diff --git a/1.train_models/linear_reg_plate_1_2_norm_data/data/.~lock.model_properties.tsv# b/1.train_models/linear_reg_plate_1_2_norm_data/data/.~lock.model_properties.tsv#
new file mode 100644
index 0000000..daea3d6
--- /dev/null
+++ b/1.train_models/linear_reg_plate_1_2_norm_data/data/.~lock.model_properties.tsv#
@@ -0,0 +1 @@
+,camo,oak,16.06.2023 16:06,file:///home/camo/.config/libreoffice/4;
\ No newline at end of file
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..4ee4103
--- /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	corrected_p_value	critical_threshold	neg_log_p
+Cytoplasm_AreaShape_Area	0.18474653920991335	1.1102230246251565e-16	-0.014366895583179384	0.16455192415455927	-0.23170835918151744	0.23170835918151778	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_BoundingBoxArea	0.17189193243211054	1.1102230246251565e-16	-0.016323544414939307	0.20781946368794818	-0.17741893610840856	0.1774189361084092	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_BoundingBoxMaximum_X	0.0006686522828195374	0.8477981783245556	-0.00020982393246391827	-0.002611737414587152	-0.02402534842193505	0.024025348421934985	0.9059896101777439	0.04960096249006299	0.04287678275182567
+Cytoplasm_AreaShape_BoundingBoxMaximum_Y	0.004623520471195297	0.04861314077390888	-0.0009847817393303932	0.0012232505317791575	-0.05727241082936399	0.05727241082936387	0.06391829721235358	0.04960096249006299	1.1943748031735537
+Cytoplasm_AreaShape_BoundingBoxMinimum_X	0.006643417228757387	0.008157038547171647	0.0038512013186171377	-0.0539343420742183	0.02167513974965026	-0.021675139749650487	0.012315112813090093	0.04960096249006299	1.909561605434451
+Cytoplasm_AreaShape_BoundingBoxMinimum_Y	0.0065068094079506755	0.009230214384759461	0.00481213451282194	-0.07467209079560387	-0.0028819431758143124	0.0028819431758139308	0.013863232730537819	0.04960096249006299	1.858135485934254
+Cytoplasm_AreaShape_Center_X	0.0008253840774573584	0.790237195752141	0.0017318404680637819	-0.026991759598672196	-0.00152286257861017	0.0015228625786100278	0.872945180083954	0.04960096249006299	0.05901302861353463
+Cytoplasm_AreaShape_Center_Y	0.0006476256396724667	0.8552666776527195	0.0018503618710664477	-0.03598224063554693	-0.03102832323811212	0.031028323238111867	0.9059896101777439	0.04960096249006299	0.04287678275182567
+Cytoplasm_AreaShape_Compactness	0.0037210957930179367	0.1037319371043417	0.004257913124454014	-0.08431896166431146	-0.07765357386835678	0.0776535738683562	0.13104763762583319	0.04960096249006299	0.8825708036018693
+Cytoplasm_AreaShape_ConvexArea	0.17982532454241573	1.1102230246251565e-16	-0.015633316704335737	0.19129038444518412	-0.20178093241798328	0.2017809324179838	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_Eccentricity	0.004410350060662371	0.05830630055513919	-0.004982139383815284	0.0952028627189675	0.07662923769481841	-0.07662923769481776	0.0761457985877577	0.04960096249006299	1.1183540542516477
+Cytoplasm_AreaShape_EquivalentDiameter	0.1494028496298394	1.1102230246251565e-16	-0.013932887661676883	0.16805131059321238	-0.1898453524123195	0.18984535241231992	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_EulerNumber	0.001101561345625779	0.6857490404406357	0.0024104555849951514	-0.044024588888653085	-0.028692996185100357	0.028692996185100066	0.7722159417571077	0.04960096249006299	0.11226123697034945
+Cytoplasm_AreaShape_Extent	0.010328495037530416	0.00026280138242185114	-0.0020499702462758434	0.012781197306802189	-0.07709512873755808	0.07709512873755797	0.00046724717456626217	0.04960096249006299	3.3304533161106957
+Cytoplasm_AreaShape_FormFactor	0.0051660518090178575	0.03040574483600289	-0.005589818784788794	0.10276991017516758	0.06932684755362556	-0.06932684755362487	0.04172703280685503	0.04960096249006299	1.3795824967027581
+Cytoplasm_AreaShape_MajorAxisLength	0.12473749755865948	1.1102230246251565e-16	-0.01623183353864438	0.22430666790882142	-0.10375595998225381	0.10375595998225472	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_MaxFeretDiameter	0.13582702830372229	1.1102230246251565e-16	-0.015693436821142704	0.208634455148764	-0.13419750390414664	0.13419750390414742	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_MaximumRadius	0.1168327128685529	1.1102230246251565e-16	-0.013228504768089028	0.16673326314309245	-0.15070398435580012	0.15070398435580062	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_MeanRadius	0.11640369081620916	1.1102230246251565e-16	-0.013266275613819548	0.1676720889026541	-0.14922959482847814	0.14922959482847867	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_MedianRadius	0.11775107386397887	1.1102230246251565e-16	-0.013351006949437685	0.16880369669042444	-0.1499329201906602	0.14993292019066073	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_MinFeretDiameter	0.14570799655408517	1.1102230246251565e-16	-0.012903500759889842	0.14898974125779602	-0.20317230066823588	0.2031723006682362	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_MinorAxisLength	0.14568815204182783	1.1102230246251565e-16	-0.01171497300810986	0.1256130097232725	-0.2241913595616944	0.2241913595616945	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_Orientation	0.00011232417337903033	0.993789681225094	-0.0008252930461758963	0.015078370132820918	0.00984536023256886	-0.009845360232568757	0.9999998395215051	0.04960096249006299	6.969493040761916e-08
