", - "variables = [\"Metadata_number_of_singlecells\", \"Metadata_plate\"]\n", - "X = cp_df.loc[:, variables]\n", - "\n", - "# Add dummy matrix of categorical genotypes\n", - "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype).astype(int)\n", - "\n", - "X = pd.concat([X, genotype_x], axis=1)\n", - "X_arr = X.values\n", - "\n", - "dfreg = X.shape[1]\n", - "dfres = X.shape[0] - dfreg - 1" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "4gra1XWKi3" - }, - "source": [ - "# Fit linear model for each feature\n", - "lm_results = []\n", - "i = 0\n", - "for cp_feature in cp_features:\n", - " # Subset CP data to each individual feature (univariate test)\n", - " y = cp_df.loc[:, cp_feature]\n", - "\n", - " # Fit linear model\n", - " lm = LinearRegression(fit_intercept=True)\n", - " lm_result = lm.fit(X=X_arr, y=y)\n", - "\n", - " # Extract Beta coefficients\n", - " # (contribution of feature to X covariates)\n", - " coef = lm_result.coef_\n", - "\n", - " # Estimate fit (R^2)\n", - " r2_score = lm.score(X=X_arr, y=y)\n", - "\n", - " y_pred = lm.predict(X_arr)\n", - "\n", - " # Calculate the residuals\n", - " residuals = y.values - y_pred\n", - "\n", - " # Calculate the Sum of Squares Regression (SSR)\n", - " ssr = np.sum((y_pred - np.mean(y.values)) ** 2)\n", - "\n", - " # Calculate the Mean Squares Regression (MSR)\n", - " msr = ssr / dfreg\n", - "\n", - " # Calculate the Sum of Squares Residual (SSE) or residual sum of squares\n", - " sse = np.sum(residuals**2)\n", - "\n", - " # Calculate the Mean Squares Residual (MSE)\n", - " mse = sse / dfres\n", - "\n", - " # Calculate the F-value\n", - " f_value = msr / mse\n", - "\n", - " # Calculate the p-value for the F-statistic\n", - " p_value = 1 - stats.f.cdf(f_value, dfreg, dfres)\n", - "\n", - " # Add results to a growing list\n", - " lm_results.append([cp_feature, r2_score, p_value] + list(coef))" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "DnyVti231X" - }, - "source": [ - "# Convert results to a pandas DataFrame\n", - "lm_results = pd.DataFrame(\n", - " lm_results,\n", - " columns=[\n", - " \"feature\",\n", - " \"r2_score\",\n", - " \"p_value\",\n", - " \"cell_count_coef\",\n", - " \"plate_coef\",\n", - " \"Null_coef\",\n", - " \"WT_coef\",\n", - " ],\n", - ")\n", - "\n", - "lm_results.to_csv(f\"{data_path}/model_properties.tsv\", sep=\"\\t\", index=False)" - ], - "outputs": [], - "execution_count": null - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "python", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + "nbformat": 4, + "nbformat_minor": 4 +} From 3e6cb5655f4ae0130d79b36c241b8b8c48025a6b Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Wed, 14 Jun 2023 10:05:35 -0600 Subject: [PATCH 07/12] Switched a comparision operator --- .../data/significant_feature_compartments.tsv | 1161 +++++++++++++---- .../figures/combined_plates_sig_lm.png | Bin 1181504 -> 1264269 bytes .../nbconverted/seperate_compartments.py | 5 +- .../seperate_compartments.ipynb | 578 ++++---- 4 files changed, 1175 insertions(+), 569 deletions(-) diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv index 2d6580a..64befb7 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv @@ -1,278 +1,885 @@ feature r2_score p_value -log_p cell_count_coef plate_coef Null_coef WT_coef compartment -Cytoplasm_Granularity_10_RFP 0.0037617125664257 0.1003252629539767 0.998589693300464 0.0035668092075674 -0.0547577118054782 0.0002926773188153 -0.0002926773188156 actin -Cytoplasm_Intensity_MaxIntensityEdge_RFP 0.0041062181249255 0.0753599505618121 1.1228593951241392 0.0045262376910301 -0.0756843357731082 -0.0251370088643017 0.0251370088643013 actin -Cytoplasm_Intensity_MaxIntensity_RFP 0.0006338886393454 0.8601030189140192 0.0654495279815697 0.000867179392635 -0.0101453541758644 0.0131088137209483 -0.0131088137209483 actin -Cytoplasm_Location_CenterMassIntensity_X_RFP 0.0007866405503756 0.804708211979182 0.094361566712008 0.0017169009877283 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0.0548510724988972 -0.054851072498897 er -Cells_Granularity_2_GFP 0.0018471647588139 0.4319931889773358 0.3645231004371194 0.0025729804423935 -0.0399852809409123 -0.0017844638675422 0.001784463867542 er -Cells_Granularity_9_GFP 0.0008194091787173 0.7924763204456392 0.1010137058337883 0.0002455205894635 0.0026454090209695 0.0264224700612829 -0.0264224700612828 er -Cells_Intensity_MaxIntensity_GFP 0.0044176696479387 0.0579449421065902 1.2369844667075307 -0.0035998996995328 0.0775132923563265 0.0912743931923461 -0.0912743931923455 er -Cells_Location_CenterMassIntensity_X_GFP 0.0007956766052217 0.801343983656713 0.0961810193266429 0.0016980927439741 -0.0264507512618806 -0.0014313242110441 0.001431324211044 er -Cells_Location_CenterMassIntensity_Y_GFP 0.0006334881672256 0.8602434778794763 0.0653786113913582 0.0018575367112588 -0.0358428848136485 -0.0300007895132433 0.030000789513243 er -Cells_Location_MaxIntensity_X_GFP 0.001062607317869 0.7004463894618262 0.1546250989961683 0.0016253473902022 -0.0233385317463606 0.0067757929210782 -0.0067757929210783 er -Cells_Location_MaxIntensity_Y_GFP 0.0004918070276632 0.9076114850348646 0.0421000171329507 0.0015996626552389 -0.031226953244381 -0.0273175889390097 0.0273175889390095 er -Cells_RadialDistribution_FracAtD_GFP_1of4 9.513572403785364e-05 0.9954925332209238 0.0019619930882157 -0.0007318416831456 0.0139832579210523 0.0112506163987204 -0.0112506163987204 er -Cells_RadialDistribution_FracAtD_GFP_2of4 0.0004991113518849 0.9053019507606817 0.0432065438030533 -0.00173325959651 0.0320581105709066 0.0222858942046641 -0.0222858942046639 er -Cells_RadialDistribution_FracAtD_GFP_3of4 0.0041832493028874 0.070641988535916 1.150937084090451 -0.0044422155503375 0.0885354230185213 0.0833474617910887 -0.0833474617910881 er -Cells_RadialDistribution_FracAtD_GFP_4of4 0.0023255361256051 0.3083497279506422 0.5109564304048145 0.0034692821672873 -0.0678078681330054 -0.0595915893161797 0.0595915893161792 er 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0.0044070912074402 -0.0843352630864772 -0.0682821569137859 0.0682821569137853 er -Cells_Texture_InfoMeas2_GFP_3_00_256 0.0008517169746365 0.7803414350983944 0.1077153320094615 -0.0022715003703414 0.0411114578373915 0.0254944208771856 -0.0254944208771853 er -Cells_Texture_InfoMeas2_GFP_3_01_256 0.0013261665383932 0.6028655374653653 0.2197795416718903 -0.002827369629366 0.0522143301852329 0.0360233232052486 -0.0360233232052482 er -Cells_Texture_InfoMeas2_GFP_3_02_256 0.0019565128359896 0.4008499450384903 0.3970181716137602 -0.0034397525768552 0.0620418426944752 0.0377272881653404 -0.03772728816534 er -Cells_Texture_InfoMeas2_GFP_3_03_256 0.0015667153532853 0.5196014077284845 0.2843296809864526 -0.0030094756193776 0.0526664376669866 0.0263623077815917 -0.0263623077815914 er -Nuclei_Granularity_10_GFP 0.0039491453845529 0.0859155493756601 1.0659282285213216 -0.0028548844585144 0.0647651979914293 0.0859411339540115 -0.085941133954011 er -Nuclei_Granularity_11_GFP 0.0005627775416604 0.8845137613147607 0.0532954059237629 0.0004893010438738 -0.003189279008026 0.0178312317647578 -0.0178312317647578 er -Nuclei_Granularity_12_GFP 0.0020619278379977 0.3724566376484277 0.4289242816437531 0.0016479988294947 -0.0199649835797536 0.0220942892507555 -0.0220942892507555 er -Nuclei_Granularity_14_GFP 0.0016148474164955 0.5037984675510914 0.2977431578683087 -0.001356453708351 0.033764513218679 0.053149900432504 -0.0531499004325038 er -Nuclei_Granularity_15_GFP 0.0017140693020247 0.4722079678601589 0.3258666890732496 -0.0021072030007904 0.0462179720614556 0.0569076621534371 -0.0569076621534368 er -Nuclei_Granularity_2_GFP 0.0010490319408842 0.705581420050845 0.1514528639244695 -3.86207481008494e-05 -0.0073977489151264 -0.0328922635683254 0.0328922635683252 er -Nuclei_Intensity_IntegratedIntensityEdge_GFP 0.0023987109902465 0.2922495432543798 0.5342461589078075 -0.0034620578196622 0.0681981473524371 0.0616550421896342 -0.0616550421896337 er 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0.2645203858467868 -0.0018671589740721 0.041593611087282 0.0530616588214852 -0.0530616588214848 nucleus -Cytoplasm_Granularity_9_DAPI 0.0018006015906264 0.4457760730461524 0.350883245911628 0.0013816319568592 -0.0348065883329415 -0.0558459935116626 0.0558459935116623 nucleus -Cytoplasm_Location_CenterMassIntensity_X_DAPI 0.0008209714222029 0.7918911021386273 0.1013345368347035 0.0017187153484721 -0.026731761212607 -0.00128315129122 0.0012831512912198 nucleus -Cytoplasm_Location_CenterMassIntensity_Y_DAPI 0.0006408890857526 0.8576427977233153 0.0666935551334524 0.0018432529914507 -0.0358195007865007 -0.0308082745788126 0.0308082745788124 nucleus -Cytoplasm_Location_MaxIntensity_X_DAPI 0.0008102679022686 0.7958971037472358 0.0991430756835873 0.0016134144511718 -0.0245018953261707 0.0012324545692643 -0.0012324545692644 nucleus -Cytoplasm_Location_MaxIntensity_Y_DAPI 0.0005056024748221 0.9032356962374724 0.0441989072515796 0.0016747192388272 -0.032146462733196 -0.026353401283591 0.0263534012835908 nucleus -Cytoplasm_RadialDistribution_FracAtD_DAPI_1of4 0.0033613439476051 0.1389439494482449 0.8571603605703313 -0.0038471267348392 0.0624754445012082 0.0137373641732462 -0.0137373641732458 nucleus -Cytoplasm_RadialDistribution_RadialCV_DAPI_1of4 0.002401462940082 0.2916579799369576 0.5351261365143526 0.0037411443341174 -0.0657123768946159 -0.0337659160753935 0.0337659160753931 nucleus -Cytoplasm_Texture_Correlation_DAPI_3_00_256 0.0035656436514082 0.1177887775434129 0.9288960854669354 -0.0024808854606974 0.0325503992834769 -0.0229902382978591 0.0229902382978592 nucleus -Cytoplasm_Texture_Correlation_DAPI_3_01_256 0.0011767970045039 0.6575761142527337 0.1820539704628871 -0.0003680393610205 -0.0017963477435139 -0.0306795561840553 0.0306795561840552 nucleus -Cytoplasm_Texture_Correlation_DAPI_3_02_256 0.0024564903995981 0.2800381376694643 0.5527828191880839 -0.0009588498215833 0.0054540275038673 -0.0382180519989423 0.0382180519989422 nucleus 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z_Z=x;YODCudV}A&Ploq_T zRm1NZVSfado}K=5D3$~-C0%eE&~2C6(}`#d+~p)wPdwiPayx+J^_<3u!*|mrPeoyp zqwoG_ven%#oOxIhn5nBAug>>vWM_Y3k3Bf>0J0z?S{QBvB@SvYPjynmQ}z@0+s}CR zuhU>LGCF!s*4wMD0?q^$TbVqTj~vn)Ls0nZDquSPd`lkMzkg-pCh%`@3Dz#Qpi2g) z;nmBRDTdgheNXXCk-_rMx#IU1r#tC@+n!G)h1^EI*1sf)_hg*={(AF%8^#66y?gCJ zmi^=#aY+=+B=V~2M^*w`c6AY29HeHBm`>-(dJ2loI=JER z-XNJWdPPHq0GyPDCuGA+eiBZ@zh0_K@cDoKgVpjSruaYq;Xd`>7v#@>^4anC$^YxG z+$&z%+JAoG|GWVTNB_TBM8W%|{_oGkB3-Z3sa`s@RZF9~jviK% Ke=29<`+osTQAcwC diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py index 534aef1..35d5b00 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py @@ -6,7 +6,7 @@ # In[1]: -#%%--%%| +# %%--%%| r"""°°° ## Imports °°°""" @@ -117,7 +117,6 @@ # Find intersected rows between each pair of compartments for pair in pairs: - # Create placeholder dataframes for each comparment in the pair df1 = compartment_data[pair[0]] df2 = compartment_data[pair[1]] @@ -162,6 +161,6 @@ concatenated_df.to_csv(output_path / "feature_compartments.tsv", sep="\t", index=False) # Create a dataframe with only significant models -concatenated_df.loc[concatenated_df["p_value"] >= 0.05].to_csv( +concatenated_df.loc[concatenated_df["p_value"] <= 0.05].to_csv( output_path / "significant_feature_compartments.tsv", sep="\t", index=False ) diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb index dcf45c8..64a7f04 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb @@ -1,296 +1,296 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "vk3QsHOEjG" - }, - "source": [ - "# Here the features are seperated according to compartments for later analysis" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "SyZ3qa8iz3" - }, - "source": [ - "# %%--%%| \n", - "r\"\"\"°°°\n", - "## Imports\n", - "°°°\"\"\"" - ], - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "vk3QsHOEjG" + }, + "source": [ + "# Here the features are seperated according to compartments for later analysis" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "SyZ3qa8iz3" + }, + "source": [ + "# %%--%%| \n", + "r\"\"\"\u00b0\u00b0\u00b0\n", + "## Imports\n", + "\u00b0\u00b0\u00b0\"\"\"" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "\n" + }, + { + "output_type": "execute_result", + "execution_count": 1, + "data": { + "text/plain": "'\u00b0\u00b0\u00b0\\n## Imports\\n\u00b0\u00b0\u00b0'" + }, + "metadata": {} + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "FKd1dIo45E" + }, + "source": [ + "import sys\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "from sklearn.metrics import (\n", + " confusion_matrix,\n", + " ConfusionMatrixDisplay,\n", + " precision_score,\n", + " accuracy_score,\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "from collections import Counter\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import itertools\n", + "\n", + "from joblib import dump, load" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "ItCxTfECNg" + }, + "source": [ + "## Find the git root Directory" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "zjh0SKYFgl" + }, + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "OTkUmqTBVt" + }, + "source": [ + "## Create the output path if it doesn't exist" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "W8zS6OpdNq" + }, + "source": [ + "output_path = Path(\"data\")\n", + "\n", + "output_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "KChOdgSroB" + }, + "source": [ + "## Import the model data as a dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "aYwhtTNAgx" + }, + "source": [ + "feature_properties = pd.read_csv(\n", + " root_dir\n", + " / \"1.train_models/linear_reg_plate_1_2_norm_data/data\"\n", + " / \"model_properties.tsv\",\n", + " sep=\"\\t\",\n", + ")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "QmPiNYcAdw" + }, + "source": [ + "## Seperate cell data by channel" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "wSEsy3OWnz" + }, + "source": [ + "# A map for comparments to channel\n", + "compartment2channel = {\"actin\": \"RFP\", \"er\": \"GFP\", \"nucleus\": \"DAPI\"}\n", + "\n", + "# Create a Dictionary to hold the comparment data as dataframes\n", + "compartment_data = {\n", + " compartment: feature_properties[feature_properties[\"feature\"].str.contains(channel)]\n", + " for compartment, channel in compartment2channel.items()\n", + "}" + ], + "outputs": [], + "execution_count": null + }, { - "output_type": "stream", - "name": "stdout", - "text": "\n" + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "hAmkVoVyDd" + }, + "source": [ + "## Find the features that use more than one compartment" + ] }, { - "output_type": "execute_result", - "execution_count": 1, - "data": { - "text/plain": "'°°°\\n## Imports\\n°°°'" - }, - "metadata": {} + "cell_type": "code", + "metadata": { + "jukit_cell_id": "CQscjFvYen" + }, + "source": [ + "# Create a list of all possible comparment pairs\n", + "pairs = list(itertools.combinations(compartment_data.keys(), 2))\n", + "\n", + "# Use a list of possible compartments for find compartment agnostic features\n", + "pos_compartments = list(compartment2channel.values())\n", + "\n", + "# Get the features that do not belong to any compartment specifically\n", + "other_compartment = feature_properties[\n", + " ~feature_properties[\"feature\"].str.contains(\"|\".join(pos_compartments))\n", + "]\n", + "\n", + "# Find intersected rows between each pair of compartments\n", + "for pair in pairs:\n", + " # Create placeholder dataframes for each comparment in the pair\n", + " df1 = compartment_data[pair[0]]\n", + " df2 = compartment_data[pair[1]]\n", + "\n", + " # Get duplicate features between the two compartment dataframes\n", + " intersection = pd.concat([df1, df2], axis=0)\n", + " intersection = intersection[intersection.duplicated(subset=\"feature\", keep=False)]\n", + "\n", + " # Remove any duplicate features already added to the other compartment dataframe\n", + " other_compartment = pd.concat([other_compartment, intersection], axis=0)\n", + " other_compartment = other_compartment.drop_duplicates(subset=\"feature\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "UwxezzhMqY" + }, + "source": [ + "## Organize the data according to compartment in one dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "iTJOXFvhuT" + }, + "source": [ + "# Remove features from each compartment dataframe if they are duplicates in other dataframes, or if the features only exist in the other_compartment dataframe\n", + "compartment_data = {\n", + " compartment: compartment_data[compartment][\n", + " ~compartment_data[compartment][\"feature\"].isin(other_compartment[\"feature\"])\n", + " ]\n", + " for compartment, channel in compartment2channel.items()\n", + "}\n", + "\n", + "compartment_data[\"other\"] = other_compartment\n", + "\n", + "# Create a compartment column for each comparment dataframe\n", + "for compartment, df in compartment_data.items():\n", + " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n", + "\n", + "# Concatenate the rows of DataFrames to create the plot below\n", + "concatenated_df = pd.concat(compartment_data.values(), ignore_index=True)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "NtZG4xLsob" + }, + "source": [ + "## Save the dataframes" + ] + }, + { + "cell_type": "code", + "metadata": { + "jukit_cell_id": "NUwlraBlOz" + }, + "source": [ + "concatenated_df.to_csv(output_path / \"feature_compartments.tsv\", sep=\"\\t\", index=False)\n", + "\n", + "# Create a dataframe with only significant models\n", + "concatenated_df.loc[concatenated_df[\"p_value\"] <= 0.05].to_csv(\n", + " output_path / \"significant_feature_compartments.tsv\", sep=\"\\t\", index=False\n", + ")" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" } - ], - "execution_count": 1 - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "FKd1dIo45E" - }, - "source": [ - "import sys\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pathlib import Path\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "from sklearn.metrics import (\n", - " confusion_matrix,\n", - " ConfusionMatrixDisplay,\n", - " precision_score,\n", - " accuracy_score,\n", - ")\n", - "from sklearn.model_selection import train_test_split\n", - "from collections import Counter\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import itertools\n", - "\n", - "from joblib import dump, load" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "ItCxTfECNg" - }, - "source": [ - "## Find the git root Directory" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "zjh0SKYFgl" - }, - "source": [ - "# Get the current working directory\n", - "cwd = Path.cwd()\n", - "\n", - "if (cwd / \".git\").is_dir():\n", - " root_dir = cwd\n", - "\n", - "else:\n", - " root_dir = None\n", - " for parent in cwd.parents:\n", - " if (parent / \".git\").is_dir():\n", - " root_dir = parent\n", - " break\n", - "\n", - "# Check if a Git root directory was found\n", - "if root_dir is None:\n", - " raise FileNotFoundError(\"No Git root directory found.\")" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "OTkUmqTBVt" - }, - "source": [ - "## Create the output path if it doesn't exist" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "W8zS6OpdNq" - }, - "source": [ - "output_path = Path(\"data\")\n", - "\n", - "output_path.mkdir(\n", - " parents=True, exist_ok=True\n", - ") # Create the parent directories if they don't exist" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "KChOdgSroB" - }, - "source": [ - "## Import the model data as a dataframe" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "aYwhtTNAgx" - }, - "source": [ - "feature_properties = pd.read_csv(\n", - " root_dir\n", - " / \"1.train_models/linear_reg_plate_1_2_norm_data/data\"\n", - " / \"model_properties.tsv\",\n", - " sep=\"\\t\",\n", - ")" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "QmPiNYcAdw" - }, - "source": [ - "## Seperate cell data by channel" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "wSEsy3OWnz" - }, - "source": [ - "# A map for comparments to channel\n", - "compartment2channel = {\"actin\": \"RFP\", \"er\": \"GFP\", \"nucleus\": \"DAPI\"}\n", - "\n", - "# Create a Dictionary to hold the comparment data as dataframes\n", - "compartment_data = {\n", - " compartment: feature_properties[feature_properties[\"feature\"].str.contains(channel)]\n", - " for compartment, channel in compartment2channel.items()\n", - "}" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "hAmkVoVyDd" - }, - "source": [ - "## Find the features that use more than one compartment" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "CQscjFvYen" - }, - "source": [ - "# Create a list of all possible comparment pairs\n", - "pairs = list(itertools.combinations(compartment_data.keys(), 2))\n", - "\n", - "# Use a list of possible compartments for find compartment agnostic features\n", - "pos_compartments = list(compartment2channel.values())\n", - "\n", - "# Get the features that do not belong to any compartment specifically\n", - "other_compartment = feature_properties[\n", - " ~feature_properties[\"feature\"].str.contains(\"|\".join(pos_compartments))\n", - "]\n", - "\n", - "# Find intersected rows between each pair of compartments\n", - "for pair in pairs:\n", - " # Create placeholder dataframes for each comparment in the pair\n", - " df1 = compartment_data[pair[0]]\n", - " df2 = compartment_data[pair[1]]\n", - "\n", - " # Get duplicate features between the two compartment dataframes\n", - " intersection = pd.concat([df1, df2], axis=0)\n", - " intersection = intersection[intersection.duplicated(subset=\"feature\", keep=False)]\n", - "\n", - " # Remove any duplicate features already added to the other compartment dataframe\n", - " other_compartment = pd.concat([other_compartment, intersection], axis=0)\n", - " other_compartment = other_compartment.drop_duplicates(subset=\"feature\")" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "UwxezzhMqY" - }, - "source": [ - "## Organize the data according to compartment in one dataframe" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "iTJOXFvhuT" - }, - "source": [ - "# Remove features from each compartment dataframe if they are duplicates in other dataframes, or if the features only exist in the other_compartment dataframe\n", - "compartment_data = {\n", - " compartment: compartment_data[compartment][\n", - " ~compartment_data[compartment][\"feature\"].isin(other_compartment[\"feature\"])\n", - " ]\n", - " for compartment, channel in compartment2channel.items()\n", - "}\n", - "\n", - "compartment_data[\"other\"] = other_compartment\n", - "\n", - "# Create a compartment column for each comparment dataframe\n", - "for compartment, df in compartment_data.items():\n", - " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n", - "\n", - "# Concatenate the rows of DataFrames to create the plot below\n", - "concatenated_df = pd.concat(compartment_data.values(), ignore_index=True)" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "NtZG4xLsob" - }, - "source": [ - "## Save the dataframes" - ] }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "NUwlraBlOz" - }, - "source": [ - "concatenated_df.to_csv(output_path / \"feature_compartments.tsv\", sep=\"\\t\", index=False)\n", - "\n", - "# Create a dataframe with only significant models\n", - "concatenated_df.loc[concatenated_df[\"p_value\"] >= 0.05].to_csv(\n", - " output_path / \"significant_feature_compartments.tsv\", sep=\"\\t\", index=False\n", - ")" - ], - "outputs": [], - "execution_count": null - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "python", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 } From 64363ccd0b328481c84bd0d89033d5a149fc1fc2 Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Fri, 16 Jun 2023 13:50:08 -0600 Subject: [PATCH 08/12] Added refacotring and p_value correction suggestions --- .../data/model_properties.tsv | 2324 ++++++++--------- .../nbconverted/train_model.py | 65 +- .../train_model.ipynb | 638 +++-- 3 files changed, 1602 insertions(+), 1425 deletions(-) diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv b/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv index b5996e9..db17b5f 100644 --- a/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv +++ b/1.train_models/linear_reg_plate_1_2_norm_data/data/model_properties.tsv @@ -1,1162 +1,1162 @@ -feature r2_score p_value -log_p cell_count_coef plate_coef Null_coef WT_coef -Cytoplasm_AreaShape_Area 0.18474653920991335 1.1102230246251565e-16 15.954589770191003 -0.014366895583179384 0.16455192415455927 -0.23170835918151744 0.23170835918151778 -Cytoplasm_AreaShape_BoundingBoxArea 0.17189193243211054 1.1102230246251565e-16 15.954589770191003 -0.016323544414939307 0.20781946368794818 -0.17741893610840856 0.1774189361084092 -Cytoplasm_AreaShape_BoundingBoxMaximum_X 0.0006686522828195374 0.8477981783245556 0.07170752094123875 -0.00020982393246391827 -0.002611737414587152 -0.02402534842193505 0.024025348421934985 -Cytoplasm_AreaShape_BoundingBoxMaximum_Y 0.004623520471195297 0.04861314077390888 1.313246319334559 -0.0009847817393303932 0.0012232505317791575 -0.05727241082936399 0.05727241082936387 -Cytoplasm_AreaShape_BoundingBoxMinimum_X 0.006643417228757387 0.008157038547171647 2.088467485371594 0.0038512013186171377 -0.0539343420742183 0.02167513974965026 -0.021675139749650487 -Cytoplasm_AreaShape_BoundingBoxMinimum_Y 0.0065068094079506755 0.009230214384759461 2.034788211754503 0.00481213451282194 -0.07467209079560387 -0.0028819431758143124 0.0028819431758139308 -Cytoplasm_AreaShape_Center_X 0.0008253840774573584 0.790237195752141 0.10224253232386624 0.0017318404680637819 -0.026991759598672196 -0.00152286257861017 0.0015228625786100278 -Cytoplasm_AreaShape_Center_Y 0.0006476256396724667 0.8552666776527195 0.06789844834204918 0.0018503618710664477 -0.03598224063554693 -0.03102832323811212 0.031028323238111867 -Cytoplasm_AreaShape_Compactness 0.0037210957930179367 0.1037319371043417 0.984087511954332 0.004257913124454014 -0.08431896166431146 -0.07765357386835678 0.0776535738683562 -Cytoplasm_AreaShape_ConvexArea 0.17982532454241573 1.1102230246251565e-16 15.954589770191003 -0.015633316704335737 0.19129038444518412 -0.20178093241798328 0.2017809324179838 -Cytoplasm_AreaShape_Eccentricity 0.004410350060662371 0.05830630055513919 1.234284513020008 -0.004982139383815284 0.0952028627189675 0.07662923769481841 -0.07662923769481776 -Cytoplasm_AreaShape_EquivalentDiameter 0.1494028496298394 1.1102230246251565e-16 15.954589770191003 -0.013932887661676883 0.16805131059321238 -0.1898453524123195 0.18984535241231992 -Cytoplasm_AreaShape_EulerNumber 0.001101561345625779 0.6857490404406357 0.16383479142540677 0.0024104555849951514 -0.044024588888653085 -0.028692996185100357 0.028692996185100066 -Cytoplasm_AreaShape_Extent 0.010328495037530416 0.00026280138242185114 3.5803723545741954 -0.0020499702462758434 0.012781197306802189 -0.07709512873755808 0.07709512873755797 -Cytoplasm_AreaShape_FormFactor 0.0051660518090178575 0.03040574483600289 1.5170443534023084 -0.005589818784788794 0.10276991017516758 0.06932684755362556 -0.06932684755362487 -Cytoplasm_AreaShape_MajorAxisLength 0.12473749755865948 1.1102230246251565e-16 15.954589770191003 -0.01623183353864438 0.22430666790882142 -0.10375595998225381 0.10375595998225472 -Cytoplasm_AreaShape_MaxFeretDiameter 0.13582702830372229 1.1102230246251565e-16 15.954589770191003 -0.015693436821142704 0.208634455148764 -0.13419750390414664 0.13419750390414742 -Cytoplasm_AreaShape_MaximumRadius 0.1168327128685529 1.1102230246251565e-16 15.954589770191003 -0.013228504768089028 0.16673326314309245 -0.15070398435580012 0.15070398435580062 -Cytoplasm_AreaShape_MeanRadius 0.11640369081620916 1.1102230246251565e-16 15.954589770191003 -0.013266275613819548 0.1676720889026541 -0.14922959482847814 0.14922959482847867 -Cytoplasm_AreaShape_MedianRadius 0.11775107386397887 1.1102230246251565e-16 15.954589770191003 -0.013351006949437685 0.16880369669042444 -0.1499329201906602 0.14993292019066073 -Cytoplasm_AreaShape_MinFeretDiameter 0.14570799655408517 1.1102230246251565e-16 15.954589770191003 -0.012903500759889842 0.14898974125779602 -0.20317230066823588 0.2031723006682362 -Cytoplasm_AreaShape_MinorAxisLength 0.14568815204182783 1.1102230246251565e-16 15.954589770191003 -0.01171497300810986 0.1256130097232725 -0.2241913595616944 0.2241913595616945 -Cytoplasm_AreaShape_Orientation 0.00011232417337903033 0.993789681225094 0.0027055169588370664 -0.0008252930461758963 0.015078370132820918 0.00984536023256886 -0.009845360232568757 -Cytoplasm_AreaShape_Perimeter 0.13717574847089586 1.1102230246251565e-16 15.954589770191003 -0.010833583190902659 0.111447221849869 -0.2267313332606949 0.22673133326069494 -Cytoplasm_AreaShape_Solidity 0.01637981962202517 7.261393928370552e-07 6.138980002262641 -0.0044779741052228105 0.05295541845348736 -0.0653603140505073 0.06536031405050742 -Cytoplasm_AreaShape_Zernike_0_0 0.011439088937936548 9.074874710390812e-05 4.042159362210475 -0.0004049954243073187 -0.01934675273346631 -0.10525426512650221 0.10525426512650188 -Cytoplasm_AreaShape_Zernike_1_1 0.005596581406647094 0.020829660673047035 1.6813178048973612 -0.00030829971541755294 -0.013072592528001317 -0.07331222170092558 0.07331222170092534 -Cytoplasm_AreaShape_Zernike_2_0 0.027911489803901013 6.3754557189099614e-12 11.19548876632394 -0.0037495390128448793 0.02827167255498375 -0.1208690817252653 0.12086908172526517 -Cytoplasm_AreaShape_Zernike_2_2 0.001321453572769915 0.6045612850898732 0.21855966762823337 -0.002818920183897007 0.052186560187874535 0.036443620720813766 -0.03644362072081342 -Cytoplasm_AreaShape_Zernike_3_1 5.495999858551315e-07 0.9999998395215051 6.969493040761916e-08 -4.9533755058258953e-05 0.0009974538229946088 0.0009714589025408957 -0.0009714589025408884 -Cytoplasm_AreaShape_Zernike_3_3 0.00031945520938991834 0.9563082435525218 0.019402100509526208 -0.0013852221551404306 0.02567059924298695 0.01801561614894007 -0.0180156161489399 -Cytoplasm_AreaShape_Zernike_4_0 0.0027762270130549105 0.22003256070437405 0.657513046960121 0.0040300380514377235 -0.07091489295095138 -0.03690084874195006 0.036900848741949606 -Cytoplasm_AreaShape_Zernike_4_2 0.004051373007551873 0.07889856864035405 1.1029308755894969 -0.004465972918278303 0.07442874211648193 0.023782013299101415 -0.02378201329910099 -Cytoplasm_AreaShape_Zernike_4_4 0.003137681995709074 0.16605046030928194 0.7797599161540992 -0.0030562606489673567 0.06566326453041352 0.07689643374231767 -0.07689643374231717 -Cytoplasm_AreaShape_Zernike_5_1 0.0010675082288147397 0.6985940560612804 0.15577511384690992 0.0024913675211216168 -0.047039312956034855 -0.03598212694359602 0.03598212694359569 -Cytoplasm_AreaShape_Zernike_5_3 0.0011295617537078195 0.6752267302181656 0.170550373615121 -0.00209974631145622 0.043262931116291176 0.04521666277577188 -0.04521666277577155 -Cytoplasm_AreaShape_Zernike_5_5 0.0005966185987893446 0.8730362010730379 0.058967747588480265 0.0018503474668604045 -0.03511948725382365 -0.027478189453467274 0.02747818945346703 -Cytoplasm_AreaShape_Zernike_6_0 0.0022850810579421177 0.3175609369441468 0.49817292536642055 0.0033749300870345154 -0.06651657454357736 -0.060246362267949514 0.06024636226794903 -Cytoplasm_AreaShape_Zernike_6_2 2.970658701462181e-05 0.9995404325247685 0.00019963349460016945 0.0001860767220049336 -0.002163602814667448 0.002867824038275239 -0.0028678240382752434 -Cytoplasm_AreaShape_Zernike_6_4 0.007918293552767297 0.0025352718563537158 2.595975464581537 -0.006839721077140726 0.12816145103295765 0.09475557652661619 -0.09475557652661531 -Cytoplasm_AreaShape_Zernike_6_6 0.0011181228145985544 0.6795205828221265 0.16779738386057008 -0.0017575224654418577 0.03818270864745717 0.04595928648884736 -0.04595928648884706 -Cytoplasm_AreaShape_Zernike_7_1 0.005385854279391866 0.02508114297550923 1.600652676109026 0.0030574462643623455 -0.07119669627524755 -0.09959671835650799 0.09959671835650744 -Cytoplasm_AreaShape_Zernike_7_3 0.0019179086405771706 0.4116481021941131 0.38547388245949177 0.003210530594643503 -0.06217246391482098 -0.052767816344329845 0.0527678163443294 -Cytoplasm_AreaShape_Zernike_7_5 0.001032442676744627 0.7118637178323952 0.14760314155763177 0.0014998235947499192 -0.0337508111855445 -0.04402247611497913 0.04402247611497885 -Cytoplasm_AreaShape_Zernike_7_7 3.5766377674884e-05 0.9993365740153008 0.00028821786048973964 0.00033478746114242304 -0.007137653175124886 -0.008196147972625776 0.00819614797262572 -Cytoplasm_AreaShape_Zernike_8_0 0.0014528502275646948 0.5581676915178619 0.25323530543533795 0.001034380278109718 -0.027776660335313008 -0.04888184873812218 0.04888184873812193 -Cytoplasm_AreaShape_Zernike_8_2 1.461736388130852e-05 0.9998875728891521 4.8829218773005154e-05 0.00029765458734849645 -0.005447373387437035 -0.003588450730478407 0.0035884507304783705 -Cytoplasm_AreaShape_Zernike_8_4 0.001596521357308922 0.5097792329261129 0.2926178605287711 -0.0015606591041984036 0.037071909930880734 0.05384286566879661 -0.0538428656687963 -Cytoplasm_AreaShape_Zernike_8_6 0.0024511655886019224 0.2811452603365874 0.5510692336093777 -0.0022950321101087743 0.03184765053510717 -0.014123752556755425 0.014123752556755558 -Cytoplasm_AreaShape_Zernike_8_8 0.0023878378749209173 0.2945966556464452 0.5307721877124598 -0.003804538066846134 0.06960391941405278 0.045772361561530035 -0.045772361561529584 -Cytoplasm_AreaShape_Zernike_9_1 0.010062470539738855 0.00033850358119946744 3.470436732334996 -0.00195379139899465 0.01128891885339868 -0.07715201861704793 0.0771520186170478 -Cytoplasm_AreaShape_Zernike_9_3 0.0006214273596650832 0.8644589281830433 0.06325563592065168 0.0010707097489291812 -0.024702901186262553 -0.033931856936258026 0.03393185693625782 -Cytoplasm_AreaShape_Zernike_9_5 0.00024343336737187027 0.9733039708451275 0.011751504804412465 0.00121062764921286 -0.021712917281225683 -0.012772628776978757 0.012772628776978618 -Cytoplasm_AreaShape_Zernike_9_7 0.00085930298869763 0.7774832717413938 0.10930894644075241 -0.0007174932145907056 0.006106450509730906 -0.020262057605748954 0.020262057605748937 -Cytoplasm_AreaShape_Zernike_9_9 0.0007003810085535189 0.8363914278888568 0.07759042688215764 0.0019985911415759455 -0.0380192275027919 -0.030033967134088443 0.030033967134088173 -Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928875 0.03426765582386959 1.4651156036990793 -0.005507817489283557 0.09900261499000954 0.059009099631753326 -0.059009099631752694 -Cytoplasm_Correlation_Correlation_DAPI_RFP 0.01647709493797045 6.594567361073445e-07 6.18081369122629 -0.006627974205122522 0.09667752833493706 -0.021433259287394056 0.0214332592873945 -Cytoplasm_Correlation_Correlation_GFP_RFP 0.00199587704315185 0.39006033120792405 0.40886821480870517 0.0018136544577989986 -0.04256969265267932 -0.06046439275583843 0.06046439275583807 -Cytoplasm_Correlation_Costes_DAPI_GFP 0.12668875220153752 1.1102230246251565e-16 15.954589770191003 -0.007711576775701956 0.22597520296845497 0.4421891625863014 -0.44218916258629926 -Cytoplasm_Correlation_Costes_DAPI_RFP 0.058169420512800385 1.1102230246251565e-16 15.954589770191003 -0.012945726387770452 0.27949855271431157 0.33132311992936303 -0.3313231199293609 -Cytoplasm_Correlation_Costes_GFP_DAPI 0.07171136556320179 1.1102230246251565e-16 15.954589770191003 -0.0027105789835034006 0.11578817655987528 0.3050791104063639 -0.30507911040636265 -Cytoplasm_Correlation_Costes_GFP_RFP 0.016622614806175218 5.709150631139082e-07 6.2434284983474075 -0.0014483839868720961 0.05829414008342612 0.14829604795815957 -0.14829604795815893 -Cytoplasm_Correlation_Costes_RFP_DAPI 0.19802434686833292 1.1102230246251565e-16 15.954589770191003 -0.019314341764655584 0.4442044078430728 0.6063003461727692 -0.6063003461727656 -Cytoplasm_Correlation_K_DAPI_GFP 0.008743794398715199 0.001175018238829817 2.9299553921482704 -0.007234226804901091 0.1290649328631211 0.07351383959747056 -0.07351383959746972 -Cytoplasm_Correlation_K_DAPI_RFP 0.006214930262296137 0.012005574295978816 1.9206170602959136 -0.0012012309935377358 0.0025386693453899184 -0.06555298649074703 0.0655529864907469 -Cytoplasm_Correlation_K_GFP_DAPI 0.005581927725120228 0.02110119235699781 1.6756930034969137 0.005338620229683115 -0.08979893093517345 -0.03183237383401087 0.031832373834010344 -Cytoplasm_Correlation_K_GFP_RFP 0.018068858300495982 1.3558242606404036e-07 6.867796599244995 0.0069266749065762115 -0.1513700131877782 -0.18477915531346473 0.18477915531346353 -Cytoplasm_Correlation_K_RFP_DAPI 0.008078073617189152 0.0021861367942233034 2.6603226662117576 -0.0019245395858671626 0.05666869649934925 0.11147939829574158 -0.11147939829574104 -Cytoplasm_Correlation_K_RFP_GFP 0.013895972690740388 8.368139717385148e-06 5.077371077283823 -0.007563196441933077 0.15446621286821482 0.15725015850203122 -0.1572501585020301 -Cytoplasm_Correlation_Manders_DAPI_GFP 0.05682677261087277 1.1102230246251565e-16 15.954589770191003 0.010518674516646192 -0.14244033562193323 0.07924108904246813 -0.07924108904246867 -Cytoplasm_Correlation_Manders_DAPI_RFP 0.005763126348429903 0.017972049149014624 1.7454024023916843 0.005868469798047356 -0.10457084029436119 -0.05910909782432623 0.05910909782432558 -Cytoplasm_Correlation_Manders_GFP_DAPI 0.0017588441984072167 0.45839917038707967 0.3387561770280998 -0.001973042743801615 0.04429604253452643 0.05748543996101668 -0.05748543996101633 -Cytoplasm_Correlation_Manders_GFP_RFP 0.004491751136942446 0.054405552358263165 1.2643567761197343 0.005191871384719637 -0.09629033638095433 -0.06783545372254793 0.06783545372254729 -Cytoplasm_Correlation_Manders_RFP_DAPI 0.00014556354045824893 0.9898029487911398 0.004451256685892387 -0.0003623708894618319 0.009384543768966584 0.015699018868684318 -0.015699018868684234 -Cytoplasm_Correlation_Manders_RFP_GFP 0.04845038492229414 1.1102230246251565e-16 15.954589770191003 0.012828051473727224 -0.1971131896075793 0.00032516819538896957 -0.00032516819538996877 -Cytoplasm_Correlation_Overlap_DAPI_GFP 0.005221386256011873 0.02897080599445301 1.5380394221564682 -0.0027778261739751877 0.03487271771463569 -0.03221917381704655 0.032219173817046656 -Cytoplasm_Correlation_Overlap_DAPI_RFP 0.044700483277687386 1.1102230246251565e-16 15.954589770191003 -0.010406894851813729 0.14855634474260931 -0.04699562327442729 0.046995623274427925 -Cytoplasm_Correlation_Overlap_GFP_RFP 0.021379891039952637 4.900020389442261e-09 8.309802112827846 0.004200186245216157 -0.1104968116943455 -0.18905254048020215 0.1890525404802012 -Cytoplasm_Correlation_RWC_DAPI_GFP 0.025089935195876323 1.1402523369952178e-10 9.94299902898127 0.010217635563995615 -0.1638920903800971 -0.028099911153720125 0.02809991115371921 -Cytoplasm_Correlation_RWC_DAPI_RFP 0.0003289541781874572 0.9539638694053856 0.020468073529671168 -0.0012842748998345597 0.02150149694848574 0.007242765248701214 -0.0072427652487010875 -Cytoplasm_Correlation_RWC_GFP_DAPI 0.004081527711480959 0.07693405431750933 1.1138813802448684 0.004567066900040596 -0.07683843676814384 -0.027303956378637917 0.027303956378637466 -Cytoplasm_Correlation_RWC_GFP_RFP 0.027099958811750424 1.4641954315663952e-11 10.834400952521928 0.011337522020371674 -0.22572106743274456 -0.2117292249742364 0.2117292249742348 -Cytoplasm_Correlation_RWC_RFP_DAPI 0.0011901726515266553 0.6526040608105218 0.18535022815446517 -0.002486853537587508 0.048560798433762334 0.042530077114726075 -0.04253007711472573 -Cytoplasm_Correlation_RWC_RFP_GFP 0.038262913928817754 1.1102230246251565e-16 15.954589770191003 0.013669864639138789 -0.22737150139842816 -0.0709547615938646 0.07095476159386327 -Cytoplasm_Granularity_10_DAPI 0.0008700166154410605 0.7734418488461786 0.1115723331276185 -0.0009939826604942938 0.011451554098633802 -0.015755423018955862 0.015755423018955883 -Cytoplasm_Granularity_10_GFP 0.00013034106753262442 0.9917393246479322 0.003602465692680761 -0.0008710848509915131 0.01643817208107074 0.012544977529985825 -0.012544977529985712 -Cytoplasm_Granularity_10_RFP 0.003761712566425768 0.1003252629539767 0.9985896933004639 0.003566809207567474 -0.05475771180547825 0.00029267731881536113 -0.0002926773188156387 -Cytoplasm_Granularity_11_DAPI 0.005180989848801887 0.030011757301464215 1.5227085742524284 0.005159648740914257 -0.08693348556675273 -0.03136190394869354 0.03136190394869302 -Cytoplasm_Granularity_11_GFP 0.002334802229619015 0.3062712118045383 0.5138938231150992 0.0036441557354518323 -0.0693886760335573 -0.05503383838597352 0.05503383838597304 -Cytoplasm_Granularity_11_RFP 0.008314081070003199 0.001755350747339346 2.75563609149773 0.002034919438439572 -0.015209613918705886 0.06614763428265089 -0.06614763428265082 -Cytoplasm_Granularity_12_DAPI 0.002751908698306993 0.22416741203247903 0.6494275219959033 0.0015234576059579715 -0.015570811666440941 0.03230072143294328 -0.03230072143294329 -Cytoplasm_Granularity_12_GFP 0.0041811951465038355 0.0707640952162436 1.1501870418115965 0.004441508030217465 -0.07326958888922164 -0.02055889880146168 0.020558898801461258 -Cytoplasm_Granularity_12_RFP 0.017034681919072536 3.7936129460014456e-07 6.420946981299408 -0.008626578937455836 0.17438701176770877 0.17196274333597086 -0.1719627433359696 -Cytoplasm_Granularity_13_DAPI 0.00011316907939706855 0.9936995207044121 0.002744919655069595 -0.0002066133200880411 0.006340149354769783 0.013023257545315216 -0.013023257545315154 -Cytoplasm_Granularity_13_GFP 0.007443841776081039 0.0039280912043984095 2.4058184371001814 -0.0011146522823604738 0.04158486225826788 0.10063682516764824 -0.1006368251676478 -Cytoplasm_Granularity_13_RFP 0.016534364292089876 6.230874355361848e-07 6.205451006139428 -0.0013168894588963963 0.05587144662175819 0.14664404819273713 -0.14664404819273652 -Cytoplasm_Granularity_14_DAPI 6.74822963525612e-05 0.9976889446982755 0.001004840133224023 -0.0006301956910982547 0.011832348481753779 0.008828744669552321 -0.00882874466955224 -Cytoplasm_Granularity_14_GFP 0.003157653441450581 0.16344816049010946 0.7866199627770374 0.0012739510257342985 -0.009754647163901897 0.040453425349254675 -0.04045342534925463 -Cytoplasm_Granularity_14_RFP 0.009850189170258372 0.0004140812515598391 3.382914432691137 0.004883378246378869 -0.0696969351968968 0.022103120120776937 -0.022103120120777253 -Cytoplasm_Granularity_15_DAPI 0.001494568548114028 0.5438506026940519 0.2645203858467868 -0.0018671589740721943 0.04159361108728209 0.0530616588214852 -0.053061658821484864 -Cytoplasm_Granularity_15_GFP 0.0020953718002516553 0.36378156158444064 0.4391593171061412 0.0008180119208344385 -0.0037533449524093307 0.03630713868305872 -0.036307138683058654 -Cytoplasm_Granularity_15_RFP 0.034125533803847086 1.0436096431476471e-14 13.981461916591305 -0.011784498254099763 0.24114273394955688 0.24692371787686065 -0.24692371787685888 -Cytoplasm_Granularity_16_DAPI 0.009731631679994024 0.0004633240051676868 3.3341151980878285 0.007226632721418378 -0.12324129960333816 -0.05002456605624575 0.050024566056245 -Cytoplasm_Granularity_16_GFP 0.01260736521877992 2.9356292132742823e-05 4.532298799164025 -0.008452697569135218 0.14675097611746143 0.0692158431896126 -0.06921584318961169 -Cytoplasm_Granularity_16_RFP 0.005325210338640973 0.02645273951490723 1.5775293446269791 -0.0011101117449085602 0.03812163510971825 0.08666965428945714 -0.08666965428945675 -Cytoplasm_Granularity_1_DAPI 0.07647272368069058 1.1102230246251565e-16 15.954589770191003 -0.010752801276124446 0.13590485293829288 -0.12095399806963847 0.12095399806963889 -Cytoplasm_Granularity_1_GFP 0.15862501658920913 1.1102230246251565e-16 15.954589770191003 -0.007985903521542373 0.04917729569929702 -0.3028579142581592 0.3028579142581588 -Cytoplasm_Granularity_1_RFP 0.07976624478880545 1.1102230246251565e-16 15.954589770191003 0.012888393237553602 -0.2919905507118198 -0.38636696621633326 0.3863669662163309 -Cytoplasm_Granularity_2_DAPI 0.013783964245781544 9.336330784925906e-06 5.029823769695859 -0.0037042151263353305 0.04061425434821721 -0.06720005161457258 0.06720005161457264 -Cytoplasm_Granularity_2_GFP 0.006351163769724888 0.010621467992987355 1.9738154552701275 0.003635692360195843 -0.05005999181607998 0.02398647681280286 -0.02398647681280306 -Cytoplasm_Granularity_2_RFP 0.026576123872976476 2.502187346209439e-11 10.601680176505537 0.005561917567722708 -0.06464110395108821 0.08584389442409998 -0.08584389442410012 -Cytoplasm_Granularity_3_DAPI 0.004861761699844425 0.03960374614767348 1.402263731894835 0.004423543232503832 -0.07044940311189657 -0.010087804927984799 0.010087804927984417 -Cytoplasm_Granularity_3_GFP 0.01887264431169 6.076666880794335e-08 7.216334570761154 0.006594393828064341 -0.09304280763759606 0.03426893446642305 -0.03426893446642344 -Cytoplasm_Granularity_3_RFP 0.024745318885012302 1.6195356167258979e-10 9.790609496571722 0.005818268563442486 -0.07133928172161376 0.07449391961705294 -0.07449391961705314 -Cytoplasm_Granularity_4_DAPI 0.015940070831693798 1.121782617685696e-06 5.950091293780658 0.006771419980777344 -0.10042848334601694 0.015070365217135381 -0.01507036521713586 -Cytoplasm_Granularity_4_GFP 0.022766489263839063 1.207181798967838e-09 8.918227321167707 0.0041116028593136435 -0.03952672763139582 0.09745171526141748 -0.09745171526141747 -Cytoplasm_Granularity_4_RFP 0.023750876205265503 4.4501646812022955e-10 9.351623917375452 0.00641299082068573 -0.08421325965461741 0.05913347799036496 -0.05913347799036526 -Cytoplasm_Granularity_5_DAPI 0.043594997218054043 1.1102230246251565e-16 15.954589770191003 0.008305024593644456 -0.10635608847387819 0.08770399290039052 -0.08770399290039087 -Cytoplasm_Granularity_5_GFP 0.029088368086080063 1.904143509534606e-12 11.720300323257069 0.0014414739012570178 0.016158379708807152 0.15770919852702148 -0.15770919852702106 -Cytoplasm_Granularity_5_RFP 0.015266931525493521 2.179214255049189e-06 5.661700068876637 0.0056152854233033475 -0.07717134890778499 0.037646689832150526 -0.03764668983215083 -Cytoplasm_Granularity_6_DAPI 0.0607240321011564 1.1102230246251565e-16 15.954589770191003 0.006749081233866624 -0.06521650352978633 0.158587549293555 -0.158587549293555 -Cytoplasm_Granularity_6_GFP 0.03022343115870063 5.919709167301335e-13 12.227699629449182 0.0030332732801674907 -0.012862294939943203 0.1389750198000551 -0.13897501980005486 -Cytoplasm_Granularity_6_RFP 0.01355835037552533 1.163737040010826e-05 4.934145142515971 0.00502437230617644 -0.06726114823369321 0.04104939308216804 -0.041049393082168296 -Cytoplasm_Granularity_7_DAPI 0.03495369952127292 4.440892098500626e-15 14.35252977886304 0.005885226583762701 -0.06441084938277948 0.10724740428558964 -0.10724740428558972 -Cytoplasm_Granularity_7_GFP 0.019948292873705586 2.068551185896439e-08 7.6843337281030895 0.0019686691251815432 -0.0010951178259406558 0.12005045438879482 -0.12005045438879455 -Cytoplasm_Granularity_7_RFP 0.0068161411470623046 0.006973486447476085 2.156550038783899 0.0014019186773225332 -0.005377735306957954 0.0665650786828842 -0.06656507868288407 -Cytoplasm_Granularity_8_DAPI 0.006686116364260197 0.007847358169824181 2.1052765248307685 0.005700215976006792 -0.09469842241273377 -0.029120209237943326 0.029120209237942778 -Cytoplasm_Granularity_8_GFP 0.012264198298360895 4.093536880866999e-05 4.387901292465122 0.00021515460100190924 0.0237754941544423 0.11147147107162804 -0.11147147107162766 -Cytoplasm_Granularity_8_RFP 0.009177395448536685 0.000781989528564675 3.1067990624350723 0.0013785958797820626 -0.001557026306560086 0.08081526953198814 -0.08081526953198798 -Cytoplasm_Granularity_9_DAPI 0.0018006015906264494 0.4457760730461524 0.350883245911628 0.001381631956859259 -0.03480658833294152 -0.05584599351166265 0.055845993511662355 -Cytoplasm_Granularity_9_GFP 0.0002442501560000476 0.9731392690021424 0.011825002043144236 0.0011424626512177264 -0.019459156032257653 -0.007809078746241701 0.007809078746241585 -Cytoplasm_Granularity_9_RFP 0.011876434659947144 5.95448596415471e-05 4.22515572411686 0.0020014519906748373 -0.009960290523731069 0.08563606979716416 -0.08563606979716402 -Cytoplasm_Intensity_IntegratedIntensityEdge_DAPI 0.09074831220092539 1.1102230246251565e-16 15.954589770191003 -0.004444088342414468 0.006948341648111311 -0.25257893861879144 0.25257893861879094 -Cytoplasm_Intensity_IntegratedIntensityEdge_GFP 0.04079393274337284 1.1102230246251565e-16 15.954589770191003 -0.000759580225869157 -0.03663110868606562 -0.19883033401424585 0.1988303340142452 -Cytoplasm_Intensity_IntegratedIntensityEdge_RFP 0.04104147950050285 1.1102230246251565e-16 15.954589770191003 0.0024254421868306125 -0.09425013639702673 -0.23447036441487235 0.23447036441487135 -Cytoplasm_Intensity_IntegratedIntensity_DAPI 0.1554375053275413 1.1102230246251565e-16 15.954589770191003 -0.012157030169902303 0.1308535288442052 -0.2305906753237856 0.23059067532378574 -Cytoplasm_Intensity_IntegratedIntensity_GFP 0.11956541883270178 1.1102230246251565e-16 15.954589770191003 -0.012227964537080806 0.14564272803999587 -0.17420717518530124 0.17420717518530157 -Cytoplasm_Intensity_IntegratedIntensity_RFP 0.07838382358195373 1.1102230246251565e-16 15.954589770191003 -0.00596026842431629 0.04118740161995041 -0.20758173416850012 0.20758173416849984 -Cytoplasm_Intensity_LowerQuartileIntensity_DAPI 0.014553303920371352 4.3953341651858935e-06 5.357008101118225 0.0036484134858484566 -0.03863974943131641 0.07179642895304558 -0.07179642895304561 -Cytoplasm_Intensity_LowerQuartileIntensity_GFP 0.03770666943969847 2.220446049250313e-16 15.653559774527022 0.010200064641846982 -0.14991194752441986 0.028329868568107014 -0.02832986856810772 -Cytoplasm_Intensity_LowerQuartileIntensity_RFP 0.011707491316389373 7.008198051117187e-05 4.154393633539182 0.008057327154158675 -0.13882089342803133 -0.06159118896165591 0.06159118896165505 -Cytoplasm_Intensity_MADIntensity_DAPI 0.013718651254554737 9.951470053781719e-06 5.002112759546983 0.006387903243840341 -0.09542764483193951 0.011388396072504761 -0.011388396072505226 -Cytoplasm_Intensity_MADIntensity_GFP 0.07166378293270292 1.1102230246251565e-16 15.954589770191003 0.01198346024822436 -0.16345760569313267 0.08541231375718952 -0.08541231375719019 -Cytoplasm_Intensity_MADIntensity_RFP 0.012011050592877326 5.2287397473871344e-05 4.281602973983402 0.006334518677642407 -0.09699655002308805 0.0015532221785253264 -0.0015532221785258121 -Cytoplasm_Intensity_MassDisplacement_DAPI 0.00886120762609699 0.0010525640419272175 2.9777514705901957 0.007120168632143353 -0.12406916271993065 -0.060167998712748236 0.06016799871274746 -Cytoplasm_Intensity_MassDisplacement_GFP 0.00556448819325972 0.021428816484061586 1.6690018144287744 -0.004990086178516455 0.08132940861131864 0.019024074373029387 -0.01902407437302893 -Cytoplasm_Intensity_MassDisplacement_RFP 0.006852608602759824 0.006745998096407746 2.1709537856964363 -0.006322554247717586 0.11110228452027782 0.057262190558235324 -0.057262190558234616 -Cytoplasm_Intensity_MaxIntensityEdge_DAPI 0.06810525005759416 1.1102230246251565e-16 15.954589770191003 0.014220669949970578 -0.21236137947177486 0.025675577860318183 -0.02567557786031921 -Cytoplasm_Intensity_MaxIntensityEdge_GFP 0.03239381874950997 6.294964549624638e-14 13.201006711298097 0.005042012937953965 -0.04982142789263744 0.11394644079452246 -0.11394644079452246 -Cytoplasm_Intensity_MaxIntensityEdge_RFP 0.004106218124925509 0.0753599505618121 1.1228593951241392 0.004526237691030109 -0.07568433577310824 -0.025137008864301785 0.02513700886430134 -Cytoplasm_Intensity_MaxIntensity_DAPI 0.05671586319590616 1.1102230246251565e-16 15.954589770191003 0.013632772310371214 -0.207901491607797 0.006835884994328578 -0.006835884994329633 -Cytoplasm_Intensity_MaxIntensity_GFP 0.004733757463202259 0.04422435137312897 1.3543385280538902 -0.0033528450070647647 0.07449157230111961 0.09446833667475357 -0.09446833667475298 -Cytoplasm_Intensity_MaxIntensity_RFP 0.0006338886393454368 0.8601030189140192 0.0654495279815697 0.000867179392635046 -0.010145354175864419 0.013108813720948324 -0.013108813720948347 -Cytoplasm_Intensity_MeanIntensityEdge_DAPI 0.0587537591589149 1.1102230246251565e-16 15.954589770191003 0.011744877813830408 -0.16644041823891756 0.058040087260296774 -0.058040087260297496 -Cytoplasm_Intensity_MeanIntensityEdge_GFP 0.08026369120292831 1.1102230246251565e-16 15.954589770191003 0.012877894806624683 -0.17699996947467123 0.08626366121681703 -0.08626366121681775 -Cytoplasm_Intensity_MeanIntensityEdge_RFP 0.021559538475623863 4.088002825142212e-09 8.388488812740455 0.01099283392349338 -0.1900924928704129 -0.08689243663200796 0.0868924366320068 -Cytoplasm_Intensity_MeanIntensity_DAPI 0.030131208203921234 6.509237593377293e-13 12.186469876006207 0.00768133632616821 -0.1041693197495304 0.05724379082078092 -0.05724379082078132 -Cytoplasm_Intensity_MeanIntensity_GFP 0.08804221384129063 1.1102230246251565e-16 15.954589770191003 0.010740424699980494 -0.12989619705322963 0.1449022256858234 -0.14490222568582373 -Cytoplasm_Intensity_MeanIntensity_RFP 0.01313440510625452 1.7591094030544596e-05 4.754707149931198 0.007751207324838775 -0.12695871211765916 -0.0321353408029062 0.03213534080290548 -Cytoplasm_Intensity_MedianIntensity_DAPI 0.015419001670353483 1.8759892819186774e-06 5.726769647202622 0.004640688618996311 -0.057269443410513834 0.057898990314582705 -0.057898990314582864 -Cytoplasm_Intensity_MedianIntensity_GFP 0.0692924608478962 1.1102230246251565e-16 15.954589770191003 0.012930697563938236 -0.18441005215685038 0.05910500716864707 -0.05910500716864789 -Cytoplasm_Intensity_MedianIntensity_RFP 0.012489363068238446 3.2914871870848295e-05 4.482607831165609 0.007782812365243682 -0.12923380328518747 -0.039499835886846246 0.0394998358868455 -Cytoplasm_Intensity_MinIntensityEdge_DAPI 0.014076177206968077 7.015665123066839e-06 5.153931149213729 0.0037061756774387916 -0.040314354801024126 0.06855846581314838 -0.06855846581314842 -Cytoplasm_Intensity_MinIntensityEdge_GFP 0.012277476052323655 4.041271649979361e-05 4.3934819557632805 0.005915994006594782 -0.08756955783684221 0.01387410082605469 -0.013874100826055093 -Cytoplasm_Intensity_MinIntensityEdge_RFP 0.03341532585856355 2.1871393585115584e-14 13.66012354402941 0.014153471402013677 -0.25283874146627144 -0.14517866155352097 0.1451786615535193 -Cytoplasm_Intensity_MinIntensity_DAPI 0.01449645631975438 4.6474548969221985e-06 5.332784816319454 0.003702418802660505 -0.039761291975851 0.07059714391824534 -0.07059714391824537 -Cytoplasm_Intensity_MinIntensity_GFP 0.012555621103639836 3.086708527832993e-05 4.510504378205029 0.0057738842375740845 -0.0841428089494925 0.018987109723054577 -0.018987109723054972 -Cytoplasm_Intensity_MinIntensity_RFP 0.04590967490197184 1.1102230246251565e-16 15.954589770191003 0.01398622772945371 -0.22549653890483762 -0.04322119443259026 0.04322119443258898 -Cytoplasm_Intensity_StdIntensityEdge_DAPI 0.04264827762132595 1.1102230246251565e-16 15.954589770191003 0.010399200283436424 -0.14999298919195142 0.04059563814639207 -0.04059563814639276 -Cytoplasm_Intensity_StdIntensityEdge_GFP 0.0496578769920234 1.1102230246251565e-16 15.954589770191003 0.00637733782955769 -0.06430744530812765 0.1388089390367879 -0.13880893903678793 -Cytoplasm_Intensity_StdIntensityEdge_RFP 0.00938462223738068 0.0006432341403108666 3.1916309130233875 0.005507352654024755 -0.08374299004935687 0.0037692613774840955 -0.0037692613774845188 -Cytoplasm_Intensity_StdIntensity_DAPI 0.08310467571203939 1.1102230246251565e-16 15.954589770191003 0.01689827878625026 -0.2602898423537952 -0.002182733708075535 0.0021827337080742026 -Cytoplasm_Intensity_StdIntensity_GFP 0.05037052358742766 1.1102230246251565e-16 15.954589770191003 0.0026391535480969103 0.0074315063494072575 0.19756718592420863 -0.19756718592420813 -Cytoplasm_Intensity_StdIntensity_RFP 0.012707827870779731 2.6629636778685573e-05 4.574634757184354 0.0053962153571932 -0.07603812555555857 0.02845038870206563 -0.02845038870206596 -Cytoplasm_Intensity_UpperQuartileIntensity_DAPI 0.022291565322968165 1.951829364799096e-09 8.709558152430272 0.006842550853894068 -0.09442695479363164 0.04427278205926197 -0.044272782059262335 -Cytoplasm_Intensity_UpperQuartileIntensity_GFP 0.08461426517473747 1.1102230246251565e-16 15.954589770191003 0.010122646565784125 -0.11926781409087528 0.149561175059289 -0.1495611750592893 -Cytoplasm_Intensity_UpperQuartileIntensity_RFP 0.012365036276811736 3.712815934120428e-05 4.430296580743485 0.0074269157328729 -0.12095996474460094 -0.027962791313501267 0.027962791313500587 -Cytoplasm_Location_CenterMassIntensity_X_DAPI 0.000820971422202943 0.7918911021386273 0.10133453683470356 0.0017187153484721279 -0.026731761212607084 -0.0012831512912200235 0.0012831512912198813 -Cytoplasm_Location_CenterMassIntensity_X_GFP 0.0007880425228913301 0.8041867100453004 0.0946431083055802 0.0016993933523018671 -0.02653326978131224 -0.0016886762440684912 0.0016886762440683524 -Cytoplasm_Location_CenterMassIntensity_X_RFP 0.0007866405503756946 0.804708211979182 0.094361566712008 0.0017169009877283459 -0.026932631698232883 -0.0022247178774269377 0.0022247178774267955 -Cytoplasm_Location_CenterMassIntensity_Y_DAPI 0.0006408890857526606 0.8576427977233153 0.06669355513345246 0.0018432529914507187 -0.03581950078650076 -0.030808274578812658 0.0308082745788124 -Cytoplasm_Location_CenterMassIntensity_Y_GFP 0.000630036751665175 0.861452721114533 0.06476855283291275 0.001851253218543876 -0.03573450881099232 -0.02995227712064781 0.02995227712064756 -Cytoplasm_Location_CenterMassIntensity_Y_RFP 0.0006567480315420315 0.8520359307733876 0.06954209043566768 0.001867607258949424 -0.03627638484695045 -0.03114788705161499 0.03114788705161473 -Cytoplasm_Location_Center_X 0.0008253840774573584 0.790237195752141 0.10224253232386624 0.0017318404680637819 -0.026991759598672196 -0.00152286257861017 0.0015228625786100278 -Cytoplasm_Location_Center_Y 0.0006476256396724667 0.8552666776527195 0.06789844834204918 0.0018503618710664477 -0.03598224063554693 -0.03102832323811212 0.031028323238111867 -Cytoplasm_Location_MaxIntensity_X_DAPI 0.0008102679022686798 0.7958971037472358 0.09914307568358736 0.0016134144511718925 -0.024501895326170774 0.0012324545692643588 -0.0012324545692644871 -Cytoplasm_Location_MaxIntensity_X_GFP 0.0010559337017417292 0.7029700284216278 0.15306319100945093 0.0016145749902432179 -0.023146927242081557 0.006882855592612475 -0.006882855592612579 -Cytoplasm_Location_MaxIntensity_X_RFP 0.0006511994566603674 0.854002759221398 0.068540726134541 