+Cytoplasm_AreaShape_Perimeter	0.13717574847089586	1.1102230246251565e-16	-0.010833583190902659	0.111447221849869	-0.2267313332606949	0.22673133326069494	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_AreaShape_Solidity	0.01637981962202517	7.261393928370552e-07	-0.0044779741052228105	0.05295541845348736	-0.0653603140505073	0.06536031405050742	1.6119461473878032e-06	0.04960096249006299	5.792649471391341
+Cytoplasm_AreaShape_Zernike_0_0	0.011439088937936548	9.074874710390812e-05	-0.0004049954243073187	-0.01934675273346631	-0.10525426512650221	0.10525426512650188	0.00016750285435236458	0.04960096249006299	3.7759777879171703
+Cytoplasm_AreaShape_Zernike_1_1	0.005596581406647094	0.020829660673047035	-0.00030829971541755294	-0.013072592528001317	-0.07331222170092558	0.07331222170092534	0.029313013383524374	0.04960096249006299	1.5329395337087124
+Cytoplasm_AreaShape_Zernike_2_0	0.027911489803901013	6.3754557189099614e-12	-0.0037495390128448793	0.02827167255498375	-0.1208690817252653	0.12086908172526517	1.9326120338523406e-11	0.04960096249006299	10.71385532055399
+Cytoplasm_AreaShape_Zernike_2_2	0.001321453572769915	0.6045612850898732	-0.002818920183897007	0.052186560187874535	0.036443620720813766	-0.03644362072081342	0.689484923368706	0.04960096249006299	0.16147522589039945
+Cytoplasm_AreaShape_Zernike_3_1	5.495999858551315e-07	0.9999998395215051	-4.9533755058258953e-05	0.0009974538229946088	0.0009714589025408957	-0.0009714589025408884	0.9999998395215051	0.04960096249006299	6.969493040761916e-08
+Cytoplasm_AreaShape_Zernike_3_3	0.00031945520938991834	0.9563082435525218	-0.0013852221551404306	0.02567059924298695	0.01801561614894007	-0.0180156161489399	0.9799416335079238	0.04960096249006299	0.00879979063434963
+Cytoplasm_AreaShape_Zernike_4_0	0.0027762270130549105	0.22003256070437405	0.0040300380514377235	-0.07091489295095138	-0.03690084874195006	0.036900848741949606	0.26665741438181445	0.04960096249006299	0.5740463363000916
+Cytoplasm_AreaShape_Zernike_4_2	0.004051373007551873	0.07889856864035405	-0.004465972918278303	0.07442874211648193	0.023782013299101415	-0.02378201329910099	0.10088242091569499	0.04960096249006299	0.9961845043720082
+Cytoplasm_AreaShape_Zernike_4_4	0.003137681995709074	0.16605046030928194	-0.0030562606489673567	0.06566326453041352	0.07689643374231767	-0.07689643374231717	0.2048720344517283	0.04960096249006299	0.6885173198427823
+Cytoplasm_AreaShape_Zernike_5_1	0.0010675082288147397	0.6985940560612804	0.0024913675211216168	-0.047039312956034855	-0.03598212694359602	0.03598212694359569	0.7843981615929851	0.04960096249006299	0.10546343286625977
+Cytoplasm_AreaShape_Zernike_5_3	0.0011295617537078195	0.6752267302181656	-0.00209974631145622	0.043262931116291176	0.04521666277577188	-0.04521666277577155	0.7618447364269099	0.04960096249006299	0.11813352863898009
+Cytoplasm_AreaShape_Zernike_5_5	0.0005966185987893446	0.8730362010730379	0.0018503474668604045	-0.03511948725382365	-0.027478189453467274	0.02747818945346703	0.9172805696342055	0.04960096249006299	0.037497805871035926
+Cytoplasm_AreaShape_Zernike_6_0	0.0022850810579421177	0.3175609369441468	0.0033749300870345154	-0.06651657454357736	-0.060246362267949514	0.06024636226794903	0.3773677050073228	0.04960096249006299	0.42323526934661976
+Cytoplasm_AreaShape_Zernike_6_2	2.970658701462181e-05	0.9995404325247685	0.0001860767220049336	-0.002163602814667448	0.002867824038275239	-0.0028678240382752434	0.9999998395215051	0.04960096249006299	6.969493040761916e-08
+Cytoplasm_AreaShape_Zernike_6_4	0.007918293552767297	0.0025352718563537158	-0.006839721077140726	0.12816145103295765	0.09475557652661619	-0.09475557652661531	0.0040655395376058895	0.04960096249006299	2.39088181104011
+Cytoplasm_AreaShape_Zernike_6_6	0.0011181228145985544	0.6795205828221265	-0.0017575224654418577	0.03818270864745717	0.04595928648884736	-0.04595928648884706	0.7659450452975619	0.04960096249006299	0.11580238882716848
+Cytoplasm_AreaShape_Zernike_7_1	0.005385854279391866	0.02508114297550923	0.0030574462643623455	-0.07119669627524755	-0.09959671835650799	0.09959671835650744	0.03491511630043911	0.04960096249006299	1.4569865070081909
+Cytoplasm_AreaShape_Zernike_7_3	0.0019179086405771706	0.4116481021941131	0.003210530594643503	-0.06217246391482098	-0.052767816344329845	0.0527678163443294	0.48372818486575436	0.04960096249006299	0.315398607308546
+Cytoplasm_AreaShape_Zernike_7_5	0.001032442676744627	0.7118637178323952	0.0014998235947499192	-0.0337508111855445	-0.04402247611497913	0.04402247611497885	0.796217510985945	0.04960096249006299	0.09896827533149698
+Cytoplasm_AreaShape_Zernike_7_7	3.5766377674884e-05	0.9993365740153008	0.00033478746114242304	-0.007137653175124886	-0.008196147972625776	0.00819614797262572	0.9999998395215051	0.04960096249006299	6.969493040761916e-08