0.001704475947791918 -0.027760951289280897 -0.006420169001206094 0.006420169001205938 -Cytoplasm_Location_MaxIntensity_Y_DAPI 0.00050560247482212 0.9032356962374724 0.04419890725157962 0.001674719238827211 -0.03214646273319606 -0.026353401283591094 0.026353401283590868 -Cytoplasm_Location_MaxIntensity_Y_GFP 0.0005274894648467088 0.896176157872141 0.04760661436439698 0.0016764339782516368 -0.03253686372015717 -0.02785177838533706 0.02785177838533683 -Cytoplasm_Location_MaxIntensity_Y_RFP 0.0005174445711179088 0.8994334515678626 0.04603096426510588 0.001771619889372523 -0.03228258508939866 -0.020782759203354064 0.020782759203353852 -Cytoplasm_Number_Object_Number 0.14301412323815854 1.1102230246251565e-16 15.954589770191003 0.022307121678997385 -0.34451192107504147 -0.006618097534413614 0.00661809753441181 -Cytoplasm_RadialDistribution_FracAtD_DAPI_1of4 0.003361343947605122 0.13894394944824495 0.8571603605703313 -0.0038471267348392534 0.062475444501208253 0.013737364173246239 -0.013737364173245885 -Cytoplasm_RadialDistribution_FracAtD_DAPI_2of4 0.005092860203852467 0.03240929769388312 1.4893303799821647 -0.004191348130091957 0.0646134913945494 0.0007585235814501143 -0.0007585235814497812 -Cytoplasm_RadialDistribution_FracAtD_DAPI_3of4 0.005910220770351193 0.01576759506340597 1.8022345419494714 -0.0029871531658937755 0.0377387103536038 -0.03366704721338188 0.033667047213382 -Cytoplasm_RadialDistribution_FracAtD_DAPI_4of4 0.005555931879330678 0.021591354432315857 1.665720113384583 0.003473504725296112 -0.048323743178682634 0.020871151654035445 -0.020871151654035643 -Cytoplasm_RadialDistribution_FracAtD_GFP_1of4 0.011669413333983969 7.270143519388927e-05 4.138457015679693 0.0026459410605297823 -0.02253950236818622 0.07463762043670237 -0.07463762043670231 -Cytoplasm_RadialDistribution_FracAtD_GFP_2of4 0.009813025873182335 0.0004289321896713272 3.3676113604476736 0.002180057002818877 -0.015935431054491286 0.07234307982341287 -0.07234307982341279 -Cytoplasm_RadialDistribution_FracAtD_GFP_3of4 0.004102175777975892 0.07561556856512308 1.1213887779666956 0.0013376081976134288 -0.008927092813977654 0.047887189058019246 -0.04788718905801918 -Cytoplasm_RadialDistribution_FracAtD_GFP_4of4 0.006744859303470352 0.007440038807423943 2.1284247991576 -0.0018028801838774354 0.013124923483917694 -0.06004695708079005 0.060046957080789984 -Cytoplasm_RadialDistribution_FracAtD_RFP_1of4 0.0033651275081684195 0.13852233301586725 0.8584802026020388 0.0029849522202331138 -0.043472453006183714 0.00992802592856867 -0.009928025928568868 -Cytoplasm_RadialDistribution_FracAtD_RFP_2of4 0.0029756728426880708 0.1885793123074293 0.7245059523384797 0.0030269409493874654 -0.045555085946232754 0.0040126553094279455 -0.0040126553094281675 -Cytoplasm_RadialDistribution_FracAtD_RFP_3of4 0.0016069979523801736 0.5063546830315826 0.295545169084659 0.0026992343448554255 -0.0441222182551672 -0.01082377131321332 0.010823771313213073 -Cytoplasm_RadialDistribution_FracAtD_RFP_4of4 0.002183159501151888 0.34176619198580893 0.46627090053920595 -0.0029029977041593385 0.04582489762153613 0.004939776520357625 -0.004939776520357382 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_1of4 0.026150670356733796 3.864764064331894e-11 10.412877013695548 -0.008935160942624025 0.13422849476277818 -0.012851504006392026 0.012851504006392678 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_2of4 0.03965755680045435 1.1102230246251565e-16 15.954589770191003 -0.009920217284806404 0.1423855120796844 -0.041602288888080874 0.04160228888808151 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_3of4 0.04057281266738688 1.1102230246251565e-16 15.954589770191003 -0.010489485466232056 0.15357983609240033 -0.03154472777497755 0.03154472777497826 -Cytoplasm_RadialDistribution_MeanFrac_DAPI_4of4 0.044791648164616116 1.1102230246251565e-16 15.954589770191003 0.009696631090553038 -0.1337942566092568 0.0628170990401274 -0.06281709904012797 -Cytoplasm_RadialDistribution_MeanFrac_GFP_1of4 0.010678140592266683 0.00018824421528584878 3.725278360721077 0.0015394158165649506 -0.0026654951419884665 0.08642796718848064 -0.08642796718848046 -Cytoplasm_RadialDistribution_MeanFrac_GFP_2of4 0.011134866653659148 0.00012155341731501235 3.915232827028868 0.001326901175847777 0.0018830671000312216 0.091703774229858 -0.09170377422985776 -Cytoplasm_RadialDistribution_MeanFrac_GFP_3of4 0.0072789316585334385 0.004570214905069592 2.3400633776344284 -0.0003629546611953275 0.0279208038458012 0.09195630351671882 -0.09195630351671848 -Cytoplasm_RadialDistribution_MeanFrac_GFP_4of4 0.01655567651677259 6.100688976218294e-07 6.214621115534621 -0.002868328657810678 0.021401302950388335 -0.09339287243730349 0.09339287243730338 -Cytoplasm_RadialDistribution_MeanFrac_RFP_1of4 0.004173096156914413 0.0712474758032654 1.1472305174827089 0.004240200820155366 -0.06852958494381998 -0.013786033016795228 0.013786033016794846 -Cytoplasm_RadialDistribution_MeanFrac_RFP_2of4 0.008471313552165682 0.001515980512774795 2.819306381321887 0.006151984403994113 -0.10022950091067186 -0.023302386966207497 0.023302386966206942 -Cytoplasm_RadialDistribution_MeanFrac_RFP_3of4 0.007522140000364108 0.0036550923893630127 2.437101640954156 0.00499331799321581 -0.07633307854769958 0.001745020723683162 -0.0017450207236835436 -Cytoplasm_RadialDistribution_MeanFrac_RFP_4of4 0.012119645564204196 4.707829976413613e-05 4.3271792301189 -0.00722066434214228 0.11666121379127067 0.023318917228897035 -0.023318917228896383 -Cytoplasm_RadialDistribution_RadialCV_DAPI_1of4 0.002401462940082033 0.29165797993695763 0.5351261365143526 0.0037411443341174833 -0.06571237689461594 -0.033765916075393546 0.03376591607539312 -Cytoplasm_RadialDistribution_RadialCV_DAPI_2of4 0.006110070736542683 0.013189192685102435 1.8797817869506401 0.00452097499980671 -0.06924609896135869 0.0010294462891734107 -0.0010294462891737646 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4.5811707679810354e-05 0.9989190347815181 0.00046971114598410267 0.00019973125440887144 -0.002070845077488131 0.00411353342193947 -0.004113533421939472 -Nuclei_AreaShape_Zernike_5_5 0.019830958821057676 2.3270236848560444e-08 7.63319919636521 0.0001337996109911715 0.032787439269349405 0.14341679947314426 -0.14341679947314376 -Nuclei_AreaShape_Zernike_6_0 0.0007471168274580942 0.8193316008486865 0.08654029448816644 0.0013362396311054825 -0.02965988210938944 -0.03753441285281983 0.037534412852819596 -Nuclei_AreaShape_Zernike_6_2 0.029734083066550787 9.798828415341632e-13 12.00882584707983 -0.0011754957513480176 -0.021560030517760315 -0.16311362912230126 0.1631136291223008 -Nuclei_AreaShape_Zernike_6_4 0.0035536520460041077 0.11894335465255068 0.9246598169167303 0.0007439301434207677 -0.02826882695079487 -0.06928438926714381 0.0692843892671435 -Nuclei_AreaShape_Zernike_6_6 2.50764301990003e-05 0.9996714873598254 0.0001426946666476718 -0.0003897786776107449 0.007142028724260379 0.0047348575496101285 -0.004734857549610082 -Nuclei_AreaShape_Zernike_7_1 0.003519921282360894 0.12224735474581283 0.912760529600874 6.924406406573475e-05 0.013578412351945054 0.060269072353062794 -0.06026907235306259 -Nuclei_AreaShape_Zernike_7_3 0.0007192527460109233 0.8295367449974073 0.08116437179011264 -0.001270536014808176 0.02846539973590599 0.03677506413711878 -0.03677506413711855 -Nuclei_AreaShape_Zernike_7_5 0.0011631827702947017 0.6626492239396506 0.17871630631801455 0.0006576303240817498 -0.0037116077004293244 0.026331566723195178 -0.026331566723195136 -Nuclei_AreaShape_Zernike_7_7 0.019713577047595132 2.6177965350981935e-08 7.5820641114590615 0.0006775549911709987 0.022714649896239725 0.13636121525883144 -0.13636121525883102 -Nuclei_AreaShape_Zernike_8_0 0.00515943632895588 0.03058181502936419 1.5145367428005665 0.00022960526192611355 0.013771221160992969 0.07120873227103117 -0.07120873227103093 -Nuclei_AreaShape_Zernike_8_2 0.005360731801971608 0.02564079473606673 1.5910685179933395 -0.002044356818723711 0.05433208251418964 0.09428117584579158 -0.0942811758457911 -Nuclei_AreaShape_Zernike_8_4 0.011680184626744916 7.195076015331647e-05 4.142964613420957 -0.0010667467572158902 -0.007388281797043872 -0.09790293613196419 0.09790293613196394 -Nuclei_AreaShape_Zernike_8_6 0.017554792524315355 2.262317444490236e-07 6.645446455680449 -0.0014701863717170832 -0.006028851207787678 -0.11783326115499446 0.11783326115499416 -Nuclei_AreaShape_Zernike_8_8 0.00632517219989337 0.010872928509935376 1.9636534674673476 -0.0015483558874261312 0.047463678380535085 0.09739323491060192 -0.09739323491060145 -Nuclei_AreaShape_Zernike_9_1 0.00024044337763462487 0.97390335498731 0.011484138068206893 -0.0005916732907774558 0.01415773496227949 0.02083722288819543 -0.020837222888195308 -Nuclei_AreaShape_Zernike_9_3 2.9651386554641057e-05 0.9995421215616721 0.00019889961854742822 0.00010289661901553104 -0.00319552634666752 -0.006642329772108126 0.006642329772108095 -Nuclei_AreaShape_Zernike_9_5 0.00014080482818745743 0.990427938907513 0.004177117170663428 0.0006605600038711619 -0.009954950570970681 0.0008196743643961906 -0.0008196743643962383 -Nuclei_AreaShape_Zernike_9_7 0.007022813546831985 0.00577680964152727 2.2383119431728646 -0.0009436291541522557 0.0379295023331338 0.09641221711911085 -0.09641221711911044 -Nuclei_AreaShape_Zernike_9_9 0.02581302663376739 5.455358387251863e-11 10.263176713362858 0.0031595675884058196 -0.01865764072562834 0.1231123514055964 -0.1231123514055962 -Nuclei_Correlation_Correlation_DAPI_GFP 0.005597584936572808 0.020811189718118017 1.6817030916651352 -0.0027688599181534103 0.06689736078036237 0.10015977946118539 -0.10015977946118482 -Nuclei_Correlation_Correlation_DAPI_RFP 0.01684321135815714 4.5875122578475924e-07 6.338422762302043 0.00014205156282761847 -0.03504904594788899 -0.13527231508033608 0.13527231508033558 -Nuclei_Correlation_Correlation_GFP_RFP 0.03244978474841076 5.939693181744587e-14 13.226235988169774 0.0012480577727552586 0.022152138374648023 0.17014171885326856 -0.17014171885326806 -Nuclei_Correlation_Costes_DAPI_GFP 0.09173667996128732 1.1102230246251565e-16 15.954589770191003 -0.014885198848901255 0.3298836164767035 0.415994629305783 -0.41599462930578046 -Nuclei_Correlation_Costes_DAPI_RFP 0.06537225212561171 1.1102230246251565e-16 15.954589770191003 -0.015553756747073814 0.32322078607538285 0.3462710611474306 -0.34627106114742817 -Nuclei_Correlation_Costes_GFP_RFP 0.0027040154606116085 0.23250973868569336 0.6335588519401313 -0.001439092468555444 0.03836944046694378 0.06687496308107685 -0.0668749630810765 -Nuclei_Correlation_Costes_RFP_DAPI 0.017914963514199522 1.580573694148768e-07 6.801185250399516 -0.006922633136111468 0.15111623459086815 0.18399033989712438 -0.18399033989712318 -Nuclei_Correlation_K_DAPI_GFP 0.016042896725708022 1.0133859220662345e-06 5.994225133212328 -0.0052536283477645105 0.1225024387479125 0.1718156460245471 -0.17181564602454608 -Nuclei_Correlation_K_DAPI_RFP 0.019441454867166308 3.4387600544860675e-08 7.46359812677996 -0.0011421086465465947 -0.013884442015368537 -0.12940892635109447 0.12940892635109408 -Nuclei_Correlation_K_GFP_DAPI 0.02853871792349083 3.3494318429916348e-12 11.475028855166148 0.00735535031040551 -0.1690029733733402 -0.23023310589244492 0.23023310589244353 -Nuclei_Correlation_K_GFP_RFP 0.05040048534892727 1.1102230246251565e-16 15.954589770191003 0.0043533599195334045 -0.1336791624394929 -0.27477849813696786 0.2747784981369666 -Nuclei_Correlation_K_RFP_DAPI 0.009430603278008487 0.0006159131487293301 3.210480524340274 -0.002583265883233282 0.06988113874850571 0.12418339808002987 -0.12418339808002923 -Nuclei_Correlation_K_RFP_GFP 0.03224566862605527 7.338574192772285e-14 13.134388310705363 -0.0056629689720058335 0.1442133254364107 0.23527742975558208 -0.2352774297555808 -Nuclei_Correlation_Manders_DAPI_GFP 0.005351200880022611 0.025856256193626193 1.5874343577291878 0.0030718253235459073 -0.040536345719937306 0.027509351130101313 -0.02750935113010146 -Nuclei_Correlation_Manders_DAPI_RFP 0.0003548282703975669 0.9473520293555765 0.023488610228310218 0.0012507589608186007 -0.02524297737417154 -0.024763048399490204 0.02476304839949002 -Nuclei_Correlation_Manders_GFP_DAPI 0.0007620641436754116 0.8138200273288702 0.08947162678134138 -0.0012366792775270917 0.02759340277142216 0.0353280921836848 -0.03532809218368459 -Nuclei_Correlation_Manders_GFP_RFP 0.00015630051627069097 0.9883283746167367 0.005098735989355069 0.0009610455497743142 -0.018006054342069863 -0.013306471391877578 0.013306471391877454 -Nuclei_Correlation_Manders_RFP_DAPI 0.0006703475639939782 0.8471927376411222 0.07201777577109153 -0.0010841604293917255 0.02468159081864272 0.03299311870233575 -0.03299311870233554 -Nuclei_Correlation_Manders_RFP_GFP 0.005751006520582913 0.018166477203218068 1.7407292818022488 0.0028870574000985994 -0.03603297382321839 0.03435469487790527 -0.03435469487790538 -Nuclei_Correlation_Overlap_DAPI_GFP 0.018459160769271343 9.185241267584132e-08 7.036909431649485 -0.00443332948399178 0.049885068339613335 -0.07517303529196966 0.07517303529196974 -Nuclei_Correlation_Overlap_DAPI_RFP 0.03332433089660414 2.398081733190338e-14 13.620136019040073 -0.005960273208160103 0.06709695127398127 -0.10093976532691251 0.10093976532691264 -Nuclei_Correlation_Overlap_GFP_RFP 0.00073429097986355 0.8240407973909194 0.08405128630877821 -0.0015076284699692322 0.032202164809197985 0.03715451813273144 -0.03715451813273119 -Nuclei_Correlation_RWC_DAPI_GFP 0.018989196256275598 5.4080493305797006e-08 7.266959355546295 -0.0038782622974246196 0.10277732741182427 0.17764773207388912 -0.17764773207388818 -Nuclei_Correlation_RWC_DAPI_RFP 0.009434583039872435 0.0006136031128202157 3.212112445869353 -0.006499702588718633 0.10594837765705775 0.024840801513932337 -0.024840801513931754 -Nuclei_Correlation_RWC_GFP_DAPI 0.023339770229140133 6.753302361772739e-10 9.170483805424231 -0.003969839060068012 0.10833010988950581 0.1947086060182426 -0.1947086060182416 -Nuclei_Correlation_RWC_GFP_RFP 0.004164282860392388 0.07177703368616084 1.1440144936253933 0.0020737447585981008 -0.05247387074328527 -0.08477350111511209 0.08477350111511163 -Nuclei_Correlation_RWC_RFP_DAPI 0.007795913033751156 0.0028392756370917427 2.54679244408663 -0.006734540440612779 0.1181910975788669 0.060372943591185446 -0.06037294359118471 -Nuclei_Correlation_RWC_RFP_GFP 0.0017896074019385644 0.44907556007311955 0.34768057979411443 0.001771699968436085 -0.04119039602238776 -0.057441749082049326 0.057441749082048986 -Nuclei_Granularity_10_DAPI 0.03668829423261344 7.771561172376096e-16 15.109491730176746 -0.007848174440735997 0.10219245332996524 -0.07593632825175248 0.07593632825175284 -Nuclei_Granularity_10_GFP 0.003949145384552932 0.08591554937566015 1.0659282285213216 -0.0028548844585144536 0.06476519799142932 0.08594113395401158 -0.08594113395401105 -Nuclei_Granularity_10_RFP 0.0018661188425411757 0.4264716484881157 0.37010983510474 0.0028639162544017364 -0.046490115866486374 -0.010150465336355344 0.01015046533635509 -Nuclei_Granularity_11_DAPI 0.014629219072743727 4.079738795015331e-06 5.389367641693184 -0.0044510786960680225 0.054402810263315574 -0.05770125420443894 0.05770125420443909 -Nuclei_Granularity_11_GFP 0.000562777541660453 0.8845137613147607 0.053295405923762904 0.000489301043873812 -0.00318927900802609 0.017831231764757868 -0.017831231764757843 -Nuclei_Granularity_11_RFP 0.005601917417225266 0.020731628165466476 1.6833665891019574 0.0023808982989989517 -0.026273818364219128 0.04249809972792952 -0.04249809972792956 -Nuclei_Granularity_12_DAPI 0.013958640425660085 7.87069940200169e-06 5.103986673875511 -0.004934532232575382 0.06492807077545801 -0.04496801996733666 0.04496801996733689 -Nuclei_Granularity_12_GFP 0.0020619278379977457 0.37245663764842774 0.4289242816437531 0.0016479988294947291 -0.01996498357975364 0.022094289250755503 -0.02209428925075555 -Nuclei_Granularity_12_RFP 0.007634120014861412 0.0032967736864077946 2.4819108648428165 -0.006094006978899385 0.12071717877030566 0.11129677210627444 -0.11129677210627356 -Nuclei_Granularity_13_DAPI 0.005521892653970961 0.022249876312908645 1.6526723989199288 -0.00384454084415529 0.056126829422292746 -0.012229574337466943 0.012229574337467203 -Nuclei_Granularity_13_GFP 0.009742962256615528 0.00045837783174373925 3.3387763940602335 -0.004799820279587066 0.10670736969818576 0.13551649984596947 -0.13551649984596864 -Nuclei_Granularity_13_RFP 0.012360202993951352 3.730234644860886e-05 4.428263848683771 0.00014286837896644263 0.02520510850973723 0.11278212785858613 -0.11278212785858574 -Nuclei_Granularity_14_DAPI 0.004806886792936393 0.0415249084402608 1.3816913164667748 -0.0038128907342353576 0.05716419704399471 -0.005957320934663467 0.005957320934663744 -Nuclei_Granularity_14_GFP 0.001614847416495535 0.5037984675510914 0.29774315786830874 -0.0013564537083510827 0.03376451321867903 0.05314990043250408 -0.0531499004325038 -Nuclei_Granularity_14_RFP 0.007009569639603308 0.005847051847339579 2.233063055142766 0.004443501875467801 -0.06560264886743451 0.011123952531007057 -0.011123952531007363 -Nuclei_Granularity_15_DAPI 0.0032526783758627387 0.15156524019880768 0.8194003878910774 -0.0043385790376858375 0.07596126334452565 0.038150009775234764 -0.03815000977523429 -Nuclei_Granularity_15_GFP 0.0017140693020247788 0.47220796786015895 0.3258666890732496 -0.002107203000790418 0.04621797206145567 0.056907662153437163 -0.0569076621534368 -Nuclei_Granularity_15_RFP 0.026940563786350347 1.7236212457305555e-11 10.763558161342386 -0.010847913166710122 0.21929660202160234 0.2162640838240863 -0.21626408382408466 -Nuclei_Granularity_16_DAPI 0.0027265171841915903 0.228557121652329 0.6410052419041976 0.0038431383801131383 -0.06572439431939497 -0.02736220531452524 0.027362205314524837 -Nuclei_Granularity_16_GFP 0.015948990208157654 1.11193974250412e-06 5.953918747112794 -0.008908762271943848 0.14886830595906236 0.0490753947490683 -0.04907539474906743 -Nuclei_Granularity_16_RFP 0.0046561730076774 0.04727155614209422 1.325400100832253 -0.001404465102767419 0.04204230328829017 0.08418314005903807 -0.08418314005903767 -Nuclei_Granularity_1_DAPI 0.020977225940932653 7.352081721201387e-09 8.13358967424578 -0.0061993898282064475 0.08264202711359045 -0.052086027277638045 0.05208602727763834 -Nuclei_Granularity_1_GFP 0.0963245352604084 1.1102230246251565e-16 15.954589770191003 0.001185584801777664 -0.09913740068443005 -0.3330319782704106 0.3330319782704093 -Nuclei_Granularity_1_RFP 0.06840416553554762 1.1102230246251565e-16 15.954589770191003 0.010834064780548755 -0.25272625444244134 -0.35473487068954035 0.35473487068953824 -Nuclei_Granularity_2_DAPI 0.0037835117576389132 0.09854018866650816 1.006386610553651 -0.0014110090285422699 0.04040328729525307 0.07702300614062783 -0.07702300614062745 -Nuclei_Granularity_2_GFP 0.0010490319408842375 0.705581420050845 0.15145286392446958 -3.86207481008494e-05 -0.007397748915126498 -0.0328922635683254 0.03289226356832529 -Nuclei_Granularity_2_RFP 0.007522411395353146 0.0036541793301019254 2.437210143310074 0.00037556581625907613 -0.028502806073779807 -0.09355388337370936 0.09355388337370901 -Nuclei_Granularity_3_DAPI 0.0036630142691364 0.10879087009653199 0.9634075497947248 -0.0009752859441673324 0.03257533606741404 0.07237183690973933 -0.072371836909739 -Nuclei_Granularity_3_GFP 0.006126974751988867 0.012990982979996457 1.8863579862143631 0.002565285446919489 -0.028959338073299697 0.04311084320566155 -0.04311084320566161 -Nuclei_Granularity_3_RFP 0.0029009262432447835 0.19986623753347843 0.6992605630169483 0.003176551633888478 -0.06665458926080932 -0.07336584840371246 0.07336584840371195 -Nuclei_Granularity_4_DAPI 0.018279734952277638 1.0986626330922178e-07 6.959135646138463 0.00529819244598286 -0.06726882096263012 0.05834244105805221 -0.05834244105805242 -Nuclei_Granularity_4_GFP 0.014179338240160488 6.341703263856857e-06 5.197794083009737 0.003466277019498142 -0.03550343984495903 0.07318151250084318 -0.07318151250084319 -Nuclei_Granularity_4_RFP 0.0011790080782028145 0.6567533567742909 0.18259769878578105 0.0026742215109904883 -0.04868499426149436 -0.031186469250526684 0.03118646925052636 -Nuclei_Granularity_5_DAPI 0.042516689939087304 1.1102230246251565e-16 15.954589770191003 0.005991619695453615 -0.061270739242511404 0.12690342688119144 -0.12690342688119147 -Nuclei_Granularity_5_GFP 0.023445687585669228 6.065499214003012e-10 9.217133449213803 0.002024653867957543 0.0008717478859589511 0.13168812518772446 -0.13168812518772416 -Nuclei_Granularity_5_RFP 0.002872508472941071 0.2043123553880668 0.6897053694683332 0.004143724921592989 -0.0738538634841022 -0.04180450869043457 0.04180450869043409 -Nuclei_Granularity_6_DAPI 0.043799487731673636 1.1102230246251565e-16 15.954589770191003 0.0013639773240873428 0.027326585559566963 0.19877369888311824 -0.19877369888311766 -Nuclei_Granularity_6_GFP 0.03363937832536146 1.7319479184152442e-14 13.76146517183654 0.0026079105487608026 -0.002389021545254 0.1551696891743262 -0.15516968917432583 -Nuclei_Granularity_6_RFP 0.005177975933156831 0.0300908502968682 1.5215655399428605 0.004360300195772766 -0.06810653630644306 -0.004446088364267062 0.004446088364266701 -Nuclei_Granularity_7_DAPI 0.0024421870398085854 0.28302040904447945 0.5481822456919325 0.0024586282171989667 -0.03526832546405129 0.010395186563187928 -0.010395186563188087 -Nuclei_Granularity_7_GFP 0.031973775108091185 9.71445146547012e-14 13.01258171716869 0.0017541313298017874 0.012425608383020398 0.1621271862390977 -0.16212718623909728 -Nuclei_Granularity_7_RFP 0.002462688857777451 0.27875400057173083 0.5547788910693088 0.0003884209953611028 0.00527667235562812 0.046293927656656256 -0.046293927656656124 -Nuclei_Granularity_8_DAPI 0.0413460504214036 1.1102230246251565e-16 15.954589770191003 -0.0011290916728731184 -0.030171380935634762 -0.19562147971534458 0.19562147971534397 -Nuclei_Granularity_8_GFP 0.02779813709890011 7.161160553437185e-12 11.145016589268952 -0.0005924556900318116 0.05245429902035775 0.17841425722185678 -0.1784142572218561 -Nuclei_Granularity_8_RFP 0.0041232129778308035 0.07429417112491299 1.1290452582123442 0.0009100304628709582 -0.0007799216834936534 0.05436760684176066 -0.05436760684176054 -Nuclei_Granularity_9_DAPI 0.05589152332869196 1.1102230246251565e-16 15.954589770191003 -0.008073524251051489 0.0938935857458253 -0.124351913394571 0.1243519133945712 -Nuclei_Granularity_9_GFP 0.011553879582017368 8.125845363116113e-05 4.090131446664343 -0.0009142568325005007 0.043379245699794594 0.12070144883383602 -0.12070144883383553 -Nuclei_Granularity_9_RFP 0.005041851731814373 0.03388013992363925 1.470054804718576 0.0003900724356318687 0.01061357343525507 0.06836480374138948 -0.06836480374138926 -Nuclei_Intensity_IntegratedIntensityEdge_DAPI 0.02285422656220326 1.1045678816046234e-09 8.95680758925346 0.000489292523343947 -0.04669587733828304 -0.16124005668002597 0.16124005668002533 -Nuclei_Intensity_IntegratedIntensityEdge_GFP 0.0023987109902465775 0.2922495432543798 0.5342461589078075 -0.003462057819662277 0.06819814735243711 0.06165504218963428 -0.061655042189633795 -Nuclei_Intensity_IntegratedIntensityEdge_RFP 0.010809104029136152 0.00016608451804844826 3.7796708494239946 0.002731464282691316 -0.0742316998315592 -0.1327135385381045 0.1327135385381038 -Nuclei_Intensity_IntegratedIntensity_DAPI 0.04756932996739882 1.1102230246251565e-16 15.954589770191003 0.00018014970985098108 -0.05783710980842016 -0.22665625024293798 0.22665625024293717 -Nuclei_Intensity_IntegratedIntensity_GFP 0.013355539492214907 1.4182535563400656e-05 4.8482461187472685 -0.006675040584157329 0.10218098592004961 -0.0017592273049611973 0.0017592273049617108 -Nuclei_Intensity_IntegratedIntensity_RFP 0.040309631815170666 1.1102230246251565e-16 15.954589770191003 -0.00039629609845409056 -0.042989667228236494 -0.2020167846076981 0.2020167846076974 -Nuclei_Intensity_LowerQuartileIntensity_DAPI 0.052524667852078655 1.1102230246251565e-16 15.954589770191003 0.013269616411657534 -0.20333810331926078 0.0026416272827719522 -0.002641627282773007 -Nuclei_Intensity_LowerQuartileIntensity_GFP 0.022775987316501878 1.1956294843074033e-09 8.92240338376815 0.003579806624708124 -0.029241019776979946 0.106140316528097 -0.1061403165280969 -Nuclei_Intensity_LowerQuartileIntensity_RFP 0.0058323511769345915 0.016899662813815164 1.7721219604490674 0.00555035674617288 -0.10795651527426192 -0.09317139416509788 0.09317139416509712 -Nuclei_Intensity_MADIntensity_DAPI 0.024679779568023585 1.7312395961255334e-10 9.761642823473844 -0.0013814116132072987 0.0642069498510372 0.1768701945059239 -0.17687019450592317 -Nuclei_Intensity_MADIntensity_GFP 0.03622772971604127 1.2212453270876722e-15 14.913197085032778 0.004202126387888813 -0.030874272162024707 0.1387921757987666 -0.13879217579876643 -Nuclei_Intensity_MADIntensity_RFP 0.006173503167149708 0.012460274935090498 1.9044723748939143 0.00605315901390023 -0.10736394812939844 -0.05892006924559945 0.05892006924559877 -Nuclei_Intensity_MassDisplacement_DAPI 0.013632931840414586 1.0820324182203578e-05 4.965759727359482 -0.008024636310274398 0.13241465453508822 0.03729181751934489 -0.03729181751934413 -Nuclei_Intensity_MassDisplacement_GFP 0.009674151794348163 0.0004892406931670257 3.3104774271817634 -0.003433749862574461 0.08453340912588328 0.130681122375395 -0.1306811223753943 -Nuclei_Intensity_MassDisplacement_RFP 0.00038622146864686346 0.9389110031470413 0.027375571390857764 0.0010518970947955217 -0.015587222195689742 0.0023974943979551654 -0.0023974943979552382 -Nuclei_Intensity_MaxIntensityEdge_DAPI 0.12175565251372122 1.1102230246251565e-16 15.954589770191003 0.016524499211421254 -0.23166722651266358 0.09197729571265716 -0.09197729571265814 -Nuclei_Intensity_MaxIntensityEdge_GFP 0.029188568371166235 1.7177370637000422e-12 11.765043313452077 0.00119903440623238 0.020725478348774556 0.16116795173558857 -0.1611679517355881 -Nuclei_Intensity_MaxIntensityEdge_RFP 0.005831210238853024 0.016916823028616745 1.7716811939382584 0.005797069997396208 -0.10131675593968195 -0.050232939585836855 0.05023293958583623 -Nuclei_Intensity_MaxIntensity_DAPI 0.06886184264199835 1.1102230246251565e-16 15.954589770191003 0.012240635737565543 -0.17037321788895693 0.07321955669355541 -0.07321955669355609 -Nuclei_Intensity_MaxIntensity_GFP 0.01920179559198265 4.371626105559301e-08 7.359356989274074 0.0006578234425281075 0.02261815333293027 0.13471562362929493 -0.1347156236292945 -Nuclei_Intensity_MaxIntensity_RFP 0.004707590152641883 0.04523046766315053 1.3445689218168568 0.005342517057391556 -0.0969477203429238 -0.06100984244806136 0.061009842448060715 -Nuclei_Intensity_MeanIntensityEdge_DAPI 0.13707120194929334 1.1102230246251565e-16 15.954589770191003 0.015425792704846371 -0.20270615400869024 0.14166419223139518 -0.1416641922313959 -Nuclei_Intensity_MeanIntensityEdge_GFP 0.02427477708250403 2.613653737881805e-10 9.582751949153366 0.002829115935327455 -0.013626733279023279 0.12291157765399659 -0.12291157765399638 -Nuclei_Intensity_MeanIntensityEdge_RFP 0.006907988581060565 0.006414359748106335 2.192846686403817 0.006427445579594028 -0.11459772740105469 -0.06501275448692648 0.06501275448692573 -Nuclei_Intensity_MeanIntensity_DAPI 0.07711332944498783 1.1102230246251565e-16 15.954589770191003 0.014459456536191774 -0.2116948017169039 0.04352722693710874 -0.04352722693710971 -Nuclei_Intensity_MeanIntensity_GFP 0.030928895793460365 2.859934511434403e-13 12.54364391150323 0.003869366992560177 -0.028272204903162707 0.12844841925225015 -0.12844841925225 -Nuclei_Intensity_MeanIntensity_RFP 0.006257578795469176 0.011554372155310855 1.9372536483581344 0.006049364847860872 -0.11392626473247724 -0.08617025824236506 0.08617025824236427 -Nuclei_Intensity_MedianIntensity_DAPI 0.07105310980218726 1.1102230246251565e-16 15.954589770191003 0.014588776803552299 -0.21826795777344357 0.02465458728855932 -0.024654587288560376 -Nuclei_Intensity_MedianIntensity_GFP 0.028361051778871937 4.019673482957842e-12 11.395809223111767 0.0038922115938343285 -0.03065979694848328 0.12006660289770153 -0.12006660289770141 -Nuclei_Intensity_MedianIntensity_RFP 0.00601089671480326 0.014412761446894407 1.8412528015419145 0.005781291741259216 -0.11089302312383201 -0.0906466168801702 0.09064661688016942 -Nuclei_Intensity_MinIntensityEdge_DAPI 0.15134865093194627 1.1102230246251565e-16 15.954589770191003 0.013515622044036087 -0.15906325087319756 0.200439269775729 -0.20043926977572935 -Nuclei_Intensity_MinIntensityEdge_GFP 0.0057440858454768096 0.018278413796128046 1.7380614951163254 0.0024620572526771207 -0.027610915663210667 0.042129616667843345 -0.042129616667843386 -Nuclei_Intensity_MinIntensityEdge_RFP 0.007271445064432003 0.004601690573257988 2.3370825875023544 0.006639904317373302 -0.1205023463950593 -0.07587355300626103 0.07587355300626024 -Nuclei_Intensity_MinIntensity_DAPI 0.14448932805176729 1.1102230246251565e-16 15.954589770191003 0.012691417901939657 -0.1452448744447049 0.2051674607726891 -0.20516746077268938 -Nuclei_Intensity_MinIntensity_GFP 0.011312627896791883 0.00010248017426073286 3.9893601447738076 0.002673299090162532 -0.023506507101427446 0.07238842786516403 -0.07238842786516397 -Nuclei_Intensity_MinIntensity_RFP 0.008864064231874469 0.0010497474258066575 2.9789151816667645 0.007331411927792399 -0.1339484649628522 -0.0874652745173281 0.08746527451732722 -Nuclei_Intensity_StdIntensityEdge_DAPI 0.019002030097055256 5.3390614818127347e-08 7.272535078007372 0.010726017441074265 -0.19683747777669125 -0.13153592734940112 0.1315359273493998 -Nuclei_Intensity_StdIntensityEdge_GFP 0.039432524316675654 1.1102230246251565e-16 15.954589770191003 0.0011169420981318686 0.029201410405197414 0.19086042988093638 -0.19086042988093582 -Nuclei_Intensity_StdIntensityEdge_RFP 0.007569144517153847 0.0035002502930955304 2.455900899357237 0.00587721670717621 -0.09621220458213293 -0.024151998287328738 0.024151998287328176 -Nuclei_Intensity_StdIntensity_DAPI 0.06975368280010819 1.1102230246251565e-16 15.954589770191003 0.01266290135377602 -0.17856747880788598 0.06620922166067006 -0.06620922166067082 -Nuclei_Intensity_StdIntensity_GFP 0.03571462397649594 1.9984014443252818e-15 14.699317265087696 0.0032422499416768667 -0.012938477487732719 0.15188343159201018 -0.1518834315920099 -Nuclei_Intensity_StdIntensity_RFP 0.006846889652472932 0.006781187064673677 2.168694275083988 0.006113728558955076 -0.10480453391363741 -0.044553420706541155 0.04455342070654052 -Nuclei_Intensity_UpperQuartileIntensity_DAPI 0.07633465132606643 1.1102230246251565e-16 15.954589770191003 0.014152427251305081 -0.20569885409544558 0.04878044075431612 -0.04878044075431706 -Nuclei_Intensity_UpperQuartileIntensity_GFP 0.03508900209042787 3.885780586188048e-15 14.410521725840727 0.004119038713541556 -0.030068662245291394 0.1368510558712558 -0.13685105587125565 -Nuclei_Intensity_UpperQuartileIntensity_RFP 0.006316113244675736 0.010961928602744697 1.9601130309022952 0.006108410573041978 -0.11446377095406532 -0.08464677495793299 0.08464677495793221 -Nuclei_Location_CenterMassIntensity_X_DAPI 0.0008068595573830217 0.7971709430418058 0.09844853975904219 0.0016819110405485616 -0.026017017438201364 -0.0006699178584394679 0.0006699178584393325 -Nuclei_Location_CenterMassIntensity_X_GFP 0.0008114697294212547 0.795447719046481 0.09938835916732003 0.0016900938941754999 -0.0261656383322758 -0.0007639032344557879 0.0007639032344556526 -Nuclei_Location_CenterMassIntensity_X_RFP 0.0007999812744119428 0.7997388556580222 0.09705180308498994 0.001675815281872552 -0.02592980615899035 -0.0006966403434338526 0.0006966403434337173 -Nuclei_Location_CenterMassIntensity_Y_DAPI 0.000662712343281413 0.8499157357423742 0.0706241299517842 0.0019353611648498757 -0.03693092836544124 -0.02955515530697972 0.029555155306979466 -Nuclei_Location_CenterMassIntensity_Y_GFP 0.0006535966480188948 0.8531536843560859 0.0689727293765646 0.0019190796297447227 -0.03665712552021452 -0.02945833236544155 0.029458332365441296 -Nuclei_Location_CenterMassIntensity_Y_RFP 0.0006633711879441284 0.8496811547906771 0.07074401392438508 0.001940384925904876 -0.03697427814755689 -0.029415726729081576 0.029415726729081316 -Nuclei_Location_Center_X 0.000806949692387926 0.7971372674489196 0.09846688643012029 0.0016800538744685689 -0.02597561060698395 -0.0006169927237823121 0.0006169927237821803 -Nuclei_Location_Center_Y 0.0006668564579445091 0.8484390056290597 0.07137937370089688 0.0019437938319414301 -0.03706112003447244 -0.02955748128963295 0.029557481289632696 -Nuclei_Location_MaxIntensity_X_DAPI 0.0008768983972842204 0.7708431263607122 0.11303399586331365 0.0018510127375679598 -0.02929396743762383 -0.003458580758535921 0.0034585807585357647 -Nuclei_Location_MaxIntensity_X_GFP 0.0009143033165371905 0.7566877651088701 0.12108328808696109 0.0019497869393522854 -0.03126988703684144 -0.005341902463102344 0.00534190246310217 -Nuclei_Location_MaxIntensity_X_RFP 0.0007216271563180721 0.8286708975763397 0.08161791304955493 0.001706728338383255 -0.027198339109296903 -0.003961979440464145 0.003961979440463996 -Nuclei_Location_MaxIntensity_Y_DAPI 0.0005778790943300471 0.879425338631542 0.055801025332957485 0.0017422035479664416 -0.033932955262298316 -0.029436766394667795 0.02943676639466756 -Nuclei_Location_MaxIntensity_Y_GFP 0.0003456254824234506 0.949740735364546 0.022394934256189453 0.001256741422899757 -0.025198838116360582 -0.02420286266552073 0.02420286266552055 -Nuclei_Location_MaxIntensity_Y_RFP 0.000696546131200404 0.8377782331523768 0.07687092750859643 0.0019804255279468603 -0.03783770920082894 -0.030436188104585687 0.03043618810458542 -Nuclei_Neighbors_AngleBetweenNeighbors_Adjacent 0.004366045128729623 0.06053971349451748 1.2179596386824607 0.0007011839528236036 -0.029145678112999993 -0.07559801598153414 0.0755980159815338 -Nuclei_Neighbors_FirstClosestDistance_Adjacent 0.08563968603294037 1.1102230246251565e-16 15.954589770191003 -0.014074114546091024 0.19875390077620902 -0.07241063106704326 0.07241063106704411 -Nuclei_Neighbors_FirstClosestObjectNumber_Adjacent 0.10045324696838065 1.1102230246251565e-16 15.954589770191003 0.020620776435548136 -0.33198929436262975 -0.06177089706469474 0.06177089706469285 -Nuclei_Neighbors_SecondClosestDistance_Adjacent 0.09941022802143107 1.1102230246251565e-16 15.954589770191003 -0.017668250992116896 0.2670204041885691 -0.01883086744058529 0.018830867440586624 -Nuclei_Neighbors_SecondClosestObjectNumber_Adjacent 0.08981250452975298 1.1102230246251565e-16 15.954589770191003 0.0189299877442896 -0.3009199271091334 -0.04086747704011506 0.040867477040113426 -Nuclei_RadialDistribution_FracAtD_DAPI_1of4 0.0522994757905505 1.1102230246251565e-16 15.954589770191003 0.0021772339110574324 0.01712402036502536 0.20823533374036926 -0.2082353337403687 -Nuclei_RadialDistribution_FracAtD_DAPI_2of4 0.04837897794760693 1.1102230246251565e-16 15.954589770191003 0.00391099001863293 -0.01766328000010689 0.1747476597904073 -0.174747659790407 -Nuclei_RadialDistribution_FracAtD_DAPI_3of4 0.026133657116026332 3.932509873294521e-11 10.405330178326558 0.007233764239697107 -0.0986513562354406 0.05163749096262592 -0.051637490962626306 -Nuclei_RadialDistribution_FracAtD_DAPI_4of4 0.05166417074378016 1.1102230246251565e-16 15.954589770191003 -0.005791859926054014 0.051758641992258714 -0.15341587116248168 0.15341587116248162 -Nuclei_RadialDistribution_FracAtD_GFP_1of4 0.00014853808637238508 0.9894033252135216 0.004626634380714834 -0.0009087611678098307 0.017431866154136912 0.014251182851138529 -0.01425118285113841 -Nuclei_RadialDistribution_FracAtD_GFP_2of4 0.0012889729096122737 0.6163065720835541 0.21020320107606827 -0.0012398363952466525 0.014531492272814082 -0.01863375922915501 0.018633759229155042 -Nuclei_RadialDistribution_FracAtD_GFP_3of4 0.0048969779693816 0.03841605736474474 1.4154872087767008 -0.002389904140211561 0.02779531696779855 -0.03680549623847697 0.03680549623847703 -Nuclei_RadialDistribution_FracAtD_GFP_4of4 0.0029431137791315898 0.1934240928637656 0.7134894312556009 0.002123906297029954 -0.026951454679861882 0.02344907569430028 -0.023449075694300367 -Nuclei_RadialDistribution_FracAtD_RFP_1of4 0.0004276207559165135 0.9271370528773311 0.03285606207038372 -0.0010644262275206027 0.023268630054728158 0.028426035436166598 -0.028426035436166414 -Nuclei_RadialDistribution_FracAtD_RFP_2of4 0.0023931268651403714 0.29345299622635035 0.5324614518756507 -0.0011208199263182158 0.008717870600233622 -0.03503211980616784 0.03503211980616781 -Nuclei_RadialDistribution_FracAtD_RFP_3of4 0.019442279291813302 3.43592072571397e-08 7.463956864858882 -0.0015838440823451466 -0.005660248275456609 -0.12350724697930443 0.1235072469793041 -Nuclei_RadialDistribution_FracAtD_RFP_4of4 0.007841384840180377 0.0027223478985251015 2.565056375548881 0.0015353130780121392 -0.0063674660720135084 0.070931279162484 -0.07093127916248387 -Nuclei_RadialDistribution_MeanFrac_DAPI_1of4 0.04053315067551522 1.1102230246251565e-16 15.954589770191003 -5.475484534719644e-05 0.05135961716762372 0.20792899527193734 -0.2079289952719366 -Nuclei_RadialDistribution_MeanFrac_DAPI_2of4 0.056582041028439556 1.1102230246251565e-16 15.954589770191003 0.0032911159041788657 -0.0013846081002986677 0.2025302726325578 -0.20253027263255735 -Nuclei_RadialDistribution_MeanFrac_DAPI_3of4 0.04820943898249619 1.1102230246251565e-16 15.954589770191003 0.00725993058988924 -0.0824970538368035 0.1197832526938359 -0.11978325269383605 -Nuclei_RadialDistribution_MeanFrac_DAPI_4of4 0.0702530332864928 1.1102230246251565e-16 15.954589770191003 -0.005879150163751186 0.04352041631811096 -0.19284681415504484 0.19284681415504462 -Nuclei_RadialDistribution_MeanFrac_GFP_1of4 0.007983290577341373 0.002387078776973861 2.6221332484249533 -0.0027751779644353556 0.03004825076312323 -0.05190886012162178 0.05190886012162181 -Nuclei_RadialDistribution_MeanFrac_GFP_2of4 0.00900370074078205 0.0009207707953626798 3.035848463937317 -0.0026698058798551613 0.026492393514166768 -0.05987766897083224 0.05987766897083224 -Nuclei_RadialDistribution_MeanFrac_GFP_3of4 0.004996039809264663 0.03525570587813098 1.4527705855988164 -0.0035679489674032847 0.05151885615648086 -0.013695740526055336 0.013695740526055565 -Nuclei_RadialDistribution_MeanFrac_GFP_4of4 0.012474621817970877 3.338850391965131e-05 4.47640304048431 0.004079400564910662 -0.04962232431144907 0.053861355926170515 -0.053861355926170654 -Nuclei_RadialDistribution_MeanFrac_RFP_1of4 0.008851841402379712 0.0010618515752998103 2.9739361843215404 -0.003526273203946223 0.043593788938095485 -0.04367804845519036 0.04367804845519049 -Nuclei_RadialDistribution_MeanFrac_RFP_2of4 0.015796625787496965 1.2925329841984023e-06 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+Nuclei_Texture_Variance_RFP_3_00_256 0.004956430253377442 0.03648817770541646 0.004839538752108734 -0.0798649616319095 -0.022521642911276202 0.022521642911275737 0.04914474978652959 1.3085228717388677 +Nuclei_Texture_Variance_RFP_3_01_256 0.0051424931520230865 0.031037247670725132 0.004942714940089977 -0.0816701210774374 -0.02342361115892609 0.023423611158925613 0.0424932129076791 1.3716804307056492 +Nuclei_Texture_Variance_RFP_3_02_256 0.005079289471157655 0.03279449873612705 0.004878055900939448 -0.08033849712465993 -0.02203370479382112 0.02203370479382067 0.04447945447738728 1.3518405478404167 +Nuclei_Texture_Variance_RFP_3_03_256 0.0048366468761950054 0.04047219083871889 0.004774401774033 -0.07874137281610233 -0.02201823689416045 0.022018236894159993 0.05394743233496284 1.2680292210328612 diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py index 359cf6c..46553ac 100644 --- a/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py +++ b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py @@ -11,12 +11,16 @@ from pathlib import Path import pandas as pd +import sys from sklearn.linear_model import LinearRegression import numpy as np from scipy import stats +from statsmodels.stats.multitest import multipletests +# ## Finding the git root directory to reference paths on any system + # In[ ]: @@ -45,6 +49,8 @@ import preprocess_utils as ppu +# ## Specify paths + # In[ ]: @@ -54,14 +60,18 @@ parents=True, exist_ok=True ) # Create the parent directories if they don't exist - -# In[ ]: - - plate_path = Path( f"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data" ) +model_data_path = f"{data_path}/model_properties.tsv" + + +# ## Combine data + +# In[ ]: + + filename1 = "Plate_1_sc_norm.parquet" filename2 = "Plate_2_sc_norm.parquet" @@ -84,22 +94,21 @@ # Combine the plate dataframes: cp_df = pd.concat([plate1df, plate2df], axis="rows") -cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)] -cp_df.reset_index(inplace=True, drop=True) +# ## Preprocessing + # In[ ]: +# Removes columns that contain all zeros +cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)] +cp_df.reset_index(inplace=True, drop=True) + +# Removes metadata pcp_features = po1.remove_meta(cp_df) cp_features = pcp_features.columns - -# ## Fit linear model - -# In[ ]: - - # Setup linear modeling framework variables = ["Metadata_number_of_singlecells", "Metadata_plate"] X = cp_df.loc[:, variables] @@ -114,6 +123,8 @@ dfres = X.shape[0] - dfreg - 1 +# ## Fit linear model + # In[ ]: @@ -158,11 +169,8 @@ # Calculate the p-value for the F-statistic p_value = 1 - stats.f.cdf(f_value, dfreg, dfres) - # Calculate the negative log of the p value - nlogp = -1 * np.log10(p_value) - # Add results to a growing list - lm_results.append([cp_feature, r2_score, p_value, nlogp] + list(coef)) + lm_results.append([cp_feature, r2_score, p_value] + list(coef)) # In[ ]: @@ -175,7 +183,6 @@ "feature", "r2_score", "p_value", - "-log_p", "cell_count_coef", "plate_coef", "Null_coef", @@ -183,4 +190,26 @@ ], ) -lm_results.to_csv(f"{data_path}/model_properties.tsv", sep="\t", index=False) + +# ## Controlling for FDR + +# In[ ]: + + +# Benjamini-Hochberg procedure has a greater power than Bonferroni, and still controls for FDR +_, corrected_pvalues, _, _ = multipletests( + lm_results["p_value"].tolist(), method="fdr_bh" +) + +lm_results["corrected_p_value"] = corrected_pvalues + +# Taking the negative log of the corrected p values +lm_results["neg_log_p"] = -np.log10(lm_results["corrected_p_value"]) + + +# ## Save the results + +# In[ ]: + + +lm_results.to_csv(model_data_path, sep="\t", index=False) diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb index 5d171dd..3db3584 100644 --- a/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb +++ b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb @@ -1,258 +1,406 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "9A4gVcqrrm" - }, - "source": [ - "# Fit a linear model on cell morphology features\n", - "\n", - "Our aim is to determine which features significantly contribute to NF1 genotype adjusted by covariates. This notebook is adapted from Dr. Way at:\n", - "https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/fit_linear_model.ipynb" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "9A4gVcqrrm" + }, + "source": [ + "# Fit a linear model on cell morphology features\n", + "\n", + "Our aim is to determine which features significantly contribute to NF1 genotype adjusted by covariates. This notebook is adapted from Dr. Way at:\n", + "https://github.com/WayScience/Benchmarking_NF1_data/blob/main/5_analyze_data/notebooks/linear_model/fit_linear_model.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:55.564753Z", + "iopub.status.busy": "2023-06-16T19:45:55.564652Z", + "iopub.status.idle": "2023-06-16T19:45:55.953193Z", + "shell.execute_reply": "2023-06-16T19:45:55.952874Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "Lq6i6b1L6q" - }, - "source": [ - "from pathlib import Path\n", - "import pandas as pd\n", - "\n", - "from sklearn.linear_model import LinearRegression\n", - "import numpy as np\n", - "from scipy import stats" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "Lq6i6b1L6q" + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pandas as pd\n", + "import sys\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "import numpy as np\n", + "from scipy import stats\n", + "from statsmodels.stats.multitest import multipletests" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "8WbUWhWL73" + }, + "source": [ + "## Finding the git root directory to reference paths on any system" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:55.955241Z", + "iopub.status.busy": "2023-06-16T19:45:55.954829Z", + "iopub.status.idle": "2023-06-16T19:45:55.957511Z", + "shell.execute_reply": "2023-06-16T19:45:55.957252Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "261w9QY97P" - }, - "source": [ - "# Get the current working directory\n", - "cwd = Path.cwd()\n", - "\n", - "if (cwd / \".git\").is_dir():\n", - " root_dir = cwd\n", - "\n", - "else:\n", - " root_dir = None\n", - " for parent in cwd.parents:\n", - " if (parent / \".git\").is_dir():\n", - " root_dir = parent\n", - " break\n", - "\n", - "# Check if a Git root directory was found\n", - "if root_dir is None:\n", - " raise FileNotFoundError(\"No Git root directory found.\")" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "261w9QY97P" + }, + "outputs": [], + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:55.958648Z", + "iopub.status.busy": "2023-06-16T19:45:55.958402Z", + "iopub.status.idle": "2023-06-16T19:45:55.960436Z", + "shell.execute_reply": "2023-06-16T19:45:55.960204Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "jG7YhMVm79" - }, - "source": [ - "sys.path.append(f\"{root_dir}/utils\")\n", - "import preprocess_utils as ppu" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "jG7YhMVm79" + }, + "outputs": [], + "source": [ + "sys.path.append(f\"{root_dir}/utils\")\n", + "import preprocess_utils as ppu" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "DdbElioHPv" + }, + "source": [ + "## Specify paths" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:55.961652Z", + "iopub.status.busy": "2023-06-16T19:45:55.961397Z", + "iopub.status.idle": "2023-06-16T19:45:55.963492Z", + "shell.execute_reply": "2023-06-16T19:45:55.963236Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "EwwGYfhdHG" - }, - "source": [ - "data_path = Path(\"data\")\n", - "\n", - "data_path.mkdir(\n", - " parents=True, exist_ok=True\n", - ") # Create the parent directories if they don't exist" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "EwwGYfhdHG" + }, + "outputs": [], + "source": [ + "data_path = Path(\"data\")\n", + "\n", + "data_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist\n", + "\n", + "plate_path = Path(\n", + " f\"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data\"\n", + ")\n", + "\n", + "model_data_path = f\"{data_path}/model_properties.tsv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "8BK4lXcVKc" + }, + "source": [ + "## Combine data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:55.964494Z", + "iopub.status.busy": "2023-06-16T19:45:55.964397Z", + "iopub.status.idle": "2023-06-16T19:45:56.084659Z", + "shell.execute_reply": "2023-06-16T19:45:56.084294Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "9bdkn1aAvb" - }, - "source": [ - "plate_path = Path(\n", - " f\"{root_dir}/nf1_painting_repo/3.processing_features/data/normalized_data\"\n", - ")\n", - "\n", - "filename1 = \"Plate_1_sc_norm.parquet\"\n", - "filename2 = \"Plate_2_sc_norm.parquet\"\n", - "\n", - "path1 = plate_path / filename1\n", - "path2 = plate_path / filename2\n", - "\n", - "po1 = ppu.Preprocess_data(path=path1)\n", - "po2 = ppu.Preprocess_data(path=path2)\n", - "\n", - "plate1df = po1.df # Returns the dataframe generated by the csv\n", - "plate2df = po2.df # Returns the dataframe generated by the csv\n", - "\n", - "# Set plate column:\n", - "plate1df[\"Metadata_plate\"] = \"1\"\n", - "plate2df[\"Metadata_plate\"] = \"2\"\n", - "\n", - "common_columns = list(plate1df.columns.intersection(plate2df.columns))\n", - "plate1df = plate1df.loc[:, common_columns]\n", - "plate2df = plate2df.loc[:, common_columns]\n", - "\n", - "# Combine the plate dataframes:\n", - "cp_df = pd.concat([plate1df, plate2df], axis=\"rows\")\n", - "cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)]\n", - "cp_df.reset_index(inplace=True, drop=True)" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "pKNMm62FVl" + }, + "outputs": [], + "source": [ + "filename1 = \"Plate_1_sc_norm.parquet\"\n", + "filename2 = \"Plate_2_sc_norm.parquet\"\n", + "\n", + "path1 = plate_path / filename1\n", + "path2 = plate_path / filename2\n", + "\n", + "po1 = ppu.Preprocess_data(path=path1)\n", + "po2 = ppu.Preprocess_data(path=path2)\n", + "\n", + "plate1df = po1.df # Returns the dataframe generated by the csv\n", + "plate2df = po2.df # Returns the dataframe generated by the csv\n", + "\n", + "# Set plate column:\n", + "plate1df[\"Metadata_plate\"] = \"1\"\n", + "plate2df[\"Metadata_plate\"] = \"2\"\n", + "\n", + "common_columns = list(plate1df.columns.intersection(plate2df.columns))\n", + "plate1df = plate1df.loc[:, common_columns]\n", + "plate2df = plate2df.loc[:, common_columns]\n", + "\n", + "# Combine the plate dataframes:\n", + "cp_df = pd.concat([plate1df, plate2df], axis=\"rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "6LLWg337Um" + }, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:56.086467Z", + "iopub.status.busy": "2023-06-16T19:45:56.086260Z", + "iopub.status.idle": "2023-06-16T19:45:56.097512Z", + "shell.execute_reply": "2023-06-16T19:45:56.097238Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "K57KpbjyKN" - }, - "source": [ - "pcp_features = po1.remove_meta(cp_df)\n", - "cp_features = pcp_features.columns" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "YeIyN5E3xy" + }, + "outputs": [], + "source": [ + "# Removes columns that contain all zeros\n", + "cp_df = cp_df.loc[:, (cp_df != 0).any(axis=0)]\n", + "cp_df.reset_index(inplace=True, drop=True)\n", + "\n", + "# Removes metadata\n", + "pcp_features = po1.remove_meta(cp_df)\n", + "cp_features = pcp_features.columns\n", + "\n", + "# Setup linear modeling framework\n", + "variables = [\"Metadata_number_of_singlecells\", \"Metadata_plate\"]\n", + "X = cp_df.loc[:, variables]\n", + "\n", + "# Add dummy matrix of categorical genotypes\n", + "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype).astype(int)\n", + "\n", + "X = pd.concat([X, genotype_x], axis=1)\n", + "X_arr = X.values\n", + "\n", + "dfreg = X.shape[1]\n", + "dfres = X.shape[0] - dfreg - 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "2111g4dKC3" + }, + "source": [ + "## Fit linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:56.098735Z", + "iopub.status.busy": "2023-06-16T19:45:56.098624Z", + "iopub.status.idle": "2023-06-16T19:45:57.555187Z", + "shell.execute_reply": "2023-06-16T19:45:57.554789Z" }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "2111g4dKC3" - }, - "source": [ - "## Fit linear model" - ] + "jukit_cell_id": "4gra1XWKi3" + }, + "outputs": [], + "source": [ + "# Fit linear model for each feature\n", + "lm_results = []\n", + "i = 0\n", + "for cp_feature in cp_features:\n", + " # Subset CP data to each individual feature (univariate test)\n", + " y = cp_df.loc[:, cp_feature]\n", + "\n", + " # Fit linear model\n", + " lm = LinearRegression(fit_intercept=True)\n", + " lm_result = lm.fit(X=X_arr, y=y)\n", + "\n", + " # Extract Beta coefficients\n", + " # (contribution of feature to X covariates)\n", + " coef = lm_result.coef_\n", + "\n", + " # Estimate fit (R^2)\n", + " r2_score = lm.score(X=X_arr, y=y)\n", + "\n", + " y_pred = lm.predict(X_arr)\n", + "\n", + " # Calculate the residuals\n", + " residuals = y.values - y_pred\n", + "\n", + " # Calculate the Sum of Squares Regression (SSR)\n", + " ssr = np.sum((y_pred - np.mean(y.values)) ** 2)\n", + "\n", + " # Calculate the Mean Squares Regression (MSR)\n", + " msr = ssr / dfreg\n", + "\n", + " # Calculate the Sum of Squares Residual (SSE) or residual sum of squares\n", + " sse = np.sum(residuals**2)\n", + "\n", + " # Calculate the Mean Squares Residual (MSE)\n", + " mse = sse / dfres\n", + "\n", + " # Calculate the F-value\n", + " f_value = msr / mse\n", + "\n", + " # Calculate the p-value for the F-statistic\n", + " p_value = 1 - stats.f.cdf(f_value, dfreg, dfres)\n", + "\n", + " # Add results to a growing list\n", + " lm_results.append([cp_feature, r2_score, p_value] + list(coef))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:57.557252Z", + "iopub.status.busy": "2023-06-16T19:45:57.557045Z", + "iopub.status.idle": "2023-06-16T19:45:57.560382Z", + "shell.execute_reply": "2023-06-16T19:45:57.560102Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "YeIyN5E3xy" - }, - "source": [ - "# Setup linear modeling framework\n", - "variables = [\"Metadata_number_of_singlecells\", \"Metadata_plate\"]\n", - "X = cp_df.loc[:, variables]\n", - "\n", - "# Add dummy matrix of categorical genotypes\n", - "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype).astype(int)\n", - "\n", - "X = pd.concat([X, genotype_x], axis=1)\n", - "X_arr = X.values\n", - "\n", - "dfreg = X.shape[1]\n", - "dfres = X.shape[0] - dfreg - 1" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "DnyVti231X" + }, + "outputs": [], + "source": [ + "# Convert results to a pandas DataFrame\n", + "lm_results = pd.DataFrame(\n", + " lm_results,\n", + " columns=[\n", + " \"feature\",\n", + " \"r2_score\",\n", + " \"p_value\",\n", + " \"cell_count_coef\",\n", + " \"plate_coef\",\n", + " \"Null_coef\",\n", + " \"WT_coef\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "2Ceb5fEnAf" + }, + "source": [ + "## Controlling for FDR" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:57.561901Z", + "iopub.status.busy": "2023-06-16T19:45:57.561632Z", + "iopub.status.idle": "2023-06-16T19:45:57.564452Z", + "shell.execute_reply": "2023-06-16T19:45:57.564192Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "4gra1XWKi3" - }, - "source": [ - "# Fit linear model for each feature\n", - "lm_results = []\n", - "i = 0\n", - "for cp_feature in cp_features:\n", - " # Subset CP data to each individual feature (univariate test)\n", - " y = cp_df.loc[:, cp_feature]\n", - "\n", - " # Fit linear model\n", - " lm = LinearRegression(fit_intercept=True)\n", - " lm_result = lm.fit(X=X_arr, y=y)\n", - "\n", - " # Extract Beta coefficients\n", - " # (contribution of feature to X covariates)\n", - " coef = lm_result.coef_\n", - "\n", - " # Estimate fit (R^2)\n", - " r2_score = lm.score(X=X_arr, y=y)\n", - "\n", - " y_pred = lm.predict(X_arr)\n", - "\n", - " # Calculate the residuals\n", - " residuals = y.values - y_pred\n", - "\n", - " # Calculate the Sum of Squares Regression (SSR)\n", - " ssr = np.sum((y_pred - np.mean(y.values)) ** 2)\n", - "\n", - " # Calculate the Mean Squares Regression (MSR)\n", - " msr = ssr / dfreg\n", - "\n", - " # Calculate the Sum of Squares Residual (SSE) or residual sum of squares\n", - " sse = np.sum(residuals**2)\n", - "\n", - " # Calculate the Mean Squares Residual (MSE)\n", - " mse = sse / dfres\n", - "\n", - " # Calculate the F-value\n", - " f_value = msr / mse\n", - "\n", - " # Calculate the p-value for the F-statistic\n", - " p_value = 1 - stats.f.cdf(f_value, dfreg, dfres)\n", - "\n", - " # Calculate the negative log of the p value\n", - " nlogp = -1 * np.log10(p_value)\n", - "\n", - " # Add results to a growing list\n", - " lm_results.append([cp_feature, r2_score, p_value, nlogp] + list(coef))" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "jz9zRzmXxt" + }, + "outputs": [], + "source": [ + "# Benjamini-Hochberg procedure has a greater power than Bonferroni, and still controls for FDR\n", + "_, corrected_pvalues, _, _ = multipletests(\n", + " lm_results[\"p_value\"].tolist(), method=\"fdr_bh\"\n", + ")\n", + "\n", + "lm_results[\"corrected_p_value\"] = corrected_pvalues\n", + "\n", + "# Taking the negative log of the corrected p values\n", + "lm_results[\"neg_log_p\"] = -np.log10(lm_results[\"corrected_p_value\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "xm1URor4df" + }, + "source": [ + "## Save the results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:45:57.566275Z", + "iopub.status.busy": "2023-06-16T19:45:57.566170Z", + "iopub.status.idle": "2023-06-16T19:45:57.576016Z", + "shell.execute_reply": "2023-06-16T19:45:57.575751Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "DnyVti231X" - }, - "source": [ - "# Convert results to a pandas DataFrame\n", - "lm_results = pd.DataFrame(\n", - " lm_results,\n", - " columns=[\n", - " \"feature\",\n", - " \"r2_score\",\n", - " \"p_value\",\n", - " \"-log_p\",\n", - " \"cell_count_coef\",\n", - " \"plate_coef\",\n", - " \"Null_coef\",\n", - " \"WT_coef\",\n", - " ],\n", - ")\n", - "\n", - "lm_results.to_csv(f\"{data_path}/model_properties.tsv\", sep=\"\\t\", index=False)" - ], - "outputs": [], - "execution_count": null - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "python", - "language": "python", - "name": "python3" - } + "jukit_cell_id": "YOJIOVVq1O" + }, + "outputs": [], + "source": [ + "lm_results.to_csv(model_data_path, sep=\"\\t\", index=False)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } From 671af7254cb7c5a45460c19bc75e32147ff66d93 Mon Sep 17 00:00:00 2001 From: Cameron Mattson Date: Fri, 16 Jun 2023 13:54:20 -0600 Subject: [PATCH 09/12] Refactored and changed p values reference --- .../data/feature_compartments.tsv | 2324 ++++++++--------- .../data/significant_feature_compartments.tsv | 1749 ++++++------- .../nbconverted/seperate_compartments.py | 31 +- .../seperate_compartments.ipynb | 631 +++-- 4 files changed, 2386 insertions(+), 2349 deletions(-) diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv index 0e28574..cbe811b 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/feature_compartments.tsv @@ -1,1162 +1,1162 @@ -feature r2_score p_value -log_p cell_count_coef plate_coef Null_coef WT_coef compartment -Cytoplasm_Granularity_10_RFP 0.0037617125664257 0.1003252629539767 0.998589693300464 0.0035668092075674 -0.0547577118054782 0.0002926773188153 -0.0002926773188156 actin -Cytoplasm_Granularity_11_RFP 0.0083140810700031 0.0017553507473393 2.75563609149773 0.0020349194384395 -0.0152096139187058 0.0661476342826508 -0.0661476342826508 actin -Cytoplasm_Granularity_12_RFP 0.0170346819190725 3.793612946001446e-07 6.420946981299408 -0.0086265789374558 0.1743870117677087 0.1719627433359708 -0.1719627433359696 actin -Cytoplasm_Granularity_13_RFP 