+Cytoplasm_AreaShape_Zernike_8_0	0.0014528502275646948	0.5581676915178619	0.001034380278109718	-0.027776660335313008	-0.04888184873812218	0.04888184873812193	0.6403485077591282	0.04960096249006299	0.19358359820054438
+Cytoplasm_AreaShape_Zernike_8_2	1.461736388130852e-05	0.9998875728891521	0.00029765458734849645	-0.005447373387437035	-0.003588450730478407	0.0035884507304783705	0.9999998395215051	0.04960096249006299	6.969493040761916e-08
+Cytoplasm_AreaShape_Zernike_8_4	0.001596521357308922	0.5097792329261129	-0.0015606591041984036	0.037071909930880734	0.05384286566879661	-0.0538428656687963	0.5894957066008139	0.04960096249006299	0.22951935359919778
+Cytoplasm_AreaShape_Zernike_8_6	0.0024511655886019224	0.2811452603365874	-0.0022950321101087743	0.03184765053510717	-0.014123752556755425	0.014123752556755558	0.33720004881278715	0.04960096249006299	0.4721123711791975
+Cytoplasm_AreaShape_Zernike_8_8	0.0023878378749209173	0.2945966556464452	-0.003804538066846134	0.06960391941405278	0.045772361561530035	-0.045772361561529584	0.3515176949697049	0.04960096249006299	0.4540528082422379
+Cytoplasm_AreaShape_Zernike_9_1	0.010062470539738855	0.00033850358119946744	-0.00195379139899465	0.01128891885339868	-0.07715201861704793	0.0771520186170478	0.0005954585723826996	0.04960096249006299	3.225148448138291
+Cytoplasm_AreaShape_Zernike_9_3	0.0006214273596650832	0.8644589281830433	0.0010707097489291812	-0.024702901186262553	-0.033931856936258026	0.03393185693625782	0.9107412119968359	0.04960096249006299	0.040605010697844134
+Cytoplasm_AreaShape_Zernike_9_5	0.00024343336737187027	0.9733039708451275	0.00121062764921286	-0.021712917281225683	-0.012772628776978757	0.012772628776978618	0.9935868147102521	0.04960096249006299	0.0027941803886854014
+Cytoplasm_AreaShape_Zernike_9_7	0.00085930298869763	0.7774832717413938	-0.0007174932145907056	0.006106450509730906	-0.020262057605748954	0.020262057605748937	0.8654439870486655	0.04960096249006299	0.06276103512870945
+Cytoplasm_AreaShape_Zernike_9_9	0.0007003810085535189	0.8363914278888568	0.0019985911415759455	-0.0380192275027919	-0.030033967134088443	0.030033967134088173	0.899954075791439	0.04960096249006299	0.04577965182649448
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.0050287674705928875	0.03426765582386959	-0.005507817489283557	0.09900261499000954	0.059009099631753326	-0.059009099631752694	0.04636917064278857	0.04960096249006299	1.3337706718092108
+Cytoplasm_Correlation_Correlation_DAPI_RFP	0.01647709493797045	6.594567361073445e-07	-0.006627974205122522	0.09667752833493706	-0.021433259287394056	0.0214332592873945	1.4693090827413128e-06	0.04960096249006299	5.832886836742456
+Cytoplasm_Correlation_Correlation_GFP_RFP	0.00199587704315185	0.39006033120792405	0.0018136544577989986	-0.04256969265267932	-0.06046439275583843	0.06046439275583807	0.4602236224922762	0.04960096249006299	0.33703109350147287
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.12668875220153752	1.1102230246251565e-16	-0.007711576775701956	0.22597520296845497	0.4421891625863014	-0.44218916258629926	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_Correlation_Costes_DAPI_RFP	0.058169420512800385	1.1102230246251565e-16	-0.012945726387770452	0.27949855271431157	0.33132311992936303	-0.3313231199293609	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.07171136556320179	1.1102230246251565e-16	-0.0027105789835034006	0.11578817655987528	0.3050791104063639	-0.30507911040636265	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_Correlation_Costes_GFP_RFP	0.016622614806175218	5.709150631139082e-07	-0.0014483839868720961	0.05829414008342612	0.14829604795815957	-0.14829604795815893	1.2870531811169852e-06	0.04960096249006299	5.890403507650025
+Cytoplasm_Correlation_Costes_RFP_DAPI	0.19802434686833292	1.1102230246251565e-16	-0.019314341764655584	0.4442044078430728	0.6063003461727692	-0.6063003461727656	4.028027911218146e-16	0.04960096249006299	15.394907528772336
+Cytoplasm_Correlation_K_DAPI_GFP	0.008743794398715199	0.001175018238829817	-0.007234226804901091	0.1290649328631211	0.07351383959747056	-0.07351383959746972	0.0019488516789734538	0.04960096249006299	2.7102212124239538
+Cytoplasm_Correlation_K_DAPI_RFP	0.006214930262296137	0.012005574295978816	-0.0012012309935377358	0.0025386693453899184	-0.06555298649074703	0.0655529864907469	0.017707924999207154	0.04960096249006299	1.7518323261160185
+Cytoplasm_Correlation_K_GFP_DAPI	0.005581927725120228	0.02110119235699781	0.005338620229683115	-0.08979893093517345	-0.03183237383401087	0.031832373834010344	0.029659181993310482	0.04960096249006299	1.527840831078722
+Cytoplasm_Correlation_K_GFP_RFP	0.018068858300495982	1.3558242606404036e-07	0.0069266749065762115	-0.1513700131877782	-0.18477915531346473	0.18477915531346353	3.1608674028182904e-07	0.04960096249006299	6.500193722266139
+Cytoplasm_Correlation_K_RFP_DAPI	0.008078073617189152	0.0021861367942233034	-0.0019245395858671626	0.05666869649934925	0.11147939829574158	-0.11147939829574104	0.003534964927706484	0.04960096249006299	2.451614890715484
+Cytoplasm_Correlation_K_RFP_GFP	0.013895972690740388	8.368139717385148e-06	-0.007563196441933077	0.15446621286821482	0.15725015850203122	-0.1572501585020301	1.7318021768064452e-05	0.04960096249006299	4.76150171880141