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14.410521725840727 0.0041190387135415 -0.0300686622452913 0.1368510558712558 -0.1368510558712556 er -Nuclei_Location_CenterMassIntensity_X_GFP 0.0008114697294212 0.795447719046481 0.09938835916732 0.0016900938941754 -0.0261656383322758 -0.0007639032344557 0.0007639032344556 er -Nuclei_Location_CenterMassIntensity_Y_GFP 0.0006535966480188 0.8531536843560859 0.0689727293765646 0.0019190796297447 -0.0366571255202145 -0.0294583323654415 0.0294583323654412 er -Nuclei_Location_MaxIntensity_X_GFP 0.0009143033165371 0.7566877651088701 0.121083288086961 0.0019497869393522 -0.0312698870368414 -0.0053419024631023 0.0053419024631021 er -Nuclei_Location_MaxIntensity_Y_GFP 0.0003456254824234 0.949740735364546 0.0223949342561894 0.0012567414228997 -0.0251988381163605 -0.0242028626655207 0.0242028626655205 er -Nuclei_RadialDistribution_FracAtD_GFP_1of4 0.0001485380863723 0.9894033252135216 0.0046266343807148 -0.0009087611678098 0.0174318661541369 0.0142511828511385 -0.0142511828511384 er 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1.4527705855988164 -0.0035679489674032 0.0515188561564808 -0.0136957405260553 0.0136957405260555 er -Nuclei_RadialDistribution_MeanFrac_GFP_4of4 0.0124746218179708 3.338850391965131e-05 4.47640304048431 0.0040794005649106 -0.049622324311449 0.0538613559261705 -0.0538613559261706 er -Nuclei_RadialDistribution_RadialCV_GFP_1of4 0.0006118120305967 0.8677986896688914 0.0615810099685026 -0.0014574214007185 0.0305905002990967 0.0336976027152559 -0.0336976027152556 er -Nuclei_RadialDistribution_RadialCV_GFP_2of4 0.0052672339487742 0.0278315653991271 1.555462365887177 0.0020038088523175 -0.0195198881160578 0.0464384143930551 -0.0464384143930551 er -Nuclei_RadialDistribution_RadialCV_GFP_3of4 0.0282967770263175 4.293898570040256e-12 11.367148218631044 0.0018719405801501 0.0075538925629502 0.1495292771445221 -0.1495292771445218 er -Nuclei_RadialDistribution_RadialCV_GFP_4of4 0.0272904657971755 1.204736310711496e-11 10.919107999839982 0.0007948371717584 0.0267691880534155 0.1604699254441983 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-Nuclei_Texture_Contrast_GFP_3_01_256 0.0246910775184046 1.711448760488565e-10 9.766636098787387 0.0039591893880754 -0.0349159342349973 0.1067862694831461 -0.106786269483146 er -Nuclei_Texture_Contrast_GFP_3_02_256 0.0263763247356724 3.0690783248132896e-11 10.512992027957832 0.0033770816121343 -0.0223555391249407 0.1216541411104448 -0.1216541411104446 er -Nuclei_Texture_Contrast_GFP_3_03_256 0.0232817994718105 7.162140880367929e-10 9.144957140603555 0.0032095889894389 -0.0217062260606255 0.113729402201615 -0.1137294022016149 er -Nuclei_Texture_Correlation_GFP_3_00_256 0.0240455909807728 3.299109874177475e-10 9.481603220388465 0.0003060790153853 0.0332029995891211 0.1560273584307004 -0.1560273584306998 er -Nuclei_Texture_Correlation_GFP_3_01_256 0.019808734827597 2.3794896053530582e-08 7.6235161880485265 0.000856946541576 0.0194917578397252 0.134446091943645 -0.1344460919436446 er -Nuclei_Texture_Correlation_GFP_3_02_256 0.016125426457502 9.33979426576137e-07 6.029662690175544 0.0021103900278328 -0.0074027906801439 0.1030551183732843 -0.1030551183732841 er -Nuclei_Texture_Correlation_GFP_3_03_256 0.020119529146959 1.741826916035194e-08 7.7589950026839585 0.0001536083808883 0.0326812269006805 0.1442329381613281 -0.1442329381613276 er -Nuclei_Texture_DifferenceEntropy_GFP_3_00_256 0.0433696940374218 1.1102230246251563e-16 15.954589770191005 0.0044960465679119 -0.0318337451827609 0.1534393981027048 -0.1534393981027046 er -Nuclei_Texture_DifferenceEntropy_GFP_3_01_256 0.0439814529922998 1.1102230246251563e-16 15.954589770191005 0.00428361531298 -0.0273933518574876 0.1582752637003458 -0.1582752637003456 er -Nuclei_Texture_DifferenceEntropy_GFP_3_02_256 0.0460060778339831 1.1102230246251563e-16 15.954589770191005 0.0043668947942816 -0.0277456430582258 0.1620943540040856 -0.1620943540040853 er -Nuclei_Texture_DifferenceEntropy_GFP_3_03_256 0.0446660444164624 1.1102230246251563e-16 15.954589770191005 0.0047108729866538 -0.0351435405868524 0.1534085531980423 -0.1534085531980421 er -Nuclei_Texture_DifferenceVariance_GFP_3_00_256 0.0276291535841496 8.515521621177413e-12 11.069788744030149 -0.0026551271917499 0.0076619688223906 -0.1364539320698558 0.1364539320698555 er -Nuclei_Texture_DifferenceVariance_GFP_3_01_256 0.0275834931904239 8.923417560424696e-12 11.049468784258725 -0.0023003805434663 0.0010136237727541 -0.1413732950654785 0.1413732950654782 er -Nuclei_Texture_DifferenceVariance_GFP_3_02_256 0.0281423867624462 5.030975636088897e-12 11.298347785862042 -0.0026235751081896 0.0066737150738123 -0.1385252261869459 0.1385252261869456 er -Nuclei_Texture_DifferenceVariance_GFP_3_03_256 0.0279369851871421 6.210920666660513e-12 11.20684401804983 -0.0028043723756456 0.0102470250558275 -0.1352567419592744 0.1352567419592741 er -Nuclei_Texture_Entropy_GFP_3_00_256 0.038414069245105 1.1102230246251563e-16 15.954589770191005 0.0029690304138087 -0.0059833334410769 0.1632237915910936 -0.1632237915910933 er -Nuclei_Texture_Entropy_GFP_3_01_256 0.0377875600961979 2.220446049250313e-16 15.653559774527022 0.0028613715737035 -0.0043640085196843 0.163077257366359 -0.1630772573663586 er -Nuclei_Texture_Entropy_GFP_3_02_256 0.0395460102831258 1.1102230246251563e-16 15.954589770191005 0.0031121926734525 -0.0079519711698065 0.164178878663723 -0.1641788786637227 er -Nuclei_Texture_Entropy_GFP_3_03_256 0.0383856551851717 1.1102230246251563e-16 15.954589770191005 0.0027233461874612 -0.0013773073547442 0.1666374735274694 -0.166637473527469 er -Nuclei_Texture_InfoMeas1_GFP_3_00_256 0.0476275173115742 1.1102230246251563e-16 15.954589770191005 -0.0037213057859406 0.0145088755129118 -0.1757296729925979 0.1757296729925976 er -Nuclei_Texture_InfoMeas1_GFP_3_01_256 0.0486748385420504 1.1102230246251563e-16 15.954589770191005 -0.004221468065053 0.0233884446225615 -0.1708271772993806 0.1708271772993803 er -Nuclei_Texture_InfoMeas1_GFP_3_02_256 0.0425930840313972 1.1102230246251563e-16 15.954589770191005 -0.003441340231715 0.0122483316216863 -0.1673204969531264 0.1673204969531261 er -Nuclei_Texture_InfoMeas1_GFP_3_03_256 0.0470017486549151 1.1102230246251563e-16 15.954589770191005 -0.0038640122955209 0.0175825060988873 -0.1721078353053003 0.1721078353053 er -Nuclei_Texture_InfoMeas2_GFP_3_00_256 0.0482459033039358 1.1102230246251563e-16 15.954589770191005 0.0022127640103152 0.014182644618496 0.198376787106507 -0.1983767871065065 er -Nuclei_Texture_InfoMeas2_GFP_3_01_256 0.053993334070295 1.1102230246251563e-16 15.954589770191005 0.0025797824025081 0.0105467615530296 0.2066329853123887 -0.2066329853123882 er -Nuclei_Texture_InfoMeas2_GFP_3_02_256 0.0417517405701255 1.1102230246251563e-16 15.954589770191005 0.0012260094624569 0.028632671698448 0.1954202362568485 -0.1954202362568479 er -Nuclei_Texture_InfoMeas2_GFP_3_03_256 0.048985743767752 1.1102230246251563e-16 15.954589770191005 0.0016234621431986 0.0255508881441465 0.2078826579421669 -0.2078826579421663 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_00_256 0.0417470028408092 1.1102230246251563e-16 15.954589770191005 -0.0048714989206076 0.0400681719640582 -0.1433018625910618 0.1433018625910617 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_01_256 0.0423498082841684 1.1102230246251563e-16 15.954589770191005 -0.0048352401929733 0.0389846940027606 -0.1454673044379447 0.1454673044379445 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_02_256 0.0461546362499335 1.1102230246251563e-16 15.954589770191005 -0.005229982588979 0.0442057334349798 -0.1489531874058865 0.1489531874058864 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_03_256 0.0451147216085364 1.1102230246251563e-16 15.954589770191005 -0.0050563259195176 0.0415018287834804 -0.149095033812476 0.1490950338124759 er -Nuclei_Texture_SumAverage_GFP_3_00_256 0.0306176419805944 3.9424019604439314e-13 12.404239097889388 0.0038052075091054 -0.0272745813128624 0.1284952055626233 -0.1284952055626231 er -Nuclei_Texture_SumAverage_GFP_3_01_256 0.0304288667133351 4.789502128232925e-13 12.319709629424477 0.0037680656160631 -0.0267042494334882 0.1284926885876279 -0.1284926885876278 er -Nuclei_Texture_SumAverage_GFP_3_02_256 0.0305697168381128 4.142242104876459e-13 12.382764521150174 0.0038121272573545 -0.0274427780306701 0.1282407303726188 -0.1282407303726187 er -Nuclei_Texture_SumAverage_GFP_3_03_256 0.0305995463279852 4.0167869030938164e-13 12.396121207667209 0.0037510959464925 -0.026252344199094 0.1292790327611029 -0.1292790327611028 er -Nuclei_Texture_SumEntropy_GFP_3_00_256 0.0450373077286938 1.1102230246251563e-16 15.954589770191005 0.0035521679966113 -0.012849580737742 0.1718578730105756 -0.1718578730105752 er -Nuclei_Texture_SumEntropy_GFP_3_01_256 0.0449507876654683 1.1102230246251563e-16 15.954589770191005 0.0034642408444688 -0.0112389829242032 0.1729238444268886 -0.1729238444268883 er -Nuclei_Texture_SumEntropy_GFP_3_02_256 0.0446701910807069 1.1102230246251563e-16 15.954589770191005 0.0035153735208137 -0.012375479328181 0.1714812578759525 -0.1714812578759522 er -Nuclei_Texture_SumEntropy_GFP_3_03_256 0.045444379950204 1.1102230246251563e-16 15.954589770191005 0.0032941897808161 -0.0077313014014206 0.1766021189744544 -0.176602118974454 er -Nuclei_Texture_SumVariance_GFP_3_00_256 0.0273121433275721 1.1782907982649247e-11 10.92874751388144 0.0034618532439579 -0.0232336885452703 0.1234032366216743 -0.1234032366216741 er -Nuclei_Texture_SumVariance_GFP_3_01_256 0.026746494222301 2.1021406837462564e-11 10.677338222594086 0.0033245339678202 -0.0210577480515928 0.1236710824186469 -0.1236710824186467 er -Nuclei_Texture_SumVariance_GFP_3_02_256 0.0269037594381387 1.7897683335377224e-11 10.747203180180705 0.0034498126501612 -0.0233257275841423 0.1222625994226781 -0.1222625994226779 er -Nuclei_Texture_SumVariance_GFP_3_03_256 0.0271455322640171 1.3974599255561769e-11 10.854660637365948 0.0034279011258513 -0.0227160563794688 0.1233856279334561 -0.123385627933456 er -Nuclei_Texture_Variance_GFP_3_00_256 0.0277239279415179 7.727263273693552e-12 11.111974290762022 0.0034820001462264 -0.0232963409461262 0.124420059395066 -0.1244200593950658 er -Nuclei_Texture_Variance_GFP_3_01_256 0.0274062875279413 1.0699774399824946e-11 10.97062537913199 0.0034328584390901 -0.0226058518180192 0.1241528735255075 -0.1241528735255073 er -Nuclei_Texture_Variance_GFP_3_02_256 0.0274856617489744 9.864220551492052e-12 11.00593722602859 0.0034690787623738 -0.0232356394161993 0.1238523662408014 -0.1238523662408012 er -Nuclei_Texture_Variance_GFP_3_03_256 0.0276031700847686 8.745337787274819e-12 11.05822341130636 0.0034423444095826 -0.0226329947707813 0.1246413329671 -0.1246413329670999 er -Cytoplasm_Granularity_10_DAPI 0.000870016615441 0.7734418488461786 0.1115723331276185 -0.0009939826604942 0.0114515540986338 -0.0157554230189558 0.0157554230189558 nucleus -Cytoplasm_Granularity_11_DAPI 0.0051809898488018 0.0300117573014642 1.5227085742524284 0.0051596487409142 -0.0869334855667527 -0.0313619039486935 0.031361903948693 nucleus 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-0.1209539980696384 0.1209539980696388 nucleus -Cytoplasm_Granularity_2_DAPI 0.0137839642457815 9.336330784925906e-06 5.029823769695859 -0.0037042151263353 0.0406142543482172 -0.0672000516145725 0.0672000516145726 nucleus -Cytoplasm_Granularity_3_DAPI 0.0048617616998444 0.0396037461476734 1.402263731894835 0.0044235432325038 -0.0704494031118965 -0.0100878049279847 0.0100878049279844 nucleus -Cytoplasm_Granularity_4_DAPI 0.0159400708316937 1.121782617685696e-06 5.950091293780658 0.0067714199807773 -0.1004284833460169 0.0150703652171353 -0.0150703652171358 nucleus -Cytoplasm_Granularity_5_DAPI 0.043594997218054 1.1102230246251563e-16 15.954589770191005 0.0083050245936444 -0.1063560884738781 0.0877039929003905 -0.0877039929003908 nucleus -Cytoplasm_Granularity_6_DAPI 0.0607240321011564 1.1102230246251563e-16 15.954589770191005 0.0067490812338666 -0.0652165035297863 0.158587549293555 -0.158587549293555 nucleus -Cytoplasm_Granularity_7_DAPI 0.0349536995212729 4.440892098500626e-15 14.35252977886304 0.0058852265837627 -0.0644108493827794 0.1072474042855896 -0.1072474042855897 nucleus -Cytoplasm_Granularity_8_DAPI 0.0066861163642601 0.0078473581698241 2.1052765248307685 0.0057002159760067 -0.0946984224127337 -0.0291202092379433 0.0291202092379427 nucleus -Cytoplasm_Granularity_9_DAPI 0.0018006015906264 0.4457760730461524 0.350883245911628 0.0013816319568592 -0.0348065883329415 -0.0558459935116626 0.0558459935116623 nucleus -Cytoplasm_Intensity_IntegratedIntensityEdge_DAPI 0.0907483122009253 1.1102230246251563e-16 15.954589770191005 -0.0044440883424144 0.0069483416481113 -0.2525789386187914 0.2525789386187909 nucleus -Cytoplasm_Intensity_IntegratedIntensity_DAPI 0.1554375053275413 1.1102230246251563e-16 15.954589770191005 -0.0121570301699023 0.1308535288442052 -0.2305906753237856 0.2305906753237857 nucleus -Cytoplasm_Intensity_LowerQuartileIntensity_DAPI 0.0145533039203713 4.3953341651858935e-06 5.357008101118225 0.0036484134858484 -0.0386397494313164 0.0717964289530455 -0.0717964289530456 nucleus -Cytoplasm_Intensity_MADIntensity_DAPI 0.0137186512545547 9.95147005378172e-06 5.002112759546983 0.0063879032438403 -0.0954276448319395 0.0113883960725047 -0.0113883960725052 nucleus -Cytoplasm_Intensity_MassDisplacement_DAPI 0.0088612076260969 0.0010525640419272 2.977751470590196 0.0071201686321433 -0.1240691627199306 -0.0601679987127482 0.0601679987127474 nucleus -Cytoplasm_Intensity_MaxIntensityEdge_DAPI 0.0681052500575941 1.1102230246251563e-16 15.954589770191005 0.0142206699499705 -0.2123613794717748 0.0256755778603181 -0.0256755778603192 nucleus -Cytoplasm_Intensity_MaxIntensity_DAPI 0.0567158631959061 1.1102230246251563e-16 15.954589770191005 0.0136327723103712 -0.207901491607797 0.0068358849943285 -0.0068358849943296 nucleus -Cytoplasm_Intensity_MeanIntensityEdge_DAPI 0.0587537591589149 1.1102230246251563e-16 15.954589770191005 0.0117448778138304 -0.1664404182389175 0.0580400872602967 -0.0580400872602974 nucleus -Cytoplasm_Intensity_MeanIntensity_DAPI 0.0301312082039212 6.509237593377293e-13 12.186469876006209 0.0076813363261682 -0.1041693197495304 0.0572437908207809 -0.0572437908207813 nucleus -Cytoplasm_Intensity_MedianIntensity_DAPI 0.0154190016703534 1.8759892819186772e-06 5.726769647202622 0.0046406886189963 -0.0572694434105138 0.0578989903145827 -0.0578989903145828 nucleus -Cytoplasm_Intensity_MinIntensityEdge_DAPI 0.014076177206968 7.015665123066839e-06 5.153931149213729 0.0037061756774387 -0.0403143548010241 0.0685584658131483 -0.0685584658131484 nucleus -Cytoplasm_Intensity_MinIntensity_DAPI 0.0144964563197543 4.647454896922199e-06 5.332784816319454 0.0037024188026605 -0.039761291975851 0.0705971439182453 -0.0705971439182453 nucleus -Cytoplasm_Intensity_StdIntensityEdge_DAPI 0.0426482776213259 1.1102230246251563e-16 15.954589770191005 0.0103992002834364 -0.1499929891919514 0.040595638146392 -0.0405956381463927 nucleus -Cytoplasm_Intensity_StdIntensity_DAPI 0.0831046757120393 1.1102230246251563e-16 15.954589770191005 0.0168982787862502 -0.2602898423537952 -0.0021827337080755 0.0021827337080742 nucleus -Cytoplasm_Intensity_UpperQuartileIntensity_DAPI 0.0222915653229681 1.951829364799096e-09 8.709558152430272 0.006842550853894 -0.0944269547936316 0.0442727820592619 -0.0442727820592623 nucleus -Cytoplasm_Location_CenterMassIntensity_X_DAPI 0.0008209714222029 0.7918911021386273 0.1013345368347035 0.0017187153484721 -0.026731761212607 -0.00128315129122 0.0012831512912198 nucleus -Cytoplasm_Location_CenterMassIntensity_Y_DAPI 0.0006408890857526 0.8576427977233153 0.0666935551334524 0.0018432529914507 -0.0358195007865007 -0.0308082745788126 0.0308082745788124 nucleus -Cytoplasm_Location_MaxIntensity_X_DAPI 0.0008102679022686 0.7958971037472358 0.0991430756835873 0.0016134144511718 -0.0245018953261707 0.0012324545692643 -0.0012324545692644 nucleus -Cytoplasm_Location_MaxIntensity_Y_DAPI 0.0005056024748221 0.9032356962374724 0.0441989072515796 0.0016747192388272 -0.032146462733196 -0.026353401283591 0.0263534012835908 nucleus -Cytoplasm_RadialDistribution_FracAtD_DAPI_1of4 0.0033613439476051 0.1389439494482449 0.8571603605703313 -0.0038471267348392 0.0624754445012082 0.0137373641732462 -0.0137373641732458 nucleus -Cytoplasm_RadialDistribution_FracAtD_DAPI_2of4 0.0050928602038524 0.0324092976938831 1.4893303799821649 -0.0041913481300919 0.0646134913945494 0.0007585235814501 -0.0007585235814497 nucleus -Cytoplasm_RadialDistribution_FracAtD_DAPI_3of4 0.0059102207703511 0.0157675950634059 1.8022345419494716 -0.0029871531658937 0.0377387103536038 -0.0336670472133818 0.033667047213382 nucleus -Cytoplasm_RadialDistribution_FracAtD_DAPI_4of4 0.0055559318793306 0.0215913544323158 1.665720113384583 0.0034735047252961 -0.0483237431786826 0.0208711516540354 -0.0208711516540356 nucleus -Cytoplasm_RadialDistribution_MeanFrac_DAPI_1of4 0.0261506703567337 3.864764064331894e-11 10.412877013695548 -0.008935160942624 0.1342284947627781 -0.012851504006392 0.0128515040063926 nucleus -Cytoplasm_RadialDistribution_MeanFrac_DAPI_2of4 0.0396575568004543 1.1102230246251563e-16 15.954589770191005 -0.0099202172848064 0.1423855120796844 -0.0416022888880808 0.0416022888880815 nucleus -Cytoplasm_RadialDistribution_MeanFrac_DAPI_3of4 0.0405728126673868 1.1102230246251563e-16 15.954589770191005 -0.010489485466232 0.1535798360924003 -0.0315447277749775 0.0315447277749782 nucleus -Cytoplasm_RadialDistribution_MeanFrac_DAPI_4of4 0.0447916481646161 1.1102230246251563e-16 15.954589770191005 0.009696631090553 -0.1337942566092568 0.0628170990401274 -0.0628170990401279 nucleus -Cytoplasm_RadialDistribution_RadialCV_DAPI_1of4 0.002401462940082 0.2916579799369576 0.5351261365143526 0.0037411443341174 -0.0657123768946159 -0.0337659160753935 0.0337659160753931 nucleus -Cytoplasm_RadialDistribution_RadialCV_DAPI_2of4 0.0061100707365426 0.0131891926851024 1.87978178695064 0.0045209749998067 -0.0692460989613586 0.0010294462891734 -0.0010294462891737 nucleus 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0.0035656436514082 0.1177887775434129 0.9288960854669354 -0.0024808854606974 0.0325503992834769 -0.0229902382978591 0.0229902382978592 nucleus -Cytoplasm_Texture_Correlation_DAPI_3_01_256 0.0011767970045039 0.6575761142527337 0.1820539704628871 -0.0003680393610205 -0.0017963477435139 -0.0306795561840553 0.0306795561840552 nucleus -Cytoplasm_Texture_Correlation_DAPI_3_02_256 0.0024564903995981 0.2800381376694643 0.5527828191880839 -0.0009588498215833 0.0054540275038673 -0.0382180519989423 0.0382180519989422 nucleus -Cytoplasm_Texture_Correlation_DAPI_3_03_256 0.0043189661455035 0.0630018918464003 1.2006464091794942 -0.0020469216964075 0.0222131431754425 -0.0380808798147118 0.0380808798147118 nucleus -Cytoplasm_Texture_DifferenceEntropy_DAPI_3_00_256 0.0826545759303291 1.1102230246251563e-16 15.954589770191005 0.0141200362493852 -0.2013553615155404 0.0646086336382305 -0.0646086336382313 nucleus -Cytoplasm_Texture_DifferenceEntropy_DAPI_3_01_256 0.0844083104550558 1.1102230246251563e-16 15.954589770191005 0.0137705454665068 -0.1931368949534853 0.0763230689728754 -0.0763230689728762 nucleus -Cytoplasm_Texture_DifferenceEntropy_DAPI_3_02_256 0.0812836597297852 1.1102230246251563e-16 15.954589770191005 0.012843936375498 -0.1757520813936137 0.0892513460090994 -0.0892513460091 nucleus -Cytoplasm_Texture_DifferenceEntropy_DAPI_3_03_256 0.0910510277971953 1.1102230246251563e-16 15.954589770191005 0.0143751385544322 -0.2021028070526888 0.0776725383669556 -0.0776725383669564 nucleus -Cytoplasm_Texture_DifferenceVariance_DAPI_3_00_256 0.1177045905575493 1.1102230246251563e-16 15.954589770191005 -0.0171448395430816 0.2464363145138506 -0.0704375442078454 0.0704375442078465 nucleus -Cytoplasm_Texture_DifferenceVariance_DAPI_3_01_256 0.1192104859494946 1.1102230246251563e-16 15.954589770191005 -0.0168402002626712 0.2393781165326421 -0.0802141375086571 0.0802141375086581 nucleus -Cytoplasm_Texture_DifferenceVariance_DAPI_3_02_256 0.1158341877094948 1.1102230246251563e-16 15.954589770191005 -0.0163566918115361 0.230912001051027 -0.0844685703805413 0.0844685703805423 nucleus -Cytoplasm_Texture_DifferenceVariance_DAPI_3_03_256 0.1225603683896436 1.1102230246251563e-16 15.954589770191005 -0.0169985685621454 0.2411260683184906 -0.0830396434099929 0.083039643409994 nucleus -Cytoplasm_Texture_Entropy_DAPI_3_00_256 0.1171840182715145 1.1102230246251563e-16 15.954589770191005 0.0163128527468437 -0.2293769386634864 0.0880131000093454 -0.0880131000093464 nucleus -Cytoplasm_Texture_Entropy_DAPI_3_01_256 0.112816511956711 1.1102230246251563e-16 15.954589770191005 0.0155599126200942 -0.2158536813975169 0.096035478482252 -0.096035478482253 nucleus -Cytoplasm_Texture_Entropy_DAPI_3_02_256 0.1162501489455088 1.1102230246251563e-16 15.954589770191005 0.0156747859083094 -0.2166419027801271 0.1000592477228857 -0.1000592477228866 nucleus -Cytoplasm_Texture_Entropy_DAPI_3_03_256 0.1150215627160796 1.1102230246251563e-16 15.954589770191005 0.0154946557221615 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nucleus -Cytoplasm_Texture_InverseDifferenceMoment_DAPI_3_02_256 0.044179195970177 1.1102230246251563e-16 15.954589770191005 -0.0087158840389015 0.1142371647515588 -0.0812610388935292 0.0812610388935296 nucleus -Cytoplasm_Texture_InverseDifferenceMoment_DAPI_3_03_256 0.0502629587592252 1.1102230246251563e-16 15.954589770191005 -0.0097134363404139 0.1303089775923876 -0.0782255843532165 0.078225584353217 nucleus -Cytoplasm_Texture_SumAverage_DAPI_3_00_256 0.0263701260211127 3.0885849433559536e-11 10.510240450041527 0.0066007858417579 -0.0855709390233005 0.0654271552599154 -0.0654271552599156 nucleus -Cytoplasm_Texture_SumAverage_DAPI_3_01_256 0.024818993493345 1.5025292121606526e-10 9.823177075693378 0.0062073590340344 -0.0790620890190263 0.0673250666945262 -0.0673250666945264 nucleus -Cytoplasm_Texture_SumAverage_DAPI_3_02_256 0.0262393892637595 3.5299541067956846e-11 10.45223094087137 0.0063621251858598 -0.0808833513752646 0.0696209220945693 -0.0696209220945695 nucleus 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0.0038431383801131 -0.0657243943193949 -0.0273622053145252 0.0273622053145248 nucleus -Nuclei_Granularity_1_DAPI 0.0209772259409326 7.3520817212013875e-09 8.13358967424578 -0.0061993898282064 0.0826420271135904 -0.052086027277638 0.0520860272776383 nucleus -Nuclei_Granularity_2_DAPI 0.0037835117576389 0.0985401886665081 1.006386610553651 -0.0014110090285422 0.040403287295253 0.0770230061406278 -0.0770230061406274 nucleus -Nuclei_Granularity_3_DAPI 0.0036630142691364 0.1087908700965319 0.9634075497947248 -0.0009752859441673 0.032575336067414 0.0723718369097393 -0.072371836909739 nucleus -Nuclei_Granularity_4_DAPI 0.0182797349522776 1.0986626330922179e-07 6.959135646138463 0.0052981924459828 -0.0672688209626301 0.0583424410580522 -0.0583424410580524 nucleus -Nuclei_Granularity_5_DAPI 0.0425166899390873 1.1102230246251563e-16 15.954589770191005 0.0059916196954536 -0.0612707392425114 0.1269034268811914 -0.1269034268811914 nucleus -Nuclei_Granularity_6_DAPI 0.0437994877316736 1.1102230246251563e-16 15.954589770191005 0.0013639773240873 0.0273265855595669 0.1987736988831182 -0.1987736988831176 nucleus -Nuclei_Granularity_7_DAPI 0.0024421870398085 0.2830204090444794 0.5481822456919325 0.0024586282171989 -0.0352683254640512 0.0103951865631879 -0.010395186563188 nucleus -Nuclei_Granularity_8_DAPI 0.0413460504214036 1.1102230246251563e-16 15.954589770191005 -0.0011290916728731 -0.0301713809356347 -0.1956214797153445 0.1956214797153439 nucleus -Nuclei_Granularity_9_DAPI 0.0558915233286919 1.1102230246251563e-16 15.954589770191005 -0.0080735242510514 0.0938935857458253 -0.124351913394571 0.1243519133945712 nucleus -Nuclei_Intensity_IntegratedIntensityEdge_DAPI 0.0228542265622032 1.1045678816046234e-09 8.95680758925346 0.0004892925233439 -0.046695877338283 -0.1612400566800259 0.1612400566800253 nucleus -Nuclei_Intensity_IntegratedIntensity_DAPI 0.0475693299673988 1.1102230246251563e-16 15.954589770191005 0.0001801497098509 -0.0578371098084201 -0.2266562502429379 0.2266562502429371 nucleus -Nuclei_Intensity_LowerQuartileIntensity_DAPI 0.0525246678520786 1.1102230246251563e-16 15.954589770191005 0.0132696164116575 -0.2033381033192607 0.0026416272827719 -0.002641627282773 nucleus -Nuclei_Intensity_MADIntensity_DAPI 0.0246797795680235 1.7312395961255334e-10 9.761642823473844 -0.0013814116132072 0.0642069498510372 0.1768701945059239 -0.1768701945059231 nucleus -Nuclei_Intensity_MassDisplacement_DAPI 0.0136329318404145 1.0820324182203578e-05 4.965759727359482 -0.0080246363102743 0.1324146545350882 0.0372918175193448 -0.0372918175193441 nucleus -Nuclei_Intensity_MaxIntensityEdge_DAPI 0.1217556525137212 1.1102230246251563e-16 15.954589770191005 0.0165244992114212 -0.2316672265126635 0.0919772957126571 -0.0919772957126581 nucleus -Nuclei_Intensity_MaxIntensity_DAPI 0.0688618426419983 1.1102230246251563e-16 15.954589770191005 0.0122406357375655 -0.1703732178889569 0.0732195566935554 -0.073219556693556 nucleus -Nuclei_Intensity_MeanIntensityEdge_DAPI 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0.0107260174410742 -0.1968374777766912 -0.1315359273494011 0.1315359273493998 nucleus -Nuclei_Intensity_StdIntensity_DAPI 0.0697536828001081 1.1102230246251563e-16 15.954589770191005 0.012662901353776 -0.1785674788078859 0.06620922166067 -0.0662092216606708 nucleus -Nuclei_Intensity_UpperQuartileIntensity_DAPI 0.0763346513260664 1.1102230246251563e-16 15.954589770191005 0.014152427251305 -0.2056988540954455 0.0487804407543161 -0.048780440754317 nucleus -Nuclei_Location_CenterMassIntensity_X_DAPI 0.000806859557383 0.7971709430418058 0.0984485397590421 0.0016819110405485 -0.0260170174382013 -0.0006699178584394 0.0006699178584393 nucleus -Nuclei_Location_CenterMassIntensity_Y_DAPI 0.0006627123432814 0.8499157357423742 0.0706241299517842 0.0019353611648498 -0.0369309283654412 -0.0295551553069797 0.0295551553069794 nucleus -Nuclei_Location_MaxIntensity_X_DAPI 0.0008768983972842 0.7708431263607122 0.1130339958633136 0.0018510127375679 -0.0292939674376238 -0.0034585807585359 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0.0071420287242603 0.0047348575496101 -0.00473485754961 other -Nuclei_AreaShape_Zernike_7_1 0.0035199212823608 0.1222473547458128 0.912760529600874 6.924406406573475e-05 0.013578412351945 0.0602690723530627 -0.0602690723530625 other -Nuclei_AreaShape_Zernike_7_3 0.0007192527460109 0.8295367449974073 0.0811643717901126 -0.0012705360148081 0.0284653997359059 0.0367750641371187 -0.0367750641371185 other -Nuclei_AreaShape_Zernike_7_5 0.0011631827702947 0.6626492239396506 0.1787163063180145 0.0006576303240817 -0.0037116077004293 0.0263315667231951 -0.0263315667231951 other -Nuclei_AreaShape_Zernike_7_7 0.0197135770475951 2.6177965350981932e-08 7.582064111459061 0.0006775549911709 0.0227146498962397 0.1363612152588314 -0.136361215258831 other -Nuclei_AreaShape_Zernike_8_0 0.0051594363289558 0.0305818150293641 1.5145367428005665 0.0002296052619261 0.0137712211609929 0.0712087322710311 -0.0712087322710309 other -Nuclei_AreaShape_Zernike_8_2 0.0053607318019716 0.0256407947360667 1.5910685179933397 -0.0020443568187237 0.0543320825141896 0.0942811758457915 -0.0942811758457911 other -Nuclei_AreaShape_Zernike_8_4 0.0116801846267449 7.195076015331647e-05 4.142964613420957 -0.0010667467572158 -0.0073882817970438 -0.0979029361319641 0.0979029361319639 other -Nuclei_AreaShape_Zernike_8_6 0.0175547925243153 2.262317444490236e-07 6.645446455680449 -0.001470186371717 -0.0060288512077876 -0.1178332611549944 0.1178332611549941 other -Nuclei_AreaShape_Zernike_8_8 0.0063251721998933 0.0108729285099353 1.963653467467348 -0.0015483558874261 0.047463678380535 0.0973932349106019 -0.0973932349106014 other -Nuclei_AreaShape_Zernike_9_1 0.0002404433776346 0.97390335498731 0.0114841380682068 -0.0005916732907774 0.0141577349622794 0.0208372228881954 -0.0208372228881953 other -Nuclei_AreaShape_Zernike_9_3 2.9651386554641057e-05 0.999542121561672 0.0001988996185474 0.0001028966190155 -0.0031955263466675 -0.0066423297721081 0.006642329772108 other -Nuclei_AreaShape_Zernike_9_5 0.0001408048281874 0.990427938907513 0.0041771171706634 0.0006605600038711 -0.0099549505709706 0.0008196743643961 -0.0008196743643962 other -Nuclei_AreaShape_Zernike_9_7 0.0070228135468319 0.0057768096415272 2.2383119431728646 -0.0009436291541522 0.0379295023331338 0.0964122171191108 -0.0964122171191104 other -Nuclei_AreaShape_Zernike_9_9 0.0258130266337673 5.455358387251863e-11 10.263176713362858 0.0031595675884058 -0.0186576407256283 0.1231123514055964 -0.1231123514055962 other -Nuclei_Location_Center_X 0.0008069496923879 0.7971372674489196 0.0984668864301202 0.0016800538744685 -0.0259756106069839 -0.0006169927237823 0.0006169927237821 other -Nuclei_Location_Center_Y 0.0006668564579445 0.8484390056290597 0.0713793737008968 0.0019437938319414 -0.0370611200344724 -0.0295574812896329 0.0295574812896326 other -Nuclei_Neighbors_AngleBetweenNeighbors_Adjacent 0.0043660451287296 0.0605397134945174 1.2179596386824607 0.0007011839528236 -0.0291456781129999 -0.0755980159815341 0.0755980159815338 other -Nuclei_Neighbors_FirstClosestDistance_Adjacent 0.0856396860329403 1.1102230246251563e-16 15.954589770191005 -0.014074114546091 0.198753900776209 -0.0724106310670432 0.0724106310670441 other -Nuclei_Neighbors_FirstClosestObjectNumber_Adjacent 0.1004532469683806 1.1102230246251563e-16 15.954589770191005 0.0206207764355481 -0.3319892943626297 -0.0617708970646947 0.0617708970646928 other -Nuclei_Neighbors_SecondClosestDistance_Adjacent 0.099410228021431 1.1102230246251563e-16 15.954589770191005 -0.0176682509921168 0.2670204041885691 -0.0188308674405852 0.0188308674405866 other -Nuclei_Neighbors_SecondClosestObjectNumber_Adjacent 0.0898125045297529 1.1102230246251563e-16 15.954589770191005 0.0189299877442896 -0.3009199271091334 -0.040867477040115 0.0408674770401134 other -Cytoplasm_Correlation_Correlation_GFP_RFP 0.0019958770431518 0.390060331207924 0.4088682148087051 0.0018136544577989 -0.0425696926526793 -0.0604643927558384 0.060464392755838 other 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0.0042001862452161 -0.1104968116943455 -0.1890525404802021 0.1890525404802012 other -Cytoplasm_Correlation_RWC_GFP_RFP 0.0270999588117504 1.4641954315663952e-11 10.834400952521928 0.0113375220203716 -0.2257210674327445 -0.2117292249742364 0.2117292249742348 other -Cytoplasm_Correlation_RWC_RFP_GFP 0.0382629139288177 1.1102230246251563e-16 15.954589770191005 0.0136698646391387 -0.2273715013984281 -0.0709547615938646 0.0709547615938632 other -Cells_Correlation_Correlation_GFP_RFP 0.0048085166469081 0.0414665818558966 1.3823017626177665 0.0053759444455265 -0.0962558041361731 -0.056046999362167 0.0560469993621664 other -Cells_Correlation_Costes_GFP_RFP 0.0176656942481279 2.0259232358377238e-07 6.6933770144958205 -0.0013201671095143 0.0570211990659021 0.1511689882872303 -0.1511689882872296 other -Cells_Correlation_K_GFP_RFP 0.0216990758102431 3.5511046325709117e-09 8.44963653111187 0.0070624668984834 -0.157669163703397 -0.2021145366499589 0.2021145366499576 other 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0.1181910975788669 0.0603729435911854 -0.0603729435911847 other -Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928 0.0342676558238695 1.4651156036990791 -0.0055078174892835 0.0990026149900095 0.0590090996317533 -0.0590090996317526 other -Cytoplasm_Correlation_Costes_DAPI_GFP 0.1266887522015375 1.1102230246251563e-16 15.954589770191005 -0.0077115767757019 0.2259752029684549 0.4421891625863014 -0.4421891625862992 other -Cytoplasm_Correlation_Costes_GFP_DAPI 0.0717113655632017 1.1102230246251563e-16 15.954589770191005 -0.0027105789835034 0.1157881765598752 0.3050791104063639 -0.3050791104063626 other -Cytoplasm_Correlation_K_DAPI_GFP 0.0087437943987151 0.0011750182388298 2.9299553921482704 -0.007234226804901 0.1290649328631211 0.0735138395974705 -0.0735138395974697 other -Cytoplasm_Correlation_K_GFP_DAPI 0.0055819277251202 0.0211011923569978 1.6756930034969135 0.0053386202296831 -0.0897989309351734 -0.0318323738340108 0.0318323738340103 other 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-0.0082808309727361 0.1441675777139306 0.0694567357186915 -0.0694567357186906 other -Cells_Correlation_Costes_DAPI_GFP 0.1287306386618018 1.1102230246251563e-16 15.954589770191005 -0.0159824554903969 0.3647135469087196 0.489929153154612 -0.4899291531546089 other -Cells_Correlation_Costes_GFP_DAPI 0.0725736544592836 1.1102230246251563e-16 15.954589770191005 -0.0026318368579147 0.1147914535206022 0.3059586759868545 -0.3059586759868531 other -Cells_Correlation_K_DAPI_GFP 0.0136828962432701 1.0305116863840167e-05 4.986947078796643 -0.0069037054251493 0.1448310793092576 0.1593185470532011 -0.1593185470532 other -Cells_Correlation_K_GFP_DAPI 0.0233561640861427 6.641966976417281e-10 9.177703288026311 0.0106113349978678 -0.2105839322869011 -0.195371542857158 0.1953715428571565 other -Cells_Correlation_Manders_DAPI_GFP 0.0293732964052595 1.4205303600078878e-12 11.8475494799678 0.0023528815075556 -0.0006521660881691 0.1461827762937873 -0.1461827762937869 other -Cells_Correlation_Manders_GFP_DAPI 0.0288016347820732 2.556732603409273e-12 11.592314690407887 0.0054258488493823 -0.0601105034701542 0.095882513249416 -0.0958825132494161 other -Cells_Correlation_Overlap_DAPI_GFP 0.0035468255613133 0.1196052879976049 0.9222496188642316 0.0036878280018297 -0.0762026908630778 -0.0803167972663356 0.080316797266335 other -Cells_Correlation_RWC_DAPI_GFP 0.0360072716847414 1.4432899320127037e-15 14.840646417884166 -0.0131163880035569 0.2607626491605052 0.2434094004456848 -0.243409400445683 other -Cells_Correlation_RWC_GFP_DAPI 0.0308150581209546 3.2163161023390785e-13 12.49264127498724 0.0046841958972369 -0.0440597428885552 0.1150220960993887 -0.1150220960993887 other -Nuclei_Correlation_Correlation_DAPI_GFP 0.0055975849365728 0.020811189718118 1.6817030916651352 -0.0027688599181534 0.0668973607803623 0.1001597794611853 -0.1001597794611848 other -Nuclei_Correlation_Costes_DAPI_GFP 0.0917366799612873 1.1102230246251563e-16 15.954589770191005 -0.0148851988489012 0.3298836164767035 0.415994629305783 -0.4159946293057804 other -Nuclei_Correlation_K_DAPI_GFP 0.016042896725708 1.0133859220662345e-06 5.994225133212328 -0.0052536283477645 0.1225024387479125 0.1718156460245471 -0.171815646024546 other -Nuclei_Correlation_K_GFP_DAPI 0.0285387179234908 3.3494318429916348e-12 11.475028855166148 0.0073553503104055 -0.1690029733733402 -0.2302331058924449 0.2302331058924435 other -Nuclei_Correlation_Manders_DAPI_GFP 0.0053512008800226 0.0258562561936261 1.5874343577291878 0.0030718253235459 -0.0405363457199373 0.0275093511301013 -0.0275093511301014 other -Nuclei_Correlation_Manders_GFP_DAPI 0.0007620641436754 0.8138200273288702 0.0894716267813413 -0.001236679277527 0.0275934027714221 0.0353280921836848 -0.0353280921836845 other -Nuclei_Correlation_Overlap_DAPI_GFP 0.0184591607692713 9.185241267584132e-08 7.036909431649485 -0.0044333294839917 0.0498850683396133 -0.0751730352919696 0.0751730352919697 other -Nuclei_Correlation_RWC_DAPI_GFP 0.0189891962562755 5.4080493305797006e-08 7.266959355546295 -0.0038782622974246 0.1027773274118242 0.1776477320738891 -0.1776477320738881 other -Nuclei_Correlation_RWC_GFP_DAPI 0.0233397702291401 6.753302361772738e-10 9.170483805424231 -0.003969839060068 0.1083301098895058 0.1947086060182426 -0.1947086060182416 other +feature r2_score p_value cell_count_coef plate_coef Null_coef WT_coef corrected_p_value neg_log_p compartment +Cytoplasm_Granularity_10_RFP 0.0037617125664257 0.1003252629539767 0.0035668092075674 -0.0547577118054782 0.0002926773188153 -0.0002926773188156 0.1270203165644132 0.896126809231911 actin +Cytoplasm_Granularity_11_RFP 0.0083140810700031 0.0017553507473393 0.0020349194384395 -0.0152096139187058 0.0661476342826508 -0.0661476342826508 0.00285829203038 2.543893401611021 actin +Cytoplasm_Granularity_12_RFP 0.0170346819190725 3.793612946001446e-07 -0.0086265789374558 0.1743870117677087 0.1719627433359708 -0.1719627433359696 8.636048294720938e-07 6.06368493765877 actin 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0.0265761238729764 2.5021873462094387e-11 0.0055619175677227 -0.0646411039510882 0.0858438944240999 -0.0858438944241001 7.190691853834551e-11 10.143229321877568 actin +Cytoplasm_Granularity_3_RFP 0.0247453188850123 1.6195356167258979e-10 0.0058182685634424 -0.0713392817216137 0.0744939196170529 -0.0744939196170531 4.434624648629168e-10 9.35314313342588 actin +Cytoplasm_Granularity_4_RFP 0.0237508762052655 4.450164681202296e-10 0.0064129908206857 -0.0842132596546174 0.0591334779903649 -0.0591334779903652 1.1795984463186907e-09 8.928265808140978 actin +Cytoplasm_Granularity_5_RFP 0.0152669315254935 2.179214255049189e-06 0.0056152854233033 -0.0771713489077849 0.0376466898321505 -0.0376466898321508 4.676650185050108e-06 5.330065114244632 actin +Cytoplasm_Granularity_6_RFP 0.0135583503755253 1.163737040010826e-05 0.0050243723061764 -0.0672611482336932 0.041049393082168 -0.0410493930821682 2.378694900444664e-05 4.623661258488416 actin +Cytoplasm_Granularity_7_RFP 0.0068161411470623 0.006973486447476 0.0014019186773225 -0.0053777353069579 0.0665650786828842 -0.066565078682884 0.0106529181125259 1.9725314113261163 actin +Cytoplasm_Granularity_8_RFP 0.0091773954485366 0.0007819895285646 0.001378595879782 -0.00155702630656 0.0808152695319881 -0.0808152695319879 0.0013138782093539 2.881444890070697 actin +Cytoplasm_Granularity_9_RFP 0.0118764346599471 5.95448596415471e-05 0.0020014519906748 -0.009960290523731 0.0856360697971641 -0.085636069797164 0.0001120447034746 3.950608668411528 actin +Cytoplasm_Intensity_IntegratedIntensityEdge_RFP 0.0410414795005028 1.1102230246251563e-16 0.0024254421868306 -0.0942501363970267 -0.2344703644148723 0.2344703644148713 4.0280279112181464e-16 15.394907528772336 actin +Cytoplasm_Intensity_IntegratedIntensity_RFP 0.0783838235819537 1.1102230246251563e-16 -0.0059602684243162 0.0411874016199504 -0.2075817341685001 0.2075817341684998 4.0280279112181464e-16 15.394907528772336 actin +Cytoplasm_Intensity_LowerQuartileIntensity_RFP 0.0117074913163893 7.008198051117187e-05 0.0080573271541586 -0.1388208934280313 -0.0615911889616559 0.061591188961655 0.0001310228331295 3.882653013977188 actin +Cytoplasm_Intensity_MADIntensity_RFP 0.0120110505928773 5.2287397473871344e-05 0.0063345186776424 -0.096996550023088 0.0015532221785253 -0.0015532221785258 9.951748929043382e-05 4.002100589255595 actin +Cytoplasm_Intensity_MassDisplacement_RFP 0.0068526086027598 0.0067459980964077 -0.0063225542477175 0.1111022845202778 0.0572621905582353 -0.0572621905582346 0.0103325907518857 1.985790771589916 actin +Cytoplasm_Intensity_MaxIntensityEdge_RFP 0.0041062181249255 0.0753599505618121 0.0045262376910301 -0.0756843357731082 -0.0251370088643017 0.0251370088643013 0.0966772404444904 1.0146757545907688 actin +Cytoplasm_Intensity_MaxIntensity_RFP 0.0006338886393454 0.8601030189140192 0.000867179392635 -0.0101453541758644 0.0131088137209483 -0.0131088137209483 0.90794788892552 0.0419390768110094 actin 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4.0280279112181464e-16 15.394907528772336 actin +Cytoplasm_Intensity_StdIntensityEdge_RFP 0.0093846222373806 0.0006432341403108 0.0055073526540247 -0.0837429900493568 0.003769261377484 -0.0037692613774845 0.0010854576117745 2.964387131520325 actin +Cytoplasm_Intensity_StdIntensity_RFP 0.0127078278707797 2.6629636778685573e-05 0.0053962153571932 -0.0760381255555585 0.0284503887020656 -0.0284503887020659 5.339725095000682e-05 4.272481101173216 actin +Cytoplasm_Intensity_UpperQuartileIntensity_RFP 0.0123650362768117 3.712815934120428e-05 0.0074269157328729 -0.1209599647446009 -0.0279627913135012 0.0279627913135005 7.293704398500536e-05 4.137051841886167 actin +Cytoplasm_Location_CenterMassIntensity_X_RFP 0.0007866405503756 0.804708211979182 0.0017169009877283 -0.0269326316982328 -0.0022247178774269 0.0022247178774267 0.8756009691732242 0.057693766397904 actin +Cytoplasm_Location_CenterMassIntensity_Y_RFP 0.000656748031542 0.8520359307733876 0.0018676072589494 -0.0362763848469504 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0.0070008625750292 -0.1010215190196223 0.0271463198430092 -0.0271463198430097 9.64937805438452e-08 7.015500677977812 other +Cells_Correlation_Overlap_DAPI_RFP 0.0300487556106749 7.086553566182374e-13 -0.002925585927757 0.010951848466946 -0.1400249217830361 0.1400249217830358 2.248652084207901e-12 11.648077734247408 other +Cells_Correlation_RWC_DAPI_RFP 0.0209599401776 7.48117667814796e-09 -0.0032093080671393 0.0237338072160324 -0.1053662164696798 0.1053662164696797 1.867880886737588e-08 7.728650821832879 other +Cells_Correlation_RWC_RFP_DAPI 0.0151474151660359 2.451352801524642e-06 0.0059058427178638 -0.0833146486867555 0.0307447981671124 -0.0307447981671128 5.241290244143848e-06 5.2805617899368125 other +Nuclei_Correlation_Correlation_DAPI_RFP 0.0168432113581571 4.5875122578475924e-07 0.0001420515628276 -0.0350490459478889 -0.135272315080336 0.1352723150803355 1.036206562521606e-06 5.984553661558746 other +Nuclei_Correlation_Costes_DAPI_RFP 0.0653722521256117 1.1102230246251563e-16 -0.0155537567470738 0.3232207860753828 0.3462710611474306 -0.3462710611474281 4.0280279112181464e-16 15.394907528772336 other +Nuclei_Correlation_Costes_RFP_DAPI 0.0179149635141995 1.580573694148768e-07 -0.0069226331361114 0.1511162345908681 0.1839903398971243 -0.1839903398971231 3.677447011837113e-07 6.434453576284332 other +Nuclei_Correlation_K_DAPI_RFP 0.0194414548671663 3.4387600544860675e-08 -0.0011421086465465 -0.0138844420153685 -0.1294089263510944 0.129408926351094 8.352302140707793e-08 7.078193803653505 other +Nuclei_Correlation_K_RFP_DAPI 0.0094306032780084 0.0006159131487293 -0.0025832658832332 0.0698811387485057 0.1241833980800298 -0.1241833980800292 0.0010423836234325 2.981972420308452 other +Nuclei_Correlation_Manders_DAPI_RFP 0.0003548282703975 0.9473520293555764 0.0012507589608186 -0.0252429773741715 -0.0247630483994902 0.02476304839949 0.9742831499118352 0.0113148084463755 other +Nuclei_Correlation_Manders_RFP_DAPI 0.0006703475639939 0.8471927376411222 -0.0010841604293917 0.0246815908186427 0.0329931187023357 -0.0329931187023355 0.905989610177744 0.0428767827518256 other +Nuclei_Correlation_Overlap_DAPI_RFP 0.0333243308966041 2.398081733190338e-14 -0.0059602732081601 0.0670969512739812 -0.1009397653269125 0.1009397653269126 8.140856410040885e-14 13.089329905357634 other +Nuclei_Correlation_RWC_DAPI_RFP 0.0094345830398724 0.0006136031128202 -0.0064997025887186 0.1059483776570577 0.0248408015139323 -0.0248408015139317 0.0010399900934076 2.9829707976232047 other +Nuclei_Correlation_RWC_RFP_DAPI 0.0077959130337511 0.0028392756370917 -0.0067345404406127 0.1181910975788669 0.0603729435911854 -0.0603729435911847 0.0045156150885801 2.345283084468512 other +Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928 0.0342676558238695 -0.0055078174892835 0.0990026149900095 0.0590090996317533 -0.0590090996317526 0.0463691706427885 1.3337706718092108 other +Cytoplasm_Correlation_Costes_DAPI_GFP 0.1266887522015375 1.1102230246251563e-16 -0.0077115767757019 0.2259752029684549 0.4421891625863014 -0.4421891625862992 4.0280279112181464e-16 15.394907528772336 other +Cytoplasm_Correlation_Costes_GFP_DAPI 0.0717113655632017 1.1102230246251563e-16 -0.0027105789835034 0.1157881765598752 0.3050791104063639 -0.3050791104063626 4.0280279112181464e-16 15.394907528772336 other +Cytoplasm_Correlation_K_DAPI_GFP 0.0087437943987151 0.0011750182388298 -0.007234226804901 0.1290649328631211 0.0735138395974705 -0.0735138395974697 0.0019488516789734 2.7102212124239538 other +Cytoplasm_Correlation_K_GFP_DAPI 0.0055819277251202 0.0211011923569978 0.0053386202296831 -0.0897989309351734 -0.0318323738340108 0.0318323738340103 0.0296591819933104 1.527840831078722 other +Cytoplasm_Correlation_Manders_DAPI_GFP 0.0568267726108727 1.1102230246251563e-16 0.0105186745166461 -0.1424403356219332 0.0792410890424681 -0.0792410890424686 4.0280279112181464e-16 15.394907528772336 other +Cytoplasm_Correlation_Manders_GFP_DAPI 0.0017588441984072 0.4583991703870796 -0.0019730427438016 0.0442960425345264 0.0574854399610166 -0.0574854399610163 0.5348758158988939 0.2717470380352514 other +Cytoplasm_Correlation_Overlap_DAPI_GFP 0.0052213862560118 0.028970805994453 -0.0027778261739751 0.0348727177146356 -0.0322191738170465 0.0322191738170466 0.0398992950884459 1.3990347770426366 other +Cytoplasm_Correlation_RWC_DAPI_GFP 0.0250899351958763 1.1402523369952178e-10 0.0102176355639956 -0.1638920903800971 -0.0280999111537201 0.0280999111537192 3.15198324583678e-10 9.501416099640595 other +Cytoplasm_Correlation_RWC_GFP_DAPI 0.0040815277114809 0.0769340543175093 0.0045670669000405 -0.0768384367681438 -0.0273039563786379 0.0273039563786374 0.0984789824284766 1.00665644756639 other +Cells_Correlation_Correlation_DAPI_GFP 0.0120121827110191 5.223024593559167e-05 -0.0082808309727361 0.1441675777139306 0.0694567357186915 -0.0694567357186906 9.951748929043382e-05 4.002100589255595 other +Cells_Correlation_Costes_DAPI_GFP 0.1287306386618018 1.1102230246251563e-16 -0.0159824554903969 0.3647135469087196 0.489929153154612 -0.4899291531546089 4.0280279112181464e-16 15.394907528772336 other +Cells_Correlation_Costes_GFP_DAPI 0.0725736544592836 1.1102230246251563e-16 -0.0026318368579147 0.1147914535206022 0.3059586759868545 -0.3059586759868531 4.0280279112181464e-16 15.394907528772336 other +Cells_Correlation_K_DAPI_GFP 0.0136828962432701 1.0305116863840167e-05 -0.0069037054251493 0.1448310793092576 0.1593185470532011 -0.1593185470532 2.113823441505024e-05 4.67493129024634 other +Cells_Correlation_K_GFP_DAPI 0.0233561640861427 6.641966976417281e-10 0.0106113349978678 -0.2105839322869011 -0.195371542857158 0.1953715428571565 1.7525735590046503e-09 8.756323744773926 other +Cells_Correlation_Manders_DAPI_GFP 0.0293732964052595 1.4205303600078878e-12 0.0023528815075556 -0.0006521660881691 0.1461827762937873 -0.1461827762937869 4.457393913430156e-12 11.35091898429622 other +Cells_Correlation_Manders_GFP_DAPI 0.0288016347820732 2.556732603409273e-12 0.0054258488493823 -0.0601105034701542 0.095882513249416 -0.0958825132494161 7.894591895101505e-12 11.102670315596974 other +Cells_Correlation_Overlap_DAPI_GFP 0.0035468255613133 0.1196052879976049 0.0036878280018297 -0.0762026908630778 -0.0803167972663356 0.080316797266335 0.1494744234286537 0.8254331131192995 other +Cells_Correlation_RWC_DAPI_GFP 0.0360072716847414 1.4432899320127037e-15 -0.0131163880035569 0.2607626491605052 0.2434094004456848 -0.243409400445683 5.124341318246938e-15 14.29036195080588 other +Cells_Correlation_RWC_GFP_DAPI 0.0308150581209546 3.2163161023390785e-13 0.0046841958972369 -0.0440597428885552 0.1150220960993887 -0.1150220960993887 1.0430567024624778e-12 11.98169208189254 other +Nuclei_Correlation_Correlation_DAPI_GFP 0.0055975849365728 0.020811189718118 -0.0027688599181534 0.0668973607803623 0.1001597794611853 -0.1001597794611848 0.0293130133835243 1.5329395337087124 other +Nuclei_Correlation_Costes_DAPI_GFP 0.0917366799612873 1.1102230246251563e-16 -0.0148851988489012 0.3298836164767035 0.415994629305783 -0.4159946293057804 4.0280279112181464e-16 15.394907528772336 other +Nuclei_Correlation_K_DAPI_GFP 0.016042896725708 1.0133859220662345e-06 -0.0052536283477645 0.1225024387479125 0.1718156460245471 -0.171815646024546 2.2157082024838008e-06 5.654487434555223 other +Nuclei_Correlation_K_GFP_DAPI 0.0285387179234908 3.3494318429916348e-12 0.0073553503104055 -0.1690029733733402 -0.2302331058924449 0.2302331058924435 1.0314828566878748e-11 10.986537985633367 other +Nuclei_Correlation_Manders_DAPI_GFP 0.0053512008800226 0.0258562561936261 0.0030718253235459 -0.0405363457199373 0.0275093511301013 -0.0275093511301014 0.0359080304315789 1.4448084154296303 other +Nuclei_Correlation_Manders_GFP_DAPI 0.0007620641436754 0.8138200273288702 -0.001236679277527 0.0275934027714221 0.0353280921836848 -0.0353280921836845 0.8838587948819628 0.0536171122515455 other +Nuclei_Correlation_Overlap_DAPI_GFP 0.0184591607692713 9.185241267584132e-08 -0.0044333294839917 0.0498850683396133 -0.0751730352919696 0.0751730352919697 2.176339818707179e-07 6.6622732919394245 other +Nuclei_Correlation_RWC_DAPI_GFP 0.0189891962562755 5.4080493305797006e-08 -0.0038782622974246 0.1027773274118242 0.1776477320738891 -0.1776477320738881 1.291922895638484e-07 6.888763405070014 other +Nuclei_Correlation_RWC_GFP_DAPI 0.0233397702291401 6.753302361772738e-10 -0.003969839060068 0.1083301098895058 0.1947086060182426 -0.1947086060182416 1.777910213609558e-09 8.750090175153497 other diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv index 64befb7..651db2d 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv @@ -1,885 +1,864 @@ -feature r2_score p_value -log_p cell_count_coef plate_coef Null_coef WT_coef compartment -Cytoplasm_Granularity_11_RFP 0.0083140810700031 0.0017553507473393 2.75563609149773 0.0020349194384395 -0.0152096139187058 0.0661476342826508 -0.0661476342826508 actin -Cytoplasm_Granularity_12_RFP 0.0170346819190725 3.793612946001446e-07 6.420946981299408 -0.0086265789374558 0.1743870117677087 0.1719627433359708 -0.1719627433359696 actin -Cytoplasm_Granularity_13_RFP 0.0165343642920898 6.230874355361848e-07 6.205451006139428 -0.0013168894588963 0.0558714466217581 0.1466440481927371 -0.1466440481927365 actin -Cytoplasm_Granularity_14_RFP 0.0098501891702583 0.0004140812515598 3.382914432691137 0.0048833782463788 -0.0696969351968968 0.0221031201207769 -0.0221031201207772 actin -Cytoplasm_Granularity_15_RFP 0.034125533803847 1.0436096431476471e-14 13.981461916591304 -0.0117844982540997 0.2411427339495568 0.2469237178768606 -0.2469237178768588 actin -Cytoplasm_Granularity_16_RFP 0.0053252103386409 0.0264527395149072 1.5775293446269791 -0.0011101117449085 0.0381216351097182 0.0866696542894571 -0.0866696542894567 actin -Cytoplasm_Granularity_1_RFP 0.0797662447888054 1.1102230246251563e-16 15.954589770191005 0.0128883932375536 -0.2919905507118198 -0.3863669662163332 0.3863669662163309 actin -Cytoplasm_Granularity_2_RFP 0.0265761238729764 2.5021873462094387e-11 10.601680176505536 0.0055619175677227 -0.0646411039510882 0.0858438944240999 -0.0858438944241001 actin -Cytoplasm_Granularity_3_RFP 0.0247453188850123 1.6195356167258979e-10 9.790609496571722 0.0058182685634424 -0.0713392817216137 0.0744939196170529 -0.0744939196170531 actin -Cytoplasm_Granularity_4_RFP 0.0237508762052655 4.450164681202296e-10 9.351623917375452 0.0064129908206857 -0.0842132596546174 0.0591334779903649 -0.0591334779903652 actin -Cytoplasm_Granularity_5_RFP 0.0152669315254935 2.179214255049189e-06 5.661700068876637 0.0056152854233033 -0.0771713489077849 0.0376466898321505 -0.0376466898321508 actin -Cytoplasm_Granularity_6_RFP 0.0135583503755253 1.163737040010826e-05 4.934145142515971 0.0050243723061764 -0.0672611482336932 0.041049393082168 -0.0410493930821682 actin -Cytoplasm_Granularity_7_RFP 0.0068161411470623 0.006973486447476 2.156550038783899 0.0014019186773225 -0.0053777353069579 0.0665650786828842 -0.066565078682884 actin -Cytoplasm_Granularity_8_RFP 0.0091773954485366 0.0007819895285646 3.1067990624350723 0.001378595879782 -0.00155702630656 0.0808152695319881 -0.0808152695319879 actin -Cytoplasm_Granularity_9_RFP 0.0118764346599471 5.95448596415471e-05 4.22515572411686 0.0020014519906748 -0.009960290523731 0.0856360697971641 -0.085636069797164 actin -Cytoplasm_Intensity_IntegratedIntensityEdge_RFP 0.0410414795005028 1.1102230246251563e-16 15.954589770191005 0.0024254421868306 -0.0942501363970267 -0.2344703644148723 0.2344703644148713 actin -Cytoplasm_Intensity_IntegratedIntensity_RFP 0.0783838235819537 1.1102230246251563e-16 15.954589770191005 -0.0059602684243162 0.0411874016199504 -0.2075817341685001 0.2075817341684998 actin -Cytoplasm_Intensity_LowerQuartileIntensity_RFP 0.0117074913163893 7.008198051117187e-05 4.154393633539182 0.0080573271541586 -0.1388208934280313 -0.0615911889616559 0.061591188961655 actin -Cytoplasm_Intensity_MADIntensity_RFP 0.0120110505928773 5.2287397473871344e-05 4.281602973983402 0.0063345186776424 -0.096996550023088 0.0015532221785253 -0.0015532221785258 actin -Cytoplasm_Intensity_MassDisplacement_RFP 0.0068526086027598 0.0067459980964077 2.1709537856964363 -0.0063225542477175 0.1111022845202778 0.0572621905582353 -0.0572621905582346 actin -Cytoplasm_Intensity_MeanIntensityEdge_RFP 0.0215595384756238 4.088002825142212e-09 8.388488812740455 0.0109928339234933 -0.1900924928704129 -0.0868924366320079 0.0868924366320068 actin -Cytoplasm_Intensity_MeanIntensity_RFP 0.0131344051062545 1.7591094030544596e-05 4.754707149931198 0.0077512073248387 -0.1269587121176591 -0.0321353408029062 0.0321353408029054 actin -Cytoplasm_Intensity_MedianIntensity_RFP 0.0124893630682384 3.2914871870848295e-05 4.482607831165609 0.0077828123652436 -0.1292338032851874 -0.0394998358868462 0.0394998358868455 actin -Cytoplasm_Intensity_MinIntensityEdge_RFP 0.0334153258585635 2.1871393585115584e-14 13.66012354402941 0.0141534714020136 -0.2528387414662714 -0.1451786615535209 0.1451786615535193 actin -Cytoplasm_Intensity_MinIntensity_RFP 0.0459096749019718 1.1102230246251563e-16 15.954589770191005 0.0139862277294537 -0.2254965389048376 -0.0432211944325902 0.0432211944325889 actin -Cytoplasm_Intensity_StdIntensityEdge_RFP 0.0093846222373806 0.0006432341403108 3.1916309130233875 0.0055073526540247 -0.0837429900493568 0.003769261377484 -0.0037692613774845 actin -Cytoplasm_Intensity_StdIntensity_RFP 0.0127078278707797 2.6629636778685573e-05 4.574634757184354 0.0053962153571932 -0.0760381255555585 0.0284503887020656 -0.0284503887020659 actin -Cytoplasm_Intensity_UpperQuartileIntensity_RFP 0.0123650362768117 3.712815934120428e-05 4.430296580743485 0.0074269157328729 -0.1209599647446009 -0.0279627913135012 0.0279627913135005 actin -Cytoplasm_RadialDistribution_MeanFrac_RFP_2of4 0.0084713135521656 0.0015159805127747 2.819306381321887 0.0061519844039941 -0.1002295009106718 -0.0233023869662074 0.0233023869662069 actin -Cytoplasm_RadialDistribution_MeanFrac_RFP_3of4 0.0075221400003641 0.003655092389363 2.437101640954156 0.0049933179932158 -0.0763330785476995 0.0017450207236831 -0.0017450207236835 actin -Cytoplasm_RadialDistribution_MeanFrac_RFP_4of4 0.0121196455642041 4.707829976413613e-05 4.3271792301189 -0.0072206643421422 0.1166612137912706 0.023318917228897 -0.0233189172288963 actin -Cytoplasm_RadialDistribution_RadialCV_RFP_4of4 0.0127380083940388 2.586076535893156e-05 4.587358626159338 0.0005067883890415 0.0189556314604897 0.1100848830106401 -0.1100848830106398 actin 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0.0020246538679575 0.0008717478859589 0.1316881251877244 -0.1316881251877241 er -Nuclei_Granularity_6_GFP 0.0336393783253614 1.7319479184152442e-14 13.76146517183654 0.0026079105487608 -0.002389021545254 0.1551696891743262 -0.1551696891743258 er -Nuclei_Granularity_7_GFP 0.0319737751080911 9.714451465470121e-14 13.01258171716869 0.0017541313298017 0.0124256083830203 0.1621271862390977 -0.1621271862390972 er -Nuclei_Granularity_8_GFP 0.0277981370989001 7.161160553437185e-12 11.145016589268952 -0.0005924556900318 0.0524542990203577 0.1784142572218567 -0.1784142572218561 er -Nuclei_Granularity_9_GFP 0.0115538795820173 8.125845363116113e-05 4.090131446664343 -0.0009142568325005 0.0433792456997945 0.120701448833836 -0.1207014488338355 er -Nuclei_Intensity_IntegratedIntensity_GFP 0.0133555394922149 1.4182535563400656e-05 4.8482461187472685 -0.0066750405841573 0.1021809859200496 -0.0017592273049611 0.0017592273049617 er -Nuclei_Intensity_LowerQuartileIntensity_GFP 0.0227759873165018 1.1956294843074031e-09 8.92240338376815 0.0035798066247081 -0.0292410197769799 0.106140316528097 -0.1061403165280969 er -Nuclei_Intensity_MADIntensity_GFP 0.0362277297160412 1.2212453270876722e-15 14.913197085032778 0.0042021263878888 -0.0308742721620247 0.1387921757987666 -0.1387921757987664 er -Nuclei_Intensity_MassDisplacement_GFP 0.0096741517943481 0.000489240693167 3.3104774271817634 -0.0034337498625744 0.0845334091258832 0.130681122375395 -0.1306811223753943 er -Nuclei_Intensity_MaxIntensityEdge_GFP 0.0291885683711662 1.7177370637000422e-12 11.765043313452075 0.0011990344062323 0.0207254783487745 0.1611679517355885 -0.1611679517355881 er -Nuclei_Intensity_MaxIntensity_GFP 0.0192017955919826 4.3716261055593015e-08 7.359356989274074 0.0006578234425281 0.0226181533329302 0.1347156236292949 -0.1347156236292945 er -Nuclei_Intensity_MeanIntensityEdge_GFP 0.024274777082504 2.613653737881805e-10 9.582751949153366 0.0028291159353274 -0.0136267332790232 0.1229115776539965 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0.0032422499416768 -0.0129384774877327 0.1518834315920101 -0.1518834315920099 er -Nuclei_Intensity_UpperQuartileIntensity_GFP 0.0350890020904278 3.885780586188048e-15 14.410521725840727 0.0041190387135415 -0.0300686622452913 0.1368510558712558 -0.1368510558712556 er -Nuclei_RadialDistribution_FracAtD_GFP_3of4 0.0048969779693816 0.0384160573647447 1.4154872087767008 -0.0023899041402115 0.0277953169677985 -0.0368054962384769 0.036805496238477 er -Nuclei_RadialDistribution_MeanFrac_GFP_1of4 0.0079832905773413 0.0023870787769738 2.6221332484249533 -0.0027751779644353 0.0300482507631232 -0.0519088601216217 0.0519088601216218 er -Nuclei_RadialDistribution_MeanFrac_GFP_2of4 0.009003700740782 0.0009207707953626 3.035848463937317 -0.0026698058798551 0.0264923935141667 -0.0598776689708322 0.0598776689708322 er -Nuclei_RadialDistribution_MeanFrac_GFP_3of4 0.0049960398092646 0.0352557058781309 1.4527705855988164 -0.0035679489674032 0.0515188561564808 -0.0136957405260553 0.0136957405260555 er -Nuclei_RadialDistribution_MeanFrac_GFP_4of4 0.0124746218179708 3.338850391965131e-05 4.47640304048431 0.0040794005649106 -0.049622324311449 0.0538613559261705 -0.0538613559261706 er -Nuclei_RadialDistribution_RadialCV_GFP_2of4 0.0052672339487742 0.0278315653991271 1.555462365887177 0.0020038088523175 -0.0195198881160578 0.0464384143930551 -0.0464384143930551 er -Nuclei_RadialDistribution_RadialCV_GFP_3of4 0.0282967770263175 4.293898570040256e-12 