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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..194b569
--- /dev/null
+++ b/1.train_models/linear_reg_plate_1_2_norm_data/nbconverted/train_model.py
@@ -0,0 +1,220 @@
+#!/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
+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[ ]:
+
+
+# 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
+
+
+# ## Specify paths
+
+# In[ ]:
+
+
+data_path = Path("data")
+
+data_path.mkdir(
+    parents=True, exist_ok=True
+)  # Create the parent directories if they don't exist
+
+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"
+
+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")
+
+
+# ## 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
+
+# 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
+
+
+# ## Fit linear model
+
+# 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",
+    ],
+)
+
+
+# ## Controlling for FDR
+
+# In[1]:
+
+
+# Benjamini-Hochberg procedure has a greater power than Bonferroni, and still controls for FDR
+# Here, alpha is the FDR level
+rejected, corrected_pvalues, _, _ = multipletests(
+    lm_results["p_value"].tolist(), method="fdr_bh", alpha=0.05
+)
+
+# Store the correcte p values
+lm_results["corrected_p_value"] = corrected_pvalues
+
+# Store the critical threshold value in one column
+lm_results["critical_threshold"] = [max(corrected_pvalues[rejected])] * len(lm_results)
+
+# 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
new file mode 100644
index 0000000..5afc4d8
--- /dev/null
+++ b/1.train_models/linear_reg_plate_1_2_norm_data/train_model.ipynb
@@ -0,0 +1,411 @@
+{
+ "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": "2023-06-16T22:18:53.878090Z",
+     "iopub.status.busy": "2023-06-16T22:18:53.877993Z",
+     "iopub.status.idle": "2023-06-16T22:18:54.265267Z",
+     "shell.execute_reply": "2023-06-16T22:18:54.264836Z"
+    },
+    "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": "2023-06-16T22:18:54.266824Z",
+     "iopub.status.busy": "2023-06-16T22:18:54.266517Z",
+     "iopub.status.idle": "2023-06-16T22:18:54.269553Z",
+     "shell.execute_reply": "2023-06-16T22:18:54.269141Z"
+    },
+    "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": "2023-06-16T22:18:54.271200Z",
+     "iopub.status.busy": "2023-06-16T22:18:54.270996Z",
+     "iopub.status.idle": "2023-06-16T22:18:54.273478Z",
+     "shell.execute_reply": "2023-06-16T22:18:54.273099Z"
+    },
+    "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": "2023-06-16T22:18:54.275159Z",
+     "iopub.status.busy": "2023-06-16T22:18:54.274898Z",
+     "iopub.status.idle": "2023-06-16T22:18:54.277364Z",
+     "shell.execute_reply": "2023-06-16T22:18:54.276972Z"
+    },
+    "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": "2023-06-16T22:18:54.279081Z",
+     "iopub.status.busy": "2023-06-16T22:18:54.278820Z",
+     "iopub.status.idle": "2023-06-16T22:18:54.394842Z",
+     "shell.execute_reply": "2023-06-16T22:18:54.394348Z"
+    },
+    "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": "2023-06-16T22:18:54.396880Z",
+     "iopub.status.busy": "2023-06-16T22:18:54.396712Z",
+     "iopub.status.idle": "2023-06-16T22:18:54.408032Z",
+     "shell.execute_reply": "2023-06-16T22:18:54.407644Z"
+    },
+    "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": "2023-06-16T22:18:54.409714Z",
+     "iopub.status.busy": "2023-06-16T22:18:54.409419Z",
+     "iopub.status.idle": "2023-06-16T22:18:55.871159Z",
+     "shell.execute_reply": "2023-06-16T22:18:55.870646Z"
+    },
+    "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": "2023-06-16T22:18:55.873006Z",
+     "iopub.status.busy": "2023-06-16T22:18:55.872886Z",
+     "iopub.status.idle": "2023-06-16T22:18:55.876392Z",
+     "shell.execute_reply": "2023-06-16T22:18:55.875980Z"
+    },
+    "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": "2023-06-16T22:18:55.878283Z",
+     "iopub.status.busy": "2023-06-16T22:18:55.877989Z",
+     "iopub.status.idle": "2023-06-16T22:18:55.881660Z",
+     "shell.execute_reply": "2023-06-16T22:18:55.881300Z"
+    },
+    "jukit_cell_id": "jz9zRzmXxt"
+   },
+   "outputs": [],
+   "source": [
+    "# Benjamini-Hochberg procedure has a greater power than Bonferroni, and still controls for FDR\n",
+    "# Here, alpha is the FDR level\n",
+    "rejected, corrected_pvalues, _, _ = multipletests(\n",
+    "    lm_results[\"p_value\"].tolist(), method=\"fdr_bh\", alpha=0.05\n",
+    ")\n",
+    "\n",
+    "# Store the correcte p values\n",
+    "lm_results[\"corrected_p_value\"] = corrected_pvalues\n",
+    "\n",
+    "# Store the critical threshold value in one column\n",
+    "lm_results[\"critical_threshold\"] = [max(corrected_pvalues[rejected])] * len(lm_results)\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": "2023-06-16T22:18:55.883704Z",
+     "iopub.status.busy": "2023-06-16T22:18:55.883388Z",
+     "iopub.status.idle": "2023-06-16T22:18:55.894601Z",
+     "shell.execute_reply": "2023-06-16T22:18:55.894241Z"
+    },
+    "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"
+  },
+  "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
+}
diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/.~lock.feature_compartments.tsv# b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/.~lock.feature_compartments.tsv#
new file mode 100644
index 0000000..92a2fc9
--- /dev/null
+++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/.~lock.feature_compartments.tsv#
@@ -0,0 +1 @@
+,camo,oak,16.06.2023 16:24,file:///home/camo/.config/libreoffice/4;
\ No newline at end of file
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..ffe9eff
--- /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	cell_count_coef	plate_coef	Null_coef	WT_coef	corrected_p_value	critical_threshold	neg_log_p	compartment
+Cytoplasm_Granularity_10_RFP	0.0037617125664257	0.1003252629539767	0.0035668092075674	-0.0547577118054782	0.0002926773188153	-0.0002926773188156	0.1270203165644132	0.0496009624900629	0.896126809231911	actin
+Cytoplasm_Granularity_11_RFP	0.0083140810700031	0.0017553507473393	0.0020349194384395	-0.0152096139187058	0.0661476342826508	-0.0661476342826508	0.00285829203038	0.0496009624900629	2.543893401611021	actin
+Cytoplasm_Granularity_12_RFP	0.0170346819190725	3.793612946001446e-07	-0.0086265789374558	0.1743870117677087	0.1719627433359708	-0.1719627433359696	8.636048294720938e-07	0.0496009624900629	6.06368493765877	actin
+Cytoplasm_Granularity_13_RFP	0.0165343642920898	6.230874355361848e-07	-0.0013168894588963	0.0558714466217581	0.1466440481927371	-0.1466440481927365	1.3992350341537925e-06	0.0496009624900629	5.854109329494796	actin
+Cytoplasm_Granularity_14_RFP	0.0098501891702583	0.0004140812515598	0.0048833782463788	-0.0696969351968968	0.0221031201207769	-0.0221031201207772	0.0007182589532842	0.0496009624900629	3.143718951708868	actin
+Cytoplasm_Granularity_15_RFP	0.034125533803847	1.0436096431476471e-14	-0.0117844982540997	0.2411427339495568	0.2469237178768606	-0.2469237178768588	3.6060440348048167e-14	0.0496009624900629	13.442968974242577	actin
+Cytoplasm_Granularity_16_RFP	0.0053252103386409	0.0264527395149072	-0.0011101117449085	0.0381216351097182	0.0866696542894571	-0.0866696542894567	0.0365827648580458	0.0496009624900629	1.4367234744414588	actin
+Cytoplasm_Granularity_1_RFP	0.0797662447888054	1.1102230246251563e-16	0.0128883932375536	-0.2919905507118198	-0.3863669662163332	0.3863669662163309	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Granularity_2_RFP	0.0265761238729764	2.5021873462094387e-11	0.0055619175677227	-0.0646411039510882	0.0858438944240999	-0.0858438944241001	7.190691853834551e-11	0.0496009624900629	10.143229321877568	actin
+Cytoplasm_Granularity_3_RFP	0.0247453188850123	1.6195356167258979e-10	0.0058182685634424	-0.0713392817216137	0.0744939196170529	-0.0744939196170531	4.434624648629168e-10	0.0496009624900629	9.35314313342588	actin
+Cytoplasm_Granularity_4_RFP	0.0237508762052655	4.450164681202296e-10	0.0064129908206857	-0.0842132596546174	0.0591334779903649	-0.0591334779903652	1.1795984463186907e-09	0.0496009624900629	8.928265808140978	actin
+Cytoplasm_Granularity_5_RFP	0.0152669315254935	2.179214255049189e-06	0.0056152854233033	-0.0771713489077849	0.0376466898321505	-0.0376466898321508	4.676650185050108e-06	0.0496009624900629	5.330065114244632	actin
+Cytoplasm_Granularity_6_RFP	0.0135583503755253	1.163737040010826e-05	0.0050243723061764	-0.0672611482336932	0.041049393082168	-0.0410493930821682	2.378694900444664e-05	0.0496009624900629	4.623661258488416	actin
+Cytoplasm_Granularity_7_RFP	0.0068161411470623	0.006973486447476	0.0014019186773225	-0.0053777353069579	0.0665650786828842	-0.066565078682884	0.0106529181125259	0.0496009624900629	1.9725314113261163	actin
+Cytoplasm_Granularity_8_RFP	0.0091773954485366	0.0007819895285646	0.001378595879782	-0.00155702630656	0.0808152695319881	-0.0808152695319879	0.0013138782093539	0.0496009624900629	2.881444890070697	actin
+Cytoplasm_Granularity_9_RFP	0.0118764346599471	5.95448596415471e-05	0.0020014519906748	-0.009960290523731	0.0856360697971641	-0.085636069797164	0.0001120447034746	0.0496009624900629	3.950608668411528	actin
+Cytoplasm_Intensity_IntegratedIntensityEdge_RFP	0.0410414795005028	1.1102230246251563e-16	0.0024254421868306	-0.0942501363970267	-0.2344703644148723	0.2344703644148713	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Intensity_IntegratedIntensity_RFP	0.0783838235819537	1.1102230246251563e-16	-0.0059602684243162	0.0411874016199504	-0.2075817341685001	0.2075817341684998	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Intensity_LowerQuartileIntensity_RFP	0.0117074913163893	7.008198051117187e-05	0.0080573271541586	-0.1388208934280313	-0.0615911889616559	0.061591188961655	0.0001310228331295	0.0496009624900629	3.882653013977188	actin
+Cytoplasm_Intensity_MADIntensity_RFP	0.0120110505928773	5.2287397473871344e-05	0.0063345186776424	-0.096996550023088	0.0015532221785253	-0.0015532221785258	9.951748929043382e-05	0.0496009624900629	4.002100589255595	actin
+Cytoplasm_Intensity_MassDisplacement_RFP	0.0068526086027598	0.0067459980964077	-0.0063225542477175	0.1111022845202778	0.0572621905582353	-0.0572621905582346	0.0103325907518857	0.0496009624900629	1.985790771589916	actin