11.367148218631044 0.0018719405801501 0.0075538925629502 0.1495292771445221 -0.1495292771445218 er -Nuclei_RadialDistribution_RadialCV_GFP_4of4 0.0272904657971755 1.204736310711496e-11 10.919107999839982 0.0007948371717584 0.0267691880534155 0.1604699254441983 -0.1604699254441978 er -Nuclei_Texture_Contrast_GFP_3_00_256 0.0252898366128905 9.301304171316359e-11 10.03145615309164 0.0034441620494981 -0.0245171112339967 0.117001416211123 -0.1170014162111228 er -Nuclei_Texture_Contrast_GFP_3_01_256 0.0246910775184046 1.711448760488565e-10 9.766636098787387 0.0039591893880754 -0.0349159342349973 0.1067862694831461 -0.106786269483146 er -Nuclei_Texture_Contrast_GFP_3_02_256 0.0263763247356724 3.0690783248132896e-11 10.512992027957832 0.0033770816121343 -0.0223555391249407 0.1216541411104448 -0.1216541411104446 er -Nuclei_Texture_Contrast_GFP_3_03_256 0.0232817994718105 7.162140880367929e-10 9.144957140603555 0.0032095889894389 -0.0217062260606255 0.113729402201615 -0.1137294022016149 er -Nuclei_Texture_Correlation_GFP_3_00_256 0.0240455909807728 3.299109874177475e-10 9.481603220388465 0.0003060790153853 0.0332029995891211 0.1560273584307004 -0.1560273584306998 er -Nuclei_Texture_Correlation_GFP_3_01_256 0.019808734827597 2.3794896053530582e-08 7.6235161880485265 0.000856946541576 0.0194917578397252 0.134446091943645 -0.1344460919436446 er -Nuclei_Texture_Correlation_GFP_3_02_256 0.016125426457502 9.33979426576137e-07 6.029662690175544 0.0021103900278328 -0.0074027906801439 0.1030551183732843 -0.1030551183732841 er -Nuclei_Texture_Correlation_GFP_3_03_256 0.020119529146959 1.741826916035194e-08 7.7589950026839585 0.0001536083808883 0.0326812269006805 0.1442329381613281 -0.1442329381613276 er -Nuclei_Texture_DifferenceEntropy_GFP_3_00_256 0.0433696940374218 1.1102230246251563e-16 15.954589770191005 0.0044960465679119 -0.0318337451827609 0.1534393981027048 -0.1534393981027046 er -Nuclei_Texture_DifferenceEntropy_GFP_3_01_256 0.0439814529922998 1.1102230246251563e-16 15.954589770191005 0.00428361531298 -0.0273933518574876 0.1582752637003458 -0.1582752637003456 er -Nuclei_Texture_DifferenceEntropy_GFP_3_02_256 0.0460060778339831 1.1102230246251563e-16 15.954589770191005 0.0043668947942816 -0.0277456430582258 0.1620943540040856 -0.1620943540040853 er -Nuclei_Texture_DifferenceEntropy_GFP_3_03_256 0.0446660444164624 1.1102230246251563e-16 15.954589770191005 0.0047108729866538 -0.0351435405868524 0.1534085531980423 -0.1534085531980421 er -Nuclei_Texture_DifferenceVariance_GFP_3_00_256 0.0276291535841496 8.515521621177413e-12 11.069788744030149 -0.0026551271917499 0.0076619688223906 -0.1364539320698558 0.1364539320698555 er -Nuclei_Texture_DifferenceVariance_GFP_3_01_256 0.0275834931904239 8.923417560424696e-12 11.049468784258725 -0.0023003805434663 0.0010136237727541 -0.1413732950654785 0.1413732950654782 er -Nuclei_Texture_DifferenceVariance_GFP_3_02_256 0.0281423867624462 5.030975636088897e-12 11.298347785862042 -0.0026235751081896 0.0066737150738123 -0.1385252261869459 0.1385252261869456 er -Nuclei_Texture_DifferenceVariance_GFP_3_03_256 0.0279369851871421 6.210920666660513e-12 11.20684401804983 -0.0028043723756456 0.0102470250558275 -0.1352567419592744 0.1352567419592741 er -Nuclei_Texture_Entropy_GFP_3_00_256 0.038414069245105 1.1102230246251563e-16 15.954589770191005 0.0029690304138087 -0.0059833334410769 0.1632237915910936 -0.1632237915910933 er -Nuclei_Texture_Entropy_GFP_3_01_256 0.0377875600961979 2.220446049250313e-16 15.653559774527022 0.0028613715737035 -0.0043640085196843 0.163077257366359 -0.1630772573663586 er -Nuclei_Texture_Entropy_GFP_3_02_256 0.0395460102831258 1.1102230246251563e-16 15.954589770191005 0.0031121926734525 -0.0079519711698065 0.164178878663723 -0.1641788786637227 er -Nuclei_Texture_Entropy_GFP_3_03_256 0.0383856551851717 1.1102230246251563e-16 15.954589770191005 0.0027233461874612 -0.0013773073547442 0.1666374735274694 -0.166637473527469 er -Nuclei_Texture_InfoMeas1_GFP_3_00_256 0.0476275173115742 1.1102230246251563e-16 15.954589770191005 -0.0037213057859406 0.0145088755129118 -0.1757296729925979 0.1757296729925976 er -Nuclei_Texture_InfoMeas1_GFP_3_01_256 0.0486748385420504 1.1102230246251563e-16 15.954589770191005 -0.004221468065053 0.0233884446225615 -0.1708271772993806 0.1708271772993803 er -Nuclei_Texture_InfoMeas1_GFP_3_02_256 0.0425930840313972 1.1102230246251563e-16 15.954589770191005 -0.003441340231715 0.0122483316216863 -0.1673204969531264 0.1673204969531261 er -Nuclei_Texture_InfoMeas1_GFP_3_03_256 0.0470017486549151 1.1102230246251563e-16 15.954589770191005 -0.0038640122955209 0.0175825060988873 -0.1721078353053003 0.1721078353053 er -Nuclei_Texture_InfoMeas2_GFP_3_00_256 0.0482459033039358 1.1102230246251563e-16 15.954589770191005 0.0022127640103152 0.014182644618496 0.198376787106507 -0.1983767871065065 er -Nuclei_Texture_InfoMeas2_GFP_3_01_256 0.053993334070295 1.1102230246251563e-16 15.954589770191005 0.0025797824025081 0.0105467615530296 0.2066329853123887 -0.2066329853123882 er -Nuclei_Texture_InfoMeas2_GFP_3_02_256 0.0417517405701255 1.1102230246251563e-16 15.954589770191005 0.0012260094624569 0.028632671698448 0.1954202362568485 -0.1954202362568479 er -Nuclei_Texture_InfoMeas2_GFP_3_03_256 0.048985743767752 1.1102230246251563e-16 15.954589770191005 0.0016234621431986 0.0255508881441465 0.2078826579421669 -0.2078826579421663 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_00_256 0.0417470028408092 1.1102230246251563e-16 15.954589770191005 -0.0048714989206076 0.0400681719640582 -0.1433018625910618 0.1433018625910617 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_01_256 0.0423498082841684 1.1102230246251563e-16 15.954589770191005 -0.0048352401929733 0.0389846940027606 -0.1454673044379447 0.1454673044379445 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_02_256 0.0461546362499335 1.1102230246251563e-16 15.954589770191005 -0.005229982588979 0.0442057334349798 -0.1489531874058865 0.1489531874058864 er -Nuclei_Texture_InverseDifferenceMoment_GFP_3_03_256 0.0451147216085364 1.1102230246251563e-16 15.954589770191005 -0.0050563259195176 0.0415018287834804 -0.149095033812476 0.1490950338124759 er -Nuclei_Texture_SumAverage_GFP_3_00_256 0.0306176419805944 3.9424019604439314e-13 12.404239097889388 0.0038052075091054 -0.0272745813128624 0.1284952055626233 -0.1284952055626231 er -Nuclei_Texture_SumAverage_GFP_3_01_256 0.0304288667133351 4.789502128232925e-13 12.319709629424477 0.0037680656160631 -0.0267042494334882 0.1284926885876279 -0.1284926885876278 er -Nuclei_Texture_SumAverage_GFP_3_02_256 0.0305697168381128 4.142242104876459e-13 12.382764521150174 0.0038121272573545 -0.0274427780306701 0.1282407303726188 -0.1282407303726187 er -Nuclei_Texture_SumAverage_GFP_3_03_256 0.0305995463279852 4.0167869030938164e-13 12.396121207667209 0.0037510959464925 -0.026252344199094 0.1292790327611029 -0.1292790327611028 er -Nuclei_Texture_SumEntropy_GFP_3_00_256 0.0450373077286938 1.1102230246251563e-16 15.954589770191005 0.0035521679966113 -0.012849580737742 0.1718578730105756 -0.1718578730105752 er -Nuclei_Texture_SumEntropy_GFP_3_01_256 0.0449507876654683 1.1102230246251563e-16 15.954589770191005 0.0034642408444688 -0.0112389829242032 0.1729238444268886 -0.1729238444268883 er -Nuclei_Texture_SumEntropy_GFP_3_02_256 0.0446701910807069 1.1102230246251563e-16 15.954589770191005 0.0035153735208137 -0.012375479328181 0.1714812578759525 -0.1714812578759522 er -Nuclei_Texture_SumEntropy_GFP_3_03_256 0.045444379950204 1.1102230246251563e-16 15.954589770191005 0.0032941897808161 -0.0077313014014206 0.1766021189744544 -0.176602118974454 er -Nuclei_Texture_SumVariance_GFP_3_00_256 0.0273121433275721 1.1782907982649247e-11 10.92874751388144 0.0034618532439579 -0.0232336885452703 0.1234032366216743 -0.1234032366216741 er -Nuclei_Texture_SumVariance_GFP_3_01_256 0.026746494222301 2.1021406837462564e-11 10.677338222594086 0.0033245339678202 -0.0210577480515928 0.1236710824186469 -0.1236710824186467 er -Nuclei_Texture_SumVariance_GFP_3_02_256 0.0269037594381387 1.7897683335377224e-11 10.747203180180705 0.0034498126501612 -0.0233257275841423 0.1222625994226781 -0.1222625994226779 er -Nuclei_Texture_SumVariance_GFP_3_03_256 0.0271455322640171 1.3974599255561769e-11 10.854660637365948 0.0034279011258513 -0.0227160563794688 0.1233856279334561 -0.123385627933456 er -Nuclei_Texture_Variance_GFP_3_00_256 0.0277239279415179 7.727263273693552e-12 11.111974290762022 0.0034820001462264 -0.0232963409461262 0.124420059395066 -0.1244200593950658 er -Nuclei_Texture_Variance_GFP_3_01_256 0.0274062875279413 1.0699774399824946e-11 10.97062537913199 0.0034328584390901 -0.0226058518180192 0.1241528735255075 -0.1241528735255073 er -Nuclei_Texture_Variance_GFP_3_02_256 0.0274856617489744 9.864220551492052e-12 11.00593722602859 0.0034690787623738 -0.0232356394161993 0.1238523662408014 -0.1238523662408012 er -Nuclei_Texture_Variance_GFP_3_03_256 0.0276031700847686 8.745337787274819e-12 11.05822341130636 0.0034423444095826 -0.0226329947707813 0.1246413329671 -0.1246413329670999 er -Cytoplasm_Granularity_11_DAPI 0.0051809898488018 0.0300117573014642 1.5227085742524284 0.0051596487409142 -0.0869334855667527 -0.0313619039486935 0.031361903948693 nucleus -Cytoplasm_Granularity_16_DAPI 0.009731631679994 0.0004633240051676 3.3341151980878285 0.0072266327214183 -0.1232412996033381 -0.0500245660562457 0.050024566056245 nucleus -Cytoplasm_Granularity_1_DAPI 0.0764727236806905 1.1102230246251563e-16 15.954589770191005 -0.0107528012761244 0.1359048529382928 -0.1209539980696384 0.1209539980696388 nucleus -Cytoplasm_Granularity_2_DAPI 0.0137839642457815 9.336330784925906e-06 5.029823769695859 -0.0037042151263353 0.0406142543482172 -0.0672000516145725 0.0672000516145726 nucleus -Cytoplasm_Granularity_3_DAPI 0.0048617616998444 0.0396037461476734 1.402263731894835 0.0044235432325038 -0.0704494031118965 -0.0100878049279847 0.0100878049279844 nucleus -Cytoplasm_Granularity_4_DAPI 0.0159400708316937 1.121782617685696e-06 5.950091293780658 0.0067714199807773 -0.1004284833460169 0.0150703652171353 -0.0150703652171358 nucleus -Cytoplasm_Granularity_5_DAPI 0.043594997218054 1.1102230246251563e-16 15.954589770191005 0.0083050245936444 -0.1063560884738781 0.0877039929003905 -0.0877039929003908 nucleus -Cytoplasm_Granularity_6_DAPI 0.0607240321011564 1.1102230246251563e-16 15.954589770191005 0.0067490812338666 -0.0652165035297863 0.158587549293555 -0.158587549293555 nucleus 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9.95147005378172e-06 5.002112759546983 0.0063879032438403 -0.0954276448319395 0.0113883960725047 -0.0113883960725052 nucleus -Cytoplasm_Intensity_MassDisplacement_DAPI 0.0088612076260969 0.0010525640419272 2.977751470590196 0.0071201686321433 -0.1240691627199306 -0.0601679987127482 0.0601679987127474 nucleus -Cytoplasm_Intensity_MaxIntensityEdge_DAPI 0.0681052500575941 1.1102230246251563e-16 15.954589770191005 0.0142206699499705 -0.2123613794717748 0.0256755778603181 -0.0256755778603192 nucleus -Cytoplasm_Intensity_MaxIntensity_DAPI 0.0567158631959061 1.1102230246251563e-16 15.954589770191005 0.0136327723103712 -0.207901491607797 0.0068358849943285 -0.0068358849943296 nucleus -Cytoplasm_Intensity_MeanIntensityEdge_DAPI 0.0587537591589149 1.1102230246251563e-16 15.954589770191005 0.0117448778138304 -0.1664404182389175 0.0580400872602967 -0.0580400872602974 nucleus -Cytoplasm_Intensity_MeanIntensity_DAPI 0.0301312082039212 6.509237593377293e-13 12.186469876006209 0.0076813363261682 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0.1150215627160796 1.1102230246251563e-16 15.954589770191005 0.0154946557221615 -0.2134989828844715 0.1015984764931308 -0.1015984764931317 nucleus -Cytoplasm_Texture_InfoMeas1_DAPI_3_00_256 0.0600121374485065 1.1102230246251563e-16 15.954589770191005 -0.0083660249656829 0.0972967948559864 -0.1288510435657122 0.1288510435657124 nucleus -Cytoplasm_Texture_InfoMeas1_DAPI_3_01_256 0.049270990992515 1.1102230246251563e-16 15.954589770191005 -0.0078007871169399 0.0925251069944339 -0.1127283895283163 0.1127283895283165 nucleus -Cytoplasm_Texture_InfoMeas1_DAPI_3_02_256 0.0610097630426167 1.1102230246251563e-16 15.954589770191005 -0.0093706674861941 0.1167079152719218 -0.1125197828634823 0.1125197828634827 nucleus -Cytoplasm_Texture_InfoMeas1_DAPI_3_03_256 0.0401929324024999 1.1102230246251563e-16 15.954589770191005 -0.006877640036297 0.0802400771059454 -0.1048852811501506 0.1048852811501507 nucleus -Cytoplasm_Texture_InfoMeas2_DAPI_3_00_256 0.145569616111736 1.1102230246251563e-16 15.954589770191005 0.0139298299876936 -0.1694013064653288 0.1840953848164411 -0.1840953848164415 nucleus -Cytoplasm_Texture_InfoMeas2_DAPI_3_01_256 0.1441791804717083 1.1102230246251563e-16 15.954589770191005 0.0140181542937027 -0.1716796948790459 0.1803059522378803 -0.1803059522378808 nucleus -Cytoplasm_Texture_InfoMeas2_DAPI_3_02_256 0.1457729686413174 1.1102230246251563e-16 15.954589770191005 0.0140219044825941 -0.1711603131468355 0.1826809735226769 -0.1826809735226773 nucleus -Cytoplasm_Texture_InfoMeas2_DAPI_3_03_256 0.1371551229944171 1.1102230246251563e-16 15.954589770191005 0.0133686431749802 -0.1613949266974354 0.1815428529973036 -0.181542852997304 nucleus -Cytoplasm_Texture_InverseDifferenceMoment_DAPI_3_00_256 0.0458395052846088 1.1102230246251563e-16 15.954589770191005 -0.0093783014153752 0.1265243848324176 -0.0725987372715861 0.0725987372715866 nucleus -Cytoplasm_Texture_InverseDifferenceMoment_DAPI_3_01_256 0.0490534795984762 1.1102230246251563e-16 15.954589770191005 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1.1102230246251563e-16 15.954589770191005 0.0145887768035522 -0.2182679577734435 0.0246545872885593 -0.0246545872885603 nucleus -Nuclei_Intensity_MinIntensityEdge_DAPI 0.1513486509319462 1.1102230246251563e-16 15.954589770191005 0.013515622044036 -0.1590632508731975 0.200439269775729 -0.2004392697757293 nucleus -Nuclei_Intensity_MinIntensity_DAPI 0.1444893280517672 1.1102230246251563e-16 15.954589770191005 0.0126914179019396 -0.1452448744447049 0.2051674607726891 -0.2051674607726893 nucleus -Nuclei_Intensity_StdIntensityEdge_DAPI 0.0190020300970552 5.339061481812735e-08 7.272535078007372 0.0107260174410742 -0.1968374777766912 -0.1315359273494011 0.1315359273493998 nucleus -Nuclei_Intensity_StdIntensity_DAPI 0.0697536828001081 1.1102230246251563e-16 15.954589770191005 0.012662901353776 -0.1785674788078859 0.06620922166067 -0.0662092216606708 nucleus -Nuclei_Intensity_UpperQuartileIntensity_DAPI 0.0763346513260664 1.1102230246251563e-16 15.954589770191005 0.014152427251305 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1.643342129042935e-09 8.784272010948769 -0.0080851124262709 0.174296104142307 0.2058472146252353 -0.2058472146252339 nucleus -Nuclei_Texture_Correlation_DAPI_3_02_256 0.0345901337689555 6.439293542825908e-15 14.191161776628066 -0.0065483657008123 0.1607651219549952 0.2473845651915796 -0.2473845651915782 nucleus -Nuclei_Texture_Correlation_DAPI_3_03_256 0.0173792704187401 2.693821483656933e-07 6.569631187837804 -0.0071872469362627 0.1544528945822845 0.1809817051957463 -0.1809817051957451 nucleus -Nuclei_Texture_DifferenceEntropy_DAPI_3_00_256 0.0297990693315541 9.16489106828067e-13 12.037872692592192 0.0022280752793915 0.0020047554934785 0.149222015448915 -0.1492220154489146 nucleus -Nuclei_Texture_DifferenceEntropy_DAPI_3_01_256 0.0313777500053655 1.7985612998927538e-13 12.745074755648371 0.001084728554832 0.0244177565513393 0.169133021326257 -0.1691330213262565 nucleus -Nuclei_Texture_DifferenceEntropy_DAPI_3_02_256 0.0291295369806456 1.8252066524837574e-12 11.738687956986972 0.0014966529951172 0.0151640450263049 0.1571077590025981 -0.1571077590025977 nucleus -Nuclei_Texture_DifferenceEntropy_DAPI_3_03_256 0.0319897260670162 9.559020242022599e-14 13.019586618737348 0.0013326795370235 0.0202637614423689 0.1677232917355995 -0.167723291735599 nucleus -Nuclei_Texture_DifferenceVariance_DAPI_3_00_256 0.0059741431896692 0.0148937012816177 1.8269973609160453 0.0018221204789274 -0.0516144564346574 -0.097156493556722 0.0971564935567215 nucleus -Nuclei_Texture_DifferenceVariance_DAPI_3_01_256 0.0049444315697408 0.0368696215580743 1.433331320112449 0.0030188351104379 -0.0696585965531514 -0.0957089167730771 0.0957089167730765 nucleus -Nuclei_Texture_DifferenceVariance_DAPI_3_02_256 0.0051234222022268 0.0315576920650403 1.5008947659210634 0.0024357563391308 -0.0604686595713728 -0.0947750991242058 0.0947750991242053 nucleus -Nuclei_Texture_DifferenceVariance_DAPI_3_03_256 0.0049706051485652 0.0360424354691245 1.4431858704381224 0.0036061792602957 -0.0789796321243077 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2.0934009388037853 0.0051323655207278 -0.0814451775648545 -0.0104985443098736 0.0104985443098732 other -Cells_AreaShape_Zernike_0_0 0.0067230949163221 0.007588480562333 2.1198451739667057 0.0058648350634325 -0.0987019166219246 -0.0351829030413187 0.0351829030413181 other -Cells_AreaShape_Zernike_1_1 0.0053331824084078 0.0262683812456173 1.5805666892145658 0.0046788865353322 -0.0956040716074581 -0.0974674302797127 0.097467430279712 other -Cells_AreaShape_Zernike_2_0 0.0060616398039698 0.0137735192958202 1.8609550782271105 0.0042670045836007 -0.0915533124967364 -0.1068545437008345 0.1068545437008338 other -Cells_AreaShape_Zernike_2_2 0.0094613641126514 0.0005982793341836 3.223095998336304 0.0003402053258667 0.0181113768747492 0.0960702502920753 -0.096070250292075 other -Cells_AreaShape_Zernike_4_4 0.0103552453687428 0.0002561873426342 3.591442331109316 -0.0004610238163255 0.0338092257672358 0.1099878764813437 -0.1099878764813433 other -Cells_AreaShape_Zernike_6_4 0.0063238521783375 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9.0191396415287 0.003263544086556 -0.0229824876994416 0.1118901245377521 -0.111890124537752 other -Nuclei_AreaShape_Zernike_2_0 0.025873216959572 5.1303294945626015e-11 10.289854741501236 -0.0024196186925381 0.0045893061635391 -0.1342001943617383 0.134200194361738 other -Nuclei_AreaShape_Zernike_2_2 0.0347831157854712 5.3290705182007506e-15 14.273348532815415 -0.0032054654228831 0.0128780437809607 -0.149804813082116 0.1498048130821157 other -Nuclei_AreaShape_Zernike_3_3 0.0157900397446729 1.3009656514295642e-06 5.885734169688627 0.0014236968053767 0.0051692406192763 0.1113537519175701 -0.1113537519175699 other -Nuclei_AreaShape_Zernike_4_0 0.0213254037321541 5.176725825783991e-09 8.285944835887808 0.0019578153540195 0.0003268620199511 0.1252165254643255 -0.1252165254643252 other -Nuclei_AreaShape_Zernike_4_4 0.00791445800261 0.0025442943552164 2.5944326455978945 -0.001136495428786 -0.0012536977420967 -0.0770663335406978 0.0770663335406976 other -Nuclei_AreaShape_Zernike_5_5 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0.0206207764355481 -0.3319892943626297 -0.0617708970646947 0.0617708970646928 other -Nuclei_Neighbors_SecondClosestDistance_Adjacent 0.099410228021431 1.1102230246251563e-16 15.954589770191005 -0.0176682509921168 0.2670204041885691 -0.0188308674405852 0.0188308674405866 other -Nuclei_Neighbors_SecondClosestObjectNumber_Adjacent 0.0898125045297529 1.1102230246251563e-16 15.954589770191005 0.0189299877442896 -0.3009199271091334 -0.040867477040115 0.0408674770401134 other -Cytoplasm_Correlation_Costes_GFP_RFP 0.0166226148061752 5.709150631139082e-07 6.243428498347408 -0.001448383986872 0.0582941400834261 0.1482960479581595 -0.1482960479581589 other -Cytoplasm_Correlation_K_GFP_RFP 0.0180688583004959 1.3558242606404036e-07 6.867796599244995 0.0069266749065762 -0.1513700131877782 -0.1847791553134647 0.1847791553134635 other -Cytoplasm_Correlation_K_RFP_GFP 0.0138959726907403 8.368139717385148e-06 5.077371077283823 -0.007563196441933 0.1544662128682148 0.1572501585020312 -0.1572501585020301 other -Cytoplasm_Correlation_Manders_RFP_GFP 0.0484503849222941 1.1102230246251563e-16 15.954589770191005 0.0128280514737272 -0.1971131896075793 0.0003251681953889 -0.0003251681953899 other -Cytoplasm_Correlation_Overlap_GFP_RFP 0.0213798910399526 4.900020389442261e-09 8.309802112827846 0.0042001862452161 -0.1104968116943455 -0.1890525404802021 0.1890525404802012 other -Cytoplasm_Correlation_RWC_GFP_RFP 0.0270999588117504 1.4641954315663952e-11 10.834400952521928 0.0113375220203716 -0.2257210674327445 -0.2117292249742364 0.2117292249742348 other -Cytoplasm_Correlation_RWC_RFP_GFP 0.0382629139288177 1.1102230246251563e-16 15.954589770191005 0.0136698646391387 -0.2273715013984281 -0.0709547615938646 0.0709547615938632 other -Cells_Correlation_Correlation_GFP_RFP 0.0048085166469081 0.0414665818558966 1.3823017626177665 0.0053759444455265 -0.0962558041361731 -0.056046999362167 0.0560469993621664 other -Cells_Correlation_Costes_GFP_RFP 0.0176656942481279 2.0259232358377238e-07 6.6933770144958205 -0.0013201671095143 0.0570211990659021 0.1511689882872303 -0.1511689882872296 other -Cells_Correlation_K_GFP_RFP 0.0216990758102431 3.5511046325709117e-09 8.44963653111187 0.0070624668984834 -0.157669163703397 -0.2021145366499589 0.2021145366499576 other -Cells_Correlation_K_RFP_GFP 0.0155040938903958 1.7250395895063164e-06 5.763200933456308 -0.0075545903950773 0.1571227904179027 0.1687289900490734 -0.1687289900490722 other -Cells_Correlation_Manders_RFP_GFP 0.0541450449492438 1.1102230246251563e-16 15.954589770191005 0.012929352062409 -0.1946972376684396 0.0166783912824018 -0.0166783912824028 other -Cells_Correlation_Overlap_GFP_RFP 0.0142673165284913 5.8180671048679855e-06 5.2352212739611135 0.0045738248746546 -0.1093225917244529 -0.1605793614685596 0.1605793614685587 other -Cells_Correlation_RWC_GFP_RFP 0.0267233290459516 2.1525337068339923e-11 10.66705003914149 0.0118068048758055 -0.2303407105786117 -0.201051845636292 0.2010518456362904 other -Cells_Correlation_RWC_RFP_GFP 0.0409371503784568 1.1102230246251563e-16 15.954589770191005 0.0141617451558399 -0.2357339622005352 -0.0742528560929002 0.0742528560928988 other -Nuclei_Correlation_Correlation_GFP_RFP 0.0324497847484107 5.939693181744587e-14 13.226235988169774 0.0012480577727552 0.022152138374648 0.1701417188532685 -0.170141718853268 other -Nuclei_Correlation_K_GFP_RFP 0.0504004853489272 1.1102230246251563e-16 15.954589770191005 0.0043533599195334 -0.1336791624394929 -0.2747784981369678 0.2747784981369666 other -Nuclei_Correlation_K_RFP_GFP 0.0322456686260552 7.338574192772286e-14 13.134388310705363 -0.0056629689720058 0.1442133254364107 0.235277429755582 -0.2352774297555808 other -Nuclei_Correlation_Manders_RFP_GFP 0.0057510065205829 0.018166477203218 1.7407292818022488 0.0028870574000985 -0.0360329738232183 0.0343546948779052 -0.0343546948779053 other -Cytoplasm_Correlation_Correlation_DAPI_RFP 0.0164770949379704 6.594567361073445e-07 6.18081369122629 -0.0066279742051225 0.096677528334937 -0.021433259287394 0.0214332592873945 other -Cytoplasm_Correlation_Costes_DAPI_RFP 0.0581694205128003 1.1102230246251563e-16 15.954589770191005 -0.0129457263877704 0.2794985527143115 0.331323119929363 -0.3313231199293609 other -Cytoplasm_Correlation_Costes_RFP_DAPI 0.1980243468683329 1.1102230246251563e-16 15.954589770191005 -0.0193143417646555 0.4442044078430728 0.6063003461727692 -0.6063003461727656 other -Cytoplasm_Correlation_K_DAPI_RFP 0.0062149302622961 0.0120055742959788 1.9206170602959136 -0.0012012309935377 0.0025386693453899 -0.065552986490747 0.0655529864907469 other -Cytoplasm_Correlation_K_RFP_DAPI 0.0080780736171891 0.0021861367942233 2.6603226662117576 -0.0019245395858671 0.0566686964993492 0.1114793982957415 -0.111479398295741 other -Cytoplasm_Correlation_Manders_DAPI_RFP 0.0057631263484299 0.0179720491490146 1.7454024023916843 0.0058684697980473 -0.1045708402943611 -0.0591090978243262 0.0591090978243255 other -Cytoplasm_Correlation_Overlap_DAPI_RFP 0.0447004832776873 1.1102230246251563e-16 15.954589770191005 -0.0104068948518137 0.1485563447426093 -0.0469956232744272 0.0469956232744279 other -Cells_Correlation_Correlation_DAPI_RFP 0.0515020343225011 1.1102230246251563e-16 15.954589770191005 -0.0071793423605238 0.0788833066199859 -0.1295584105219209 0.129558410521921 other -Cells_Correlation_Costes_DAPI_RFP 0.0666268685717135 1.1102230246251563e-16 15.954589770191005 -0.0155208967755213 0.3237219422461789 0.3504128459838208 -0.3504128459838183 other -Cells_Correlation_Costes_RFP_DAPI 0.1950077784572738 1.1102230246251563e-16 15.954589770191005 -0.0189347760296095 0.4370801441522227 0.600992434586833 -0.6009924345868295 other -Cells_Correlation_K_DAPI_RFP 0.0120043749119953 5.2625663170102754e-05 4.27880241829544 -0.0031415952467806 0.0317357736562066 -0.0681463686155727 0.0681463686155727 other -Cells_Correlation_K_RFP_DAPI 0.0069883102278036 0.0059615598204791 2.224640093817362 -0.0019587218361822 0.0556174693117202 0.1049898909030043 -0.1049898909030038 other -Cells_Correlation_Manders_DAPI_RFP 0.0083424355388554 0.0017095895841842 2.767108136810939 0.004249161980246 -0.095714758301525 -0.1251117275297823 0.1251117275297815 other -Cells_Correlation_Manders_RFP_DAPI 0.0192910944690601 3.9977182120232164e-08 7.398187821342554 0.0070008625750292 -0.1010215190196223 0.0271463198430092 -0.0271463198430097 other -Cells_Correlation_Overlap_DAPI_RFP 0.0300487556106749 7.086553566182374e-13 12.149564925761196 -0.002925585927757 0.010951848466946 -0.1400249217830361 0.1400249217830358 other -Cells_Correlation_RWC_DAPI_RFP 0.0209599401776 7.48117667814796e-09 8.1260300886815 -0.0032093080671393 0.0237338072160324 -0.1053662164696798 0.1053662164696797 other -Cells_Correlation_RWC_RFP_DAPI 0.0151474151660359 2.451352801524642e-06 5.610594180086539 0.0059058427178638 -0.0833146486867555 0.0307447981671124 -0.0307447981671128 other -Nuclei_Correlation_Correlation_DAPI_RFP 0.0168432113581571 4.5875122578475924e-07 6.338422762302043 0.0001420515628276 -0.0350490459478889 -0.135272315080336 0.1352723150803355 other -Nuclei_Correlation_Costes_DAPI_RFP 0.0653722521256117 1.1102230246251563e-16 15.954589770191005 -0.0155537567470738 0.3232207860753828 0.3462710611474306 -0.3462710611474281 other -Nuclei_Correlation_Costes_RFP_DAPI 0.0179149635141995 1.580573694148768e-07 6.801185250399516 -0.0069226331361114 0.1511162345908681 0.1839903398971243 -0.1839903398971231 other -Nuclei_Correlation_K_DAPI_RFP 0.0194414548671663 3.4387600544860675e-08 7.46359812677996 -0.0011421086465465 -0.0138844420153685 -0.1294089263510944 0.129408926351094 other -Nuclei_Correlation_K_RFP_DAPI 0.0094306032780084 0.0006159131487293 3.210480524340274 -0.0025832658832332 0.0698811387485057 0.1241833980800298 -0.1241833980800292 other -Nuclei_Correlation_Overlap_DAPI_RFP 0.0333243308966041 2.398081733190338e-14 13.620136019040071 -0.0059602732081601 0.0670969512739812 -0.1009397653269125 0.1009397653269126 other -Nuclei_Correlation_RWC_DAPI_RFP 0.0094345830398724 0.0006136031128202 3.212112445869353 -0.0064997025887186 0.1059483776570577 0.0248408015139323 -0.0248408015139317 other -Nuclei_Correlation_RWC_RFP_DAPI 0.0077959130337511 0.0028392756370917 2.54679244408663 -0.0067345404406127 0.1181910975788669 0.0603729435911854 -0.0603729435911847 other -Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928 0.0342676558238695 1.4651156036990791 -0.0055078174892835 0.0990026149900095 0.0590090996317533 -0.0590090996317526 other -Cytoplasm_Correlation_Costes_DAPI_GFP 0.1266887522015375 1.1102230246251563e-16 15.954589770191005 -0.0077115767757019 0.2259752029684549 0.4421891625863014 -0.4421891625862992 other -Cytoplasm_Correlation_Costes_GFP_DAPI 0.0717113655632017 1.1102230246251563e-16 15.954589770191005 -0.0027105789835034 0.1157881765598752 0.3050791104063639 -0.3050791104063626 other -Cytoplasm_Correlation_K_DAPI_GFP 0.0087437943987151 0.0011750182388298 2.9299553921482704 -0.007234226804901 0.1290649328631211 0.0735138395974705 -0.0735138395974697 other -Cytoplasm_Correlation_K_GFP_DAPI 0.0055819277251202 0.0211011923569978 1.6756930034969135 0.0053386202296831 -0.0897989309351734 -0.0318323738340108 0.0318323738340103 other -Cytoplasm_Correlation_Manders_DAPI_GFP 0.0568267726108727 1.1102230246251563e-16 15.954589770191005 0.0105186745166461 -0.1424403356219332 0.0792410890424681 -0.0792410890424686 other -Cytoplasm_Correlation_Overlap_DAPI_GFP 0.0052213862560118 0.028970805994453 1.5380394221564682 -0.0027778261739751 0.0348727177146356 -0.0322191738170465 0.0322191738170466 other -Cytoplasm_Correlation_RWC_DAPI_GFP 0.0250899351958763 1.1402523369952178e-10 9.94299902898127 0.0102176355639956 -0.1638920903800971 -0.0280999111537201 0.0280999111537192 other -Cells_Correlation_Correlation_DAPI_GFP 0.0120121827110191 5.223024593559167e-05 4.282077929203808 -0.0082808309727361 0.1441675777139306 0.0694567357186915 -0.0694567357186906 other -Cells_Correlation_Costes_DAPI_GFP 0.1287306386618018 1.1102230246251563e-16 15.954589770191005 -0.0159824554903969 0.3647135469087196 0.489929153154612 -0.4899291531546089 other -Cells_Correlation_Costes_GFP_DAPI 0.0725736544592836 1.1102230246251563e-16 15.954589770191005 -0.0026318368579147 0.1147914535206022 0.3059586759868545 -0.3059586759868531 other -Cells_Correlation_K_DAPI_GFP 0.0136828962432701 1.0305116863840167e-05 4.986947078796643 -0.0069037054251493 0.1448310793092576 