+Cytoplasm_Intensity_MaxIntensityEdge_RFP	0.0041062181249255	0.0753599505618121	0.0045262376910301	-0.0756843357731082	-0.0251370088643017	0.0251370088643013	0.0966772404444904	0.0496009624900629	1.0146757545907688	actin
+Cytoplasm_Intensity_MaxIntensity_RFP	0.0006338886393454	0.8601030189140192	0.000867179392635	-0.0101453541758644	0.0131088137209483	-0.0131088137209483	0.90794788892552	0.0496009624900629	0.0419390768110094	actin
+Cytoplasm_Intensity_MeanIntensityEdge_RFP	0.0215595384756238	4.088002825142212e-09	0.0109928339234933	-0.1900924928704129	-0.0868924366320079	0.0868924366320068	1.03854951422103e-08	0.0496009624900629	7.9835727930717315	actin
+Cytoplasm_Intensity_MeanIntensity_RFP	0.0131344051062545	1.7591094030544596e-05	0.0077512073248387	-0.1269587121176591	-0.0321353408029062	0.0321353408029054	3.564268790482073e-05	0.0496009624900629	4.448029552160014	actin
+Cytoplasm_Intensity_MedianIntensity_RFP	0.0124893630682384	3.2914871870848295e-05	0.0077828123652436	-0.1292338032851874	-0.0394998358868462	0.0394998358868455	6.543521616790217e-05	0.0496009624900629	4.184188458539435	actin
+Cytoplasm_Intensity_MinIntensityEdge_RFP	0.0334153258585635	2.1871393585115584e-14	0.0141534714020136	-0.2528387414662714	-0.1451786615535209	0.1451786615535193	7.468437633035057e-14	0.0496009624900629	13.126770241333093	actin
+Cytoplasm_Intensity_MinIntensity_RFP	0.0459096749019718	1.1102230246251563e-16	0.0139862277294537	-0.2254965389048376	-0.0432211944325902	0.0432211944325889	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Intensity_StdIntensityEdge_RFP	0.0093846222373806	0.0006432341403108	0.0055073526540247	-0.0837429900493568	0.003769261377484	-0.0037692613774845	0.0010854576117745	0.0496009624900629	2.964387131520325	actin
+Cytoplasm_Intensity_StdIntensity_RFP	0.0127078278707797	2.6629636778685573e-05	0.0053962153571932	-0.0760381255555585	0.0284503887020656	-0.0284503887020659	5.339725095000682e-05	0.0496009624900629	4.272481101173216	actin
+Cytoplasm_Intensity_UpperQuartileIntensity_RFP	0.0123650362768117	3.712815934120428e-05	0.0074269157328729	-0.1209599647446009	-0.0279627913135012	0.0279627913135005	7.293704398500536e-05	0.0496009624900629	4.137051841886167	actin
+Cytoplasm_Location_CenterMassIntensity_X_RFP	0.0007866405503756	0.804708211979182	0.0017169009877283	-0.0269326316982328	-0.0022247178774269	0.0022247178774267	0.8756009691732242	0.0496009624900629	0.057693766397904	actin
+Cytoplasm_Location_CenterMassIntensity_Y_RFP	0.000656748031542	0.8520359307733876	0.0018676072589494	-0.0362763848469504	-0.0311478870516149	0.0311478870516147	0.905989610177744	0.0496009624900629	0.0428767827518256	actin
+Cytoplasm_Location_MaxIntensity_X_RFP	0.0006511994566603	0.854002759221398	0.0017044759477919	-0.0277609512892808	-0.006420169001206	0.0064201690012059	0.905989610177744	0.0496009624900629	0.0428767827518256	actin
+Cytoplasm_Location_MaxIntensity_Y_RFP	0.0005174445711179	0.8994334515678626	0.0017716198893725	-0.0322825850893986	-0.020782759203354	0.0207827592033538	0.9365401231123665	0.0496009624900629	0.0284736119107115	actin
+Cytoplasm_RadialDistribution_FracAtD_RFP_1of4	0.0033651275081684	0.1385223330158672	0.0029849522202331	-0.0434724530061837	0.0099280259285686	-0.0099280259285688	0.172188895750987	0.0496009624900629	0.7639948590935584	actin
+Cytoplasm_RadialDistribution_FracAtD_RFP_2of4	0.002975672842688	0.1885793123074293	0.0030269409493874	-0.0455550859462327	0.0040126553094279	-0.0040126553094281	0.2321745297867714	0.0496009624900629	0.6341854253372343	actin
+Cytoplasm_RadialDistribution_FracAtD_RFP_3of4	0.0016069979523801	0.5063546830315826	0.0026992343448554	-0.0441222182551672	-0.0108237713132133	0.010823771313213	0.5861194287135268	0.0496009624900629	0.2320138823665032	actin
+Cytoplasm_RadialDistribution_FracAtD_RFP_4of4	0.0021831595011518	0.3417661919858089	-0.0029029977041593	0.0458248976215361	0.0049397765203576	-0.0049397765203573	0.405301888555183	0.0496009624900629	0.3922213726037699	actin
+Cytoplasm_RadialDistribution_MeanFrac_RFP_1of4	0.0041730961569144	0.0712474758032654	0.0042402008201553	-0.0685295849438199	-0.0137860330167952	0.0137860330167948	0.091807235746494	0.0496009624900629	1.037123088723198	actin
+Cytoplasm_RadialDistribution_MeanFrac_RFP_2of4	0.0084713135521656	0.0015159805127747	0.0061519844039941	-0.1002295009106718	-0.0233023869662074	0.0233023869662069	0.002485951095101	0.0496009624900629	2.604507419273082	actin
+Cytoplasm_RadialDistribution_MeanFrac_RFP_3of4	0.0075221400003641	0.003655092389363	0.0049933179932158	-0.0763330785476995	0.0017450207236831	-0.0017450207236835	0.0057578863827007	0.0496009624900629	2.239736909074633	actin
+Cytoplasm_RadialDistribution_MeanFrac_RFP_4of4	0.0121196455642041	4.707829976413613e-05	-0.0072206643421422	0.1166612137912706	0.023318917228897	-0.0233189172288963	9.049322189761929e-05	0.0496009624900629	4.043383949001458	actin
+Cytoplasm_RadialDistribution_RadialCV_RFP_1of4	0.0026260879754109	0.2466609527386866	0.0004574287005823	-0.0210658753236876	-0.0577644151212342	0.0577644151212339	0.2973762888157997	0.0496009624900629	0.5266936627373521	actin
+Cytoplasm_RadialDistribution_RadialCV_RFP_2of4	0.0012068486611087	0.646422970814643	-0.0016614681402573	0.0234019291216489	-0.008800216418893	0.0088002164188931	0.7343415549078283	0.0496009624900629	0.1341018952478129	actin
+Cytoplasm_RadialDistribution_RadialCV_RFP_3of4	0.0013550008166336	0.5925396420711423	-0.0026668115339786	0.0451915210880772	0.0172763571460721	-0.0172763571460718	0.6771048468942875	0.0496009624900629	0.1693440773975106	actin
+Cytoplasm_RadialDistribution_RadialCV_RFP_4of4	0.0127380083940388	2.586076535893156e-05	0.0005067883890415	0.0189556314604897	0.1100848830106401	-0.1100848830106398	5.203526617282416e-05	0.0496009624900629	4.283702219576496	actin
+Cytoplasm_Texture_AngularSecondMoment_RFP_3_00_256	0.0056998031373336	0.0190106584504488	-0.0052480351752299	0.1041003450046695	0.096427527881142	-0.0964275278811413	0.0268835255310244	0.0496009624900629	1.5705137780933194	actin
+Cytoplasm_Texture_AngularSecondMoment_RFP_3_01_256	0.0058855139846075	0.0161185542196214	-0.0051511656067007	0.1035428680412538	0.1002619311701623	-0.1002619311701616	0.0231891467769275	0.0496009624900629	1.6347152305292885	actin
+Cytoplasm_Texture_AngularSecondMoment_RFP_3_02_256	0.0066992731388415	0.007754275971758	-0.0053112823906291	0.1080574927334363	0.1087132709422475	-0.1087132709422467	0.011783657595826	0.0496009624900629	1.9287198855140173	actin
+Cytoplasm_Texture_AngularSecondMoment_RFP_3_03_256	0.0047098663319654	0.0451420877274879	-0.0042691223949513	0.0880840593671923	0.0924412958027217	-0.0924412958027211	0.0597218035092884	0.0496009624900629	1.223867085704123	actin
+Cytoplasm_Texture_Contrast_RFP_3_00_256	0.0108099990497549	0.0001659423409569	0.0052649060725507	-0.0761300086281289	0.0197642510736147	-0.019764251073615	0.0003003491050689	0.0496009624900629	3.5223736577542737	actin
+Cytoplasm_Texture_Contrast_RFP_3_01_256	0.0101275248352172	0.0003182036719878	0.0041393361461663	-0.0540134368490686	0.0395799103106155	-0.0395799103106157	0.0005614505519421	0.0496009624900629	3.2506884868901844	actin
+Cytoplasm_Texture_Contrast_RFP_3_02_256	0.0094860165544985	0.000584508231353	0.0032303225432609	-0.0367902806217319	0.0529560830667666	-0.0529560830667667	0.000993578413764	0.0496009624900629	3.0027978524382077	actin
+Cytoplasm_Texture_Contrast_RFP_3_03_256	0.0113639288331967	9.755002624434184e-05	0.0046752438378403	-0.0631143960529715	0.036027801191574	-0.0360278011915742	0.0001794858644527	0.0496009624900629	3.7459697489347232	actin
+Cytoplasm_Texture_Correlation_RFP_3_00_256	0.0014710614494708	0.5518920123455003	-0.0017555195523293	0.024210888924264	-0.0114212252189092	0.0114212252189093	0.6350313442350107	0.0496009624900629	0.1972048380271594	actin
+Cytoplasm_Texture_Correlation_RFP_3_01_256	0.0036858884535249	0.1067717390132266	-0.0039652654240318	0.063947222237492	0.0123204727724432	-0.0123204727724429	0.1344490119244644	0.0496009624900629	0.8714423850905837	actin
+Cytoplasm_Texture_Correlation_RFP_3_02_256	0.0013894987860894	0.5802999147647814	-0.0028787015808694	0.0536378847701312	0.0386348171718561	-0.0386348171718557	0.6644262337691431	0.0496009624900629	0.1775532284839559	actin
+Cytoplasm_Texture_Correlation_RFP_3_03_256	0.006095704610984	0.0133599565399191	-0.0042403560800797	0.0632321582100238	-0.0080277053616587	0.008027705361659	0.0194372300035666	0.0496009624900629	1.7113656262349033	actin
+Cytoplasm_Texture_DifferenceEntropy_RFP_3_00_256	0.011989849186179	5.336922419707513e-05	0.0070659112723833	-0.1133561534587561	-0.0195067146270313	0.0195067146270307	0.0001010793952574	0.0496009624900629	3.995337365061848	actin
+Cytoplasm_Texture_DifferenceEntropy_RFP_3_01_256	0.0117275975085582	6.873683900254779e-05	0.0073688133894119	-0.1210302570403868	-0.0319282517770216	0.0319282517770209	0.0001287152743257	0.0496009624900629	3.890369913377092	actin
+Cytoplasm_Texture_DifferenceEntropy_RFP_3_02_256	0.0110131035573778	0.0001366094546491	0.0072992669158253	-0.1211315623968446	-0.036745431574842	0.0367454315748413	0.0002497694123584	0.0496009624900629	3.6024607479367208	actin
+Cytoplasm_Texture_DifferenceEntropy_RFP_3_03_256	0.0127772886991228	2.489300528341065e-05	0.00740794364871	-0.1196443374752259	-0.0237481080545137	0.023748108054513	5.01749637743746e-05	0.0496009624900629	4.299512932394935	actin
+Cytoplasm_Texture_DifferenceVariance_RFP_3_00_256	0.0051343575130285	0.0312582491689709	-0.0043863119611794	0.0909703306211169	0.0969064324943803	-0.0969064324943796	0.0426950909237355	0.0496009624900629	1.3696220572301483	actin
+Cytoplasm_Texture_DifferenceVariance_RFP_3_01_256	0.0061567845308748	0.0126484920237794	-0.0049813075072532	0.1020956662536104	0.1050522682387411	-0.1050522682387403	0.0184948353143677	0.0496009624900629	1.7329495314683367	actin
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+Cytoplasm_Correlation_RWC_DAPI_RFP	0.0003289541781874	0.9539638694053856	-0.0012842748998345	0.0215014969484857	0.0072427652487012	-0.007242765248701	0.9784028731269016	0.0496009624900629	0.0094822806433499	other