0.1593185470532011 -0.1593185470532 other -Cells_Correlation_K_GFP_DAPI 0.0233561640861427 6.641966976417281e-10 9.177703288026311 0.0106113349978678 -0.2105839322869011 -0.195371542857158 0.1953715428571565 other -Cells_Correlation_Manders_DAPI_GFP 0.0293732964052595 1.4205303600078878e-12 11.8475494799678 0.0023528815075556 -0.0006521660881691 0.1461827762937873 -0.1461827762937869 other -Cells_Correlation_Manders_GFP_DAPI 0.0288016347820732 2.556732603409273e-12 11.592314690407887 0.0054258488493823 -0.0601105034701542 0.095882513249416 -0.0958825132494161 other -Cells_Correlation_RWC_DAPI_GFP 0.0360072716847414 1.4432899320127037e-15 14.840646417884166 -0.0131163880035569 0.2607626491605052 0.2434094004456848 -0.243409400445683 other -Cells_Correlation_RWC_GFP_DAPI 0.0308150581209546 3.2163161023390785e-13 12.49264127498724 0.0046841958972369 -0.0440597428885552 0.1150220960993887 -0.1150220960993887 other -Nuclei_Correlation_Correlation_DAPI_GFP 0.0055975849365728 0.020811189718118 1.6817030916651352 -0.0027688599181534 0.0668973607803623 0.1001597794611853 -0.1001597794611848 other -Nuclei_Correlation_Costes_DAPI_GFP 0.0917366799612873 1.1102230246251563e-16 15.954589770191005 -0.0148851988489012 0.3298836164767035 0.415994629305783 -0.4159946293057804 other -Nuclei_Correlation_K_DAPI_GFP 0.016042896725708 1.0133859220662345e-06 5.994225133212328 -0.0052536283477645 0.1225024387479125 0.1718156460245471 -0.171815646024546 other -Nuclei_Correlation_K_GFP_DAPI 0.0285387179234908 3.3494318429916348e-12 11.475028855166148 0.0073553503104055 -0.1690029733733402 -0.2302331058924449 0.2302331058924435 other -Nuclei_Correlation_Manders_DAPI_GFP 0.0053512008800226 0.0258562561936261 1.5874343577291878 0.0030718253235459 -0.0405363457199373 0.0275093511301013 -0.0275093511301014 other -Nuclei_Correlation_Overlap_DAPI_GFP 0.0184591607692713 9.185241267584132e-08 7.036909431649485 -0.0044333294839917 0.0498850683396133 -0.0751730352919696 0.0751730352919697 other -Nuclei_Correlation_RWC_DAPI_GFP 0.0189891962562755 5.4080493305797006e-08 7.266959355546295 -0.0038782622974246 0.1027773274118242 0.1776477320738891 -0.1776477320738881 other -Nuclei_Correlation_RWC_GFP_DAPI 0.0233397702291401 6.753302361772738e-10 9.170483805424231 -0.003969839060068 0.1083301098895058 0.1947086060182426 -0.1947086060182416 other +feature r2_score p_value cell_count_coef plate_coef Null_coef WT_coef corrected_p_value neg_log_p compartment +Cytoplasm_Granularity_11_RFP 0.0083140810700031 0.0017553507473393 0.0020349194384395 -0.0152096139187058 0.0661476342826508 -0.0661476342826508 0.00285829203038 2.543893401611021 actin +Cytoplasm_Granularity_12_RFP 0.0170346819190725 3.793612946001446e-07 -0.0086265789374558 0.1743870117677087 0.1719627433359708 -0.1719627433359696 8.636048294720938e-07 6.06368493765877 actin +Cytoplasm_Granularity_13_RFP 0.0165343642920898 6.230874355361848e-07 -0.0013168894588963 0.0558714466217581 0.1466440481927371 -0.1466440481927365 1.3992350341537925e-06 5.854109329494796 actin +Cytoplasm_Granularity_14_RFP 0.0098501891702583 0.0004140812515598 0.0048833782463788 -0.0696969351968968 0.0221031201207769 -0.0221031201207772 0.0007182589532842 3.143718951708868 actin +Cytoplasm_Granularity_15_RFP 0.034125533803847 1.0436096431476471e-14 -0.0117844982540997 0.2411427339495568 0.2469237178768606 -0.2469237178768588 3.6060440348048167e-14 13.442968974242577 actin +Cytoplasm_Granularity_16_RFP 0.0053252103386409 0.0264527395149072 -0.0011101117449085 0.0381216351097182 0.0866696542894571 -0.0866696542894567 0.0365827648580458 1.4367234744414588 actin +Cytoplasm_Granularity_1_RFP 0.0797662447888054 1.1102230246251563e-16 0.0128883932375536 -0.2919905507118198 -0.3863669662163332 0.3863669662163309 4.0280279112181464e-16 15.394907528772336 actin +Cytoplasm_Granularity_2_RFP 0.0265761238729764 2.5021873462094387e-11 0.0055619175677227 -0.0646411039510882 0.0858438944240999 -0.0858438944241001 7.190691853834551e-11 10.143229321877568 actin +Cytoplasm_Granularity_3_RFP 0.0247453188850123 1.6195356167258979e-10 0.0058182685634424 -0.0713392817216137 0.0744939196170529 -0.0744939196170531 4.434624648629168e-10 9.35314313342588 actin +Cytoplasm_Granularity_4_RFP 0.0237508762052655 4.450164681202296e-10 0.0064129908206857 -0.0842132596546174 0.0591334779903649 -0.0591334779903652 1.1795984463186907e-09 8.928265808140978 actin +Cytoplasm_Granularity_5_RFP 0.0152669315254935 2.179214255049189e-06 0.0056152854233033 -0.0771713489077849 0.0376466898321505 -0.0376466898321508 4.676650185050108e-06 5.330065114244632 actin +Cytoplasm_Granularity_6_RFP 0.0135583503755253 1.163737040010826e-05 0.0050243723061764 -0.0672611482336932 0.041049393082168 -0.0410493930821682 2.378694900444664e-05 4.623661258488416 actin +Cytoplasm_Granularity_7_RFP 0.0068161411470623 0.006973486447476 0.0014019186773225 -0.0053777353069579 0.0665650786828842 -0.066565078682884 0.0106529181125259 1.9725314113261163 actin +Cytoplasm_Granularity_8_RFP 0.0091773954485366 0.0007819895285646 0.001378595879782 -0.00155702630656 0.0808152695319881 -0.0808152695319879 0.0013138782093539 2.881444890070697 actin +Cytoplasm_Granularity_9_RFP 0.0118764346599471 5.95448596415471e-05 0.0020014519906748 -0.009960290523731 0.0856360697971641 -0.085636069797164 0.0001120447034746 3.950608668411528 actin +Cytoplasm_Intensity_IntegratedIntensityEdge_RFP 0.0410414795005028 1.1102230246251563e-16 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-0.3462710611474281 4.0280279112181464e-16 15.394907528772336 other +Nuclei_Correlation_Costes_RFP_DAPI 0.0179149635141995 1.580573694148768e-07 -0.0069226331361114 0.1511162345908681 0.1839903398971243 -0.1839903398971231 3.677447011837113e-07 6.434453576284332 other +Nuclei_Correlation_K_DAPI_RFP 0.0194414548671663 3.4387600544860675e-08 -0.0011421086465465 -0.0138844420153685 -0.1294089263510944 0.129408926351094 8.352302140707793e-08 7.078193803653505 other +Nuclei_Correlation_K_RFP_DAPI 0.0094306032780084 0.0006159131487293 -0.0025832658832332 0.0698811387485057 0.1241833980800298 -0.1241833980800292 0.0010423836234325 2.981972420308452 other +Nuclei_Correlation_Overlap_DAPI_RFP 0.0333243308966041 2.398081733190338e-14 -0.0059602732081601 0.0670969512739812 -0.1009397653269125 0.1009397653269126 8.140856410040885e-14 13.089329905357634 other +Nuclei_Correlation_RWC_DAPI_RFP 0.0094345830398724 0.0006136031128202 -0.0064997025887186 0.1059483776570577 0.0248408015139323 -0.0248408015139317 0.0010399900934076 2.9829707976232047 other +Nuclei_Correlation_RWC_RFP_DAPI 0.0077959130337511 0.0028392756370917 -0.0067345404406127 0.1181910975788669 0.0603729435911854 -0.0603729435911847 0.0045156150885801 2.345283084468512 other +Cytoplasm_Correlation_Correlation_DAPI_GFP 0.0050287674705928 0.0342676558238695 -0.0055078174892835 0.0990026149900095 0.0590090996317533 -0.0590090996317526 0.0463691706427885 1.3337706718092108 other +Cytoplasm_Correlation_Costes_DAPI_GFP 0.1266887522015375 1.1102230246251563e-16 -0.0077115767757019 0.2259752029684549 0.4421891625863014 -0.4421891625862992 4.0280279112181464e-16 15.394907528772336 other +Cytoplasm_Correlation_Costes_GFP_DAPI 0.0717113655632017 1.1102230246251563e-16 -0.0027105789835034 0.1157881765598752 0.3050791104063639 -0.3050791104063626 4.0280279112181464e-16 15.394907528772336 other +Cytoplasm_Correlation_K_DAPI_GFP 0.0087437943987151 0.0011750182388298 -0.007234226804901 0.1290649328631211 0.0735138395974705 -0.0735138395974697 0.0019488516789734 2.7102212124239538 other +Cytoplasm_Correlation_K_GFP_DAPI 0.0055819277251202 0.0211011923569978 0.0053386202296831 -0.0897989309351734 -0.0318323738340108 0.0318323738340103 0.0296591819933104 1.527840831078722 other +Cytoplasm_Correlation_Manders_DAPI_GFP 0.0568267726108727 1.1102230246251563e-16 0.0105186745166461 -0.1424403356219332 0.0792410890424681 -0.0792410890424686 4.0280279112181464e-16 15.394907528772336 other +Cytoplasm_Correlation_Overlap_DAPI_GFP 0.0052213862560118 0.028970805994453 -0.0027778261739751 0.0348727177146356 -0.0322191738170465 0.0322191738170466 0.0398992950884459 1.3990347770426366 other +Cytoplasm_Correlation_RWC_DAPI_GFP 0.0250899351958763 1.1402523369952178e-10 0.0102176355639956 -0.1638920903800971 -0.0280999111537201 0.0280999111537192 3.15198324583678e-10 9.501416099640595 other +Cells_Correlation_Correlation_DAPI_GFP 0.0120121827110191 5.223024593559167e-05 -0.0082808309727361 0.1441675777139306 0.0694567357186915 -0.0694567357186906 9.951748929043382e-05 4.002100589255595 other +Cells_Correlation_Costes_DAPI_GFP 0.1287306386618018 1.1102230246251563e-16 -0.0159824554903969 0.3647135469087196 0.489929153154612 -0.4899291531546089 4.0280279112181464e-16 15.394907528772336 other +Cells_Correlation_Costes_GFP_DAPI 0.0725736544592836 1.1102230246251563e-16 -0.0026318368579147 0.1147914535206022 0.3059586759868545 -0.3059586759868531 4.0280279112181464e-16 15.394907528772336 other +Cells_Correlation_K_DAPI_GFP 0.0136828962432701 1.0305116863840167e-05 -0.0069037054251493 0.1448310793092576 0.1593185470532011 -0.1593185470532 2.113823441505024e-05 4.67493129024634 other +Cells_Correlation_K_GFP_DAPI 0.0233561640861427 6.641966976417281e-10 0.0106113349978678 -0.2105839322869011 -0.195371542857158 0.1953715428571565 1.7525735590046503e-09 8.756323744773926 other +Cells_Correlation_Manders_DAPI_GFP 0.0293732964052595 1.4205303600078878e-12 0.0023528815075556 -0.0006521660881691 0.1461827762937873 -0.1461827762937869 4.457393913430156e-12 11.35091898429622 other +Cells_Correlation_Manders_GFP_DAPI 0.0288016347820732 2.556732603409273e-12 0.0054258488493823 -0.0601105034701542 0.095882513249416 -0.0958825132494161 7.894591895101505e-12 11.102670315596974 other +Cells_Correlation_RWC_DAPI_GFP 0.0360072716847414 1.4432899320127037e-15 -0.0131163880035569 0.2607626491605052 0.2434094004456848 -0.243409400445683 5.124341318246938e-15 14.29036195080588 other +Cells_Correlation_RWC_GFP_DAPI 0.0308150581209546 3.2163161023390785e-13 0.0046841958972369 -0.0440597428885552 0.1150220960993887 -0.1150220960993887 1.0430567024624778e-12 11.98169208189254 other +Nuclei_Correlation_Correlation_DAPI_GFP 0.0055975849365728 0.020811189718118 -0.0027688599181534 0.0668973607803623 0.1001597794611853 -0.1001597794611848 0.0293130133835243 1.5329395337087124 other +Nuclei_Correlation_Costes_DAPI_GFP 0.0917366799612873 1.1102230246251563e-16 -0.0148851988489012 0.3298836164767035 0.415994629305783 -0.4159946293057804 4.0280279112181464e-16 15.394907528772336 other +Nuclei_Correlation_K_DAPI_GFP 0.016042896725708 1.0133859220662345e-06 -0.0052536283477645 0.1225024387479125 0.1718156460245471 -0.171815646024546 2.2157082024838008e-06 5.654487434555223 other +Nuclei_Correlation_K_GFP_DAPI 0.0285387179234908 3.3494318429916348e-12 0.0073553503104055 -0.1690029733733402 -0.2302331058924449 0.2302331058924435 1.0314828566878748e-11 10.986537985633367 other +Nuclei_Correlation_Manders_DAPI_GFP 0.0053512008800226 0.0258562561936261 0.0030718253235459 -0.0405363457199373 0.0275093511301013 -0.0275093511301014 0.0359080304315789 1.4448084154296303 other +Nuclei_Correlation_Overlap_DAPI_GFP 0.0184591607692713 9.185241267584132e-08 -0.0044333294839917 0.0498850683396133 -0.0751730352919696 0.0751730352919697 2.176339818707179e-07 6.6622732919394245 other +Nuclei_Correlation_RWC_DAPI_GFP 0.0189891962562755 5.4080493305797006e-08 -0.0038782622974246 0.1027773274118242 0.1776477320738891 -0.1776477320738881 1.291922895638484e-07 6.888763405070014 other +Nuclei_Correlation_RWC_GFP_DAPI 0.0233397702291401 6.753302361772738e-10 -0.003969839060068 0.1083301098895058 0.1947086060182426 -0.1947086060182416 1.777910213609558e-09 8.750090175153497 other diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py index 35d5b00..7490d61 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py @@ -16,27 +16,11 @@ import pandas as pd -import numpy as np from pathlib import Path -from sklearn.preprocessing import LabelEncoder -from sklearn.linear_model import LogisticRegression - -from sklearn.metrics import ( - confusion_matrix, - ConfusionMatrixDisplay, - precision_score, - accuracy_score, -) -from sklearn.model_selection import train_test_split - -import matplotlib.pyplot as plt -import seaborn as sns import itertools -from joblib import dump, load - -# ## Find the git root Directory +# ## Finding the git root directory to reference paths on any system # In[ ]: @@ -70,6 +54,9 @@ parents=True, exist_ok=True ) # Create the parent directories if they don't exist +sig_output_path = output_path / "significant_feature_compartments.tsv" +output_path = output_path / "feature_compartments.tsv" + # ## Import the model data as a dataframe @@ -85,6 +72,8 @@ # ## Seperate cell data by channel +# +# We are analyzing the data according to compartment. To accomplish this we must group the data by compartment, where the channels of the image represent the compartments. However, in some cases, morphology features may use multiple channels, or no specific compartment channels. These features are considered apart of the "other" category in our data. # In[ ]: @@ -104,7 +93,7 @@ # In[ ]: -# Create a list of all possible comparment pairs +# Create a list of all possible compartment pairs pairs = list(itertools.combinations(compartment_data.keys(), 2)) # Use a list of possible compartments for find compartment agnostic features @@ -158,9 +147,9 @@ # In[ ]: -concatenated_df.to_csv(output_path / "feature_compartments.tsv", sep="\t", index=False) +concatenated_df.to_csv(output_path, sep="\t", index=False) # Create a dataframe with only significant models -concatenated_df.loc[concatenated_df["p_value"] <= 0.05].to_csv( - output_path / "significant_feature_compartments.tsv", sep="\t", index=False +concatenated_df.loc[concatenated_df["corrected_p_value"] <= 0.05].to_csv( + sig_output_path, sep="\t", index=False ) diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb index 64a7f04..6dd9d5c 100644 --- a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb +++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb @@ -1,296 +1,365 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "vk3QsHOEjG" - }, - "source": [ - "# Here the features are seperated according to compartments for later analysis" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "SyZ3qa8iz3" - }, - "source": [ - "# %%--%%| \n", - "r\"\"\"\u00b0\u00b0\u00b0\n", - "## Imports\n", - "\u00b0\u00b0\u00b0\"\"\"" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "\n" - }, - { - "output_type": "execute_result", - "execution_count": 1, - "data": { - "text/plain": "'\u00b0\u00b0\u00b0\\n## Imports\\n\u00b0\u00b0\u00b0'" - }, - "metadata": {} - } - ], - "execution_count": 1 - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "FKd1dIo45E" - }, - "source": [ - "import sys\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pathlib import Path\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "from sklearn.metrics import (\n", - " confusion_matrix,\n", - " ConfusionMatrixDisplay,\n", - " precision_score,\n", - " accuracy_score,\n", - ")\n", - "from sklearn.model_selection import train_test_split\n", - "from collections import Counter\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import itertools\n", - "\n", - "from joblib import dump, load" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "ItCxTfECNg" - }, - "source": [ - "## Find the git root Directory" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "zjh0SKYFgl" - }, - "source": [ - "# Get the current working directory\n", - "cwd = Path.cwd()\n", - "\n", - "if (cwd / \".git\").is_dir():\n", - " root_dir = cwd\n", - "\n", - "else:\n", - " root_dir = None\n", - " for parent in cwd.parents:\n", - " if (parent / \".git\").is_dir():\n", - " root_dir = parent\n", - " break\n", - "\n", - "# Check if a Git root directory was found\n", - "if root_dir is None:\n", - " raise FileNotFoundError(\"No Git root directory found.\")" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "OTkUmqTBVt" - }, - "source": [ - "## Create the output path if it doesn't exist" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "W8zS6OpdNq" - }, - "source": [ - "output_path = Path(\"data\")\n", - "\n", - "output_path.mkdir(\n", - " parents=True, exist_ok=True\n", - ") # Create the parent directories if they don't exist" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "KChOdgSroB" - }, - "source": [ - "## Import the model data as a dataframe" - ] - }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "aYwhtTNAgx" - }, - "source": [ - "feature_properties = pd.read_csv(\n", - " root_dir\n", - " / \"1.train_models/linear_reg_plate_1_2_norm_data/data\"\n", - " / \"model_properties.tsv\",\n", - " sep=\"\\t\",\n", - ")" - ], - "outputs": [], - "execution_count": null + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "vk3QsHOEjG" + }, + "source": [ + "# Here the features are seperated according to compartments for later analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:15.851942Z", + "iopub.status.busy": "2023-06-16T19:52:15.851826Z", + "iopub.status.idle": "2023-06-16T19:52:15.859144Z", + "shell.execute_reply": "2023-06-16T19:52:15.858811Z" }, + "jukit_cell_id": "SyZ3qa8iz3" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "QmPiNYcAdw" - }, - "source": [ - "## Seperate cell data by channel" + "data": { + "text/plain": [ + "'°°°\\n## Imports\\n°°°'" ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# %%--%%| \n", + "r\"\"\"°°°\n", + "## Imports\n", + "°°°\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:15.861242Z", + "iopub.status.busy": "2023-06-16T19:52:15.860922Z", + "iopub.status.idle": "2023-06-16T19:52:16.033021Z", + "shell.execute_reply": "2023-06-16T19:52:16.032696Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "wSEsy3OWnz" - }, - "source": [ - "# A map for comparments to channel\n", - "compartment2channel = {\"actin\": \"RFP\", \"er\": \"GFP\", \"nucleus\": \"DAPI\"}\n", - "\n", - "# Create a Dictionary to hold the comparment data as dataframes\n", - "compartment_data = {\n", - " compartment: feature_properties[feature_properties[\"feature\"].str.contains(channel)]\n", - " for compartment, channel in compartment2channel.items()\n", - "}" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "FKd1dIo45E" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "import itertools" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "ItCxTfECNg" + }, + "source": [ + "## Finding the git root directory to reference paths on any system" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.034583Z", + "iopub.status.busy": "2023-06-16T19:52:16.034291Z", + "iopub.status.idle": "2023-06-16T19:52:16.036833Z", + "shell.execute_reply": "2023-06-16T19:52:16.036578Z" }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "hAmkVoVyDd" - }, - "source": [ - "## Find the features that use more than one compartment" - ] + "jukit_cell_id": "zjh0SKYFgl" + }, + "outputs": [], + "source": [ + "# Get the current working directory\n", + "cwd = Path.cwd()\n", + "\n", + "if (cwd / \".git\").is_dir():\n", + " root_dir = cwd\n", + "\n", + "else:\n", + " root_dir = None\n", + " for parent in cwd.parents:\n", + " if (parent / \".git\").is_dir():\n", + " root_dir = parent\n", + " break\n", + "\n", + "# Check if a Git root directory was found\n", + "if root_dir is None:\n", + " raise FileNotFoundError(\"No Git root directory found.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "OTkUmqTBVt" + }, + "source": [ + "## Create the output path if it doesn't exist" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.038007Z", + "iopub.status.busy": "2023-06-16T19:52:16.037752Z", + "iopub.status.idle": "2023-06-16T19:52:16.039716Z", + "shell.execute_reply": "2023-06-16T19:52:16.039468Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "CQscjFvYen" - }, - "source": [ - "# Create a list of all possible comparment pairs\n", - "pairs = list(itertools.combinations(compartment_data.keys(), 2))\n", - "\n", - "# Use a list of possible compartments for find compartment agnostic features\n", - "pos_compartments = list(compartment2channel.values())\n", - "\n", - "# Get the features that do not belong to any compartment specifically\n", - "other_compartment = feature_properties[\n", - " ~feature_properties[\"feature\"].str.contains(\"|\".join(pos_compartments))\n", - "]\n", - "\n", - "# Find intersected rows between each pair of compartments\n", - "for pair in pairs:\n", - " # Create placeholder dataframes for each comparment in the pair\n", - " df1 = compartment_data[pair[0]]\n", - " df2 = compartment_data[pair[1]]\n", - "\n", - " # Get duplicate features between the two compartment dataframes\n", - " intersection = pd.concat([df1, df2], axis=0)\n", - " intersection = intersection[intersection.duplicated(subset=\"feature\", keep=False)]\n", - "\n", - " # Remove any duplicate features already added to the other compartment dataframe\n", - " other_compartment = pd.concat([other_compartment, intersection], axis=0)\n", - " other_compartment = other_compartment.drop_duplicates(subset=\"feature\")" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "W8zS6OpdNq" + }, + "outputs": [], + "source": [ + "output_path = Path(\"data\")\n", + "\n", + "output_path.mkdir(\n", + " parents=True, exist_ok=True\n", + ") # Create the parent directories if they don't exist\n", + "\n", + "sig_output_path = output_path / \"significant_feature_compartments.tsv\"\n", + "output_path = output_path / \"feature_compartments.tsv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "KChOdgSroB" + }, + "source": [ + "## Import the model data as a dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.040837Z", + "iopub.status.busy": "2023-06-16T19:52:16.040599Z", + "iopub.status.idle": "2023-06-16T19:52:16.045245Z", + "shell.execute_reply": "2023-06-16T19:52:16.045011Z" }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "UwxezzhMqY" - }, - "source": [ - "## Organize the data according to compartment in one dataframe" - ] + "jukit_cell_id": "aYwhtTNAgx" + }, + "outputs": [], + "source": [ + "feature_properties = pd.read_csv(\n", + " root_dir\n", + " / \"1.train_models/linear_reg_plate_1_2_norm_data/data\"\n", + " / \"model_properties.tsv\",\n", + " sep=\"\\t\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "QmPiNYcAdw" + }, + "source": [ + "## Seperate cell data by channel\n", + "\n", + "We are analyzing the data according to compartment. To accomplish this we must group the data by compartment, where the channels of the image represent the compartments. However, in some cases, morphology features may use multiple channels, or no specific compartment channels. These features are considered apart of the \"other\" category in our data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.046464Z", + "iopub.status.busy": "2023-06-16T19:52:16.046245Z", + "iopub.status.idle": "2023-06-16T19:52:16.049887Z", + "shell.execute_reply": "2023-06-16T19:52:16.049630Z" }, - { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "iTJOXFvhuT" - }, - "source": [ - "# Remove features from each compartment dataframe if they are duplicates in other dataframes, or if the features only exist in the other_compartment dataframe\n", - "compartment_data = {\n", - " compartment: compartment_data[compartment][\n", - " ~compartment_data[compartment][\"feature\"].isin(other_compartment[\"feature\"])\n", - " ]\n", - " for compartment, channel in compartment2channel.items()\n", - "}\n", - "\n", - "compartment_data[\"other\"] = other_compartment\n", - "\n", - "# Create a compartment column for each comparment dataframe\n", - "for compartment, df in compartment_data.items():\n", - " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n", - "\n", - "# Concatenate the rows of DataFrames to create the plot below\n", - "concatenated_df = pd.concat(compartment_data.values(), ignore_index=True)" - ], - "outputs": [], - "execution_count": null + "jukit_cell_id": "wSEsy3OWnz" + }, + "outputs": [], + "source": [ + "# A map for comparments to channel\n", + "compartment2channel = {\"actin\": \"RFP\", \"er\": \"GFP\", \"nucleus\": \"DAPI\"}\n", + "\n", + "# Create a Dictionary to hold the comparment data as dataframes\n", + "compartment_data = {\n", + " compartment: feature_properties[feature_properties[\"feature\"].str.contains(channel)]\n", + " for compartment, channel in compartment2channel.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "hAmkVoVyDd" + }, + "source": [ + "## Find the features that use more than one compartment" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.051080Z", + "iopub.status.busy": "2023-06-16T19:52:16.050795Z", + "iopub.status.idle": "2023-06-16T19:52:16.056513Z", + "shell.execute_reply": "2023-06-16T19:52:16.056275Z" }, - { - "cell_type": "markdown", - "metadata": { - "jukit_cell_id": "NtZG4xLsob" - }, - "source": [ - "## Save the dataframes" - ] + "jukit_cell_id": "CQscjFvYen" + }, + "outputs": [], + "source": [ + "# Create a list of all possible compartment pairs\n", + "pairs = list(itertools.combinations(compartment_data.keys(), 2))\n", + "\n", + "# Use a list of possible compartments for find compartment agnostic features\n", + "pos_compartments = list(compartment2channel.values())\n", + "\n", + "# Get the features that do not belong to any compartment specifically\n", + "other_compartment = feature_properties[\n", + " ~feature_properties[\"feature\"].str.contains(\"|\".join(pos_compartments))\n", + "]\n", + "\n", + "# Find intersected rows between each pair of compartments\n", + "for pair in pairs:\n", + " # Create placeholder dataframes for each comparment in the pair\n", + " df1 = compartment_data[pair[0]]\n", + " df2 = compartment_data[pair[1]]\n", + "\n", + " # Get duplicate features between the two compartment dataframes\n", + " intersection = pd.concat([df1, df2], axis=0)\n", + " intersection = intersection[intersection.duplicated(subset=\"feature\", keep=False)]\n", + "\n", + " # Remove any duplicate features already added to the other compartment dataframe\n", + " other_compartment = pd.concat([other_compartment, intersection], axis=0)\n", + " other_compartment = other_compartment.drop_duplicates(subset=\"feature\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "UwxezzhMqY" + }, + "source": [ + "## Organize the data according to compartment in one dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.057760Z", + "iopub.status.busy": "2023-06-16T19:52:16.057537Z", + "iopub.status.idle": "2023-06-16T19:52:16.061890Z", + "shell.execute_reply": "2023-06-16T19:52:16.061637Z" }, + "jukit_cell_id": "iTJOXFvhuT" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "jukit_cell_id": "NUwlraBlOz" - }, - "source": [ - "concatenated_df.to_csv(output_path / \"feature_compartments.tsv\", sep=\"\\t\", index=False)\n", - "\n", - "# Create a dataframe with only significant models\n", - "concatenated_df.loc[concatenated_df[\"p_value\"] <= 0.05].to_csv(\n", - " output_path / \"significant_feature_compartments.tsv\", sep=\"\\t\", index=False\n", - ")" - ], - "outputs": [], - "execution_count": null - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "python", - "language": "python", - "name": "python3" + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_21674/1993350327.py:13: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n", + "/tmp/ipykernel_21674/1993350327.py:13: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n" + ] } + ], + "source": [ + "# Remove features from each compartment dataframe if they are duplicates in other dataframes, or if the features only exist in the other_compartment dataframe\n", + "compartment_data = {\n", + " compartment: compartment_data[compartment][\n", + " ~compartment_data[compartment][\"feature\"].isin(other_compartment[\"feature\"])\n", + " ]\n", + " for compartment, channel in compartment2channel.items()\n", + "}\n", + "\n", + "compartment_data[\"other\"] = other_compartment\n", + "\n", + "# Create a compartment column for each comparment dataframe\n", + "for compartment, df in compartment_data.items():\n", + " compartment_data[compartment][\"compartment\"] = len(df) * [compartment]\n", + "\n", + "# Concatenate the rows of DataFrames to create the plot below\n", + "concatenated_df = pd.concat(compartment_data.values(), ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jukit_cell_id": "NtZG4xLsob" + }, + "source": [ + "## Save the dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-16T19:52:16.063083Z", + "iopub.status.busy": "2023-06-16T19:52:16.062826Z", + "iopub.status.idle": "2023-06-16T19:52:16.077540Z", + "shell.execute_reply": "2023-06-16T19:52:16.077293Z" + }, + "jukit_cell_id": "NUwlraBlOz" + }, + "outputs": [], + "source": [ + "concatenated_df.to_csv(output_path, sep=\"\\t\", index=False)\n", + "\n", + "# Create a dataframe with only significant models\n", + "concatenated_df.loc[concatenated_df[\"corrected_p_value\"] <= 0.05].to_csv(\n", + " sig_output_path, sep=\"\\t\", index=False\n", + ")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "python", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": 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