+Cytoplasm_Correlation_RWC_RFP_DAPI	0.0011901726515266	0.6526040608105218	-0.0024868535375875	0.0485607984337623	0.042530077114726	-0.0425300771147257	0.7406386261984514	0.0496009624900629	0.1303936421280515	other
+Cells_Correlation_Correlation_DAPI_RFP	0.0515020343225011	1.1102230246251563e-16	-0.0071793423605238	0.0788833066199859	-0.1295584105219209	0.129558410521921	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_Costes_DAPI_RFP	0.0666268685717135	1.1102230246251563e-16	-0.0155208967755213	0.3237219422461789	0.3504128459838208	-0.3504128459838183	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_Costes_RFP_DAPI	0.1950077784572738	1.1102230246251563e-16	-0.0189347760296095	0.4370801441522227	0.600992434586833	-0.6009924345868295	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_K_DAPI_RFP	0.0120043749119953	5.2625663170102754e-05	-0.0031415952467806	0.0317357736562066	-0.0681463686155727	0.0681463686155727	9.999737306135728e-05	0.0496009624900629	4.00001140879942	other
+Cells_Correlation_K_RFP_DAPI	0.0069883102278036	0.0059615598204791	-0.0019587218361822	0.0556174693117202	0.1049898909030043	-0.1049898909030038	0.0091795370710561	0.0496009624900629	2.037179219948562	other
+Cells_Correlation_Manders_DAPI_RFP	0.0083424355388554	0.0017095895841842	0.004249161980246	-0.095714758301525	-0.1251117275297823	0.1251117275297815	0.0027876875101655	0.0496009624900629	2.554755910709221	other
+Cells_Correlation_Manders_RFP_DAPI	0.0192910944690601	3.9977182120232164e-08	0.0070008625750292	-0.1010215190196223	0.0271463198430092	-0.0271463198430097	9.64937805438452e-08	0.0496009624900629	7.015500677977812	other
+Cells_Correlation_Overlap_DAPI_RFP	0.0300487556106749	7.086553566182374e-13	-0.002925585927757	0.010951848466946	-0.1400249217830361	0.1400249217830358	2.248652084207901e-12	0.0496009624900629	11.648077734247408	other
+Cells_Correlation_RWC_DAPI_RFP	0.0209599401776	7.48117667814796e-09	-0.0032093080671393	0.0237338072160324	-0.1053662164696798	0.1053662164696797	1.867880886737588e-08	0.0496009624900629	7.728650821832879	other
+Cells_Correlation_RWC_RFP_DAPI	0.0151474151660359	2.451352801524642e-06	0.0059058427178638	-0.0833146486867555	0.0307447981671124	-0.0307447981671128	5.241290244143848e-06	0.0496009624900629	5.2805617899368125	other
+Nuclei_Correlation_Correlation_DAPI_RFP	0.0168432113581571	4.5875122578475924e-07	0.0001420515628276	-0.0350490459478889	-0.135272315080336	0.1352723150803355	1.036206562521606e-06	0.0496009624900629	5.984553661558746	other
+Nuclei_Correlation_Costes_DAPI_RFP	0.0653722521256117	1.1102230246251563e-16	-0.0155537567470738	0.3232207860753828	0.3462710611474306	-0.3462710611474281	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Nuclei_Correlation_Costes_RFP_DAPI	0.0179149635141995	1.580573694148768e-07	-0.0069226331361114	0.1511162345908681	0.1839903398971243	-0.1839903398971231	3.677447011837113e-07	0.0496009624900629	6.434453576284332	other
+Nuclei_Correlation_K_DAPI_RFP	0.0194414548671663	3.4387600544860675e-08	-0.0011421086465465	-0.0138844420153685	-0.1294089263510944	0.129408926351094	8.352302140707793e-08	0.0496009624900629	7.078193803653505	other
+Nuclei_Correlation_K_RFP_DAPI	0.0094306032780084	0.0006159131487293	-0.0025832658832332	0.0698811387485057	0.1241833980800298	-0.1241833980800292	0.0010423836234325	0.0496009624900629	2.981972420308452	other
+Nuclei_Correlation_Manders_DAPI_RFP	0.0003548282703975	0.9473520293555764	0.0012507589608186	-0.0252429773741715	-0.0247630483994902	0.02476304839949	0.9742831499118352	0.0496009624900629	0.0113148084463755	other
+Nuclei_Correlation_Manders_RFP_DAPI	0.0006703475639939	0.8471927376411222	-0.0010841604293917	0.0246815908186427	0.0329931187023357	-0.0329931187023355	0.905989610177744	0.0496009624900629	0.0428767827518256	other
+Nuclei_Correlation_Overlap_DAPI_RFP	0.0333243308966041	2.398081733190338e-14	-0.0059602732081601	0.0670969512739812	-0.1009397653269125	0.1009397653269126	8.140856410040885e-14	0.0496009624900629	13.089329905357634	other
+Nuclei_Correlation_RWC_DAPI_RFP	0.0094345830398724	0.0006136031128202	-0.0064997025887186	0.1059483776570577	0.0248408015139323	-0.0248408015139317	0.0010399900934076	0.0496009624900629	2.9829707976232047	other
+Nuclei_Correlation_RWC_RFP_DAPI	0.0077959130337511	0.0028392756370917	-0.0067345404406127	0.1181910975788669	0.0603729435911854	-0.0603729435911847	0.0045156150885801	0.0496009624900629	2.345283084468512	other
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.0050287674705928	0.0342676558238695	-0.0055078174892835	0.0990026149900095	0.0590090996317533	-0.0590090996317526	0.0463691706427885	0.0496009624900629	1.3337706718092108	other
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.1266887522015375	1.1102230246251563e-16	-0.0077115767757019	0.2259752029684549	0.4421891625863014	-0.4421891625862992	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.0717113655632017	1.1102230246251563e-16	-0.0027105789835034	0.1157881765598752	0.3050791104063639	-0.3050791104063626	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cytoplasm_Correlation_K_DAPI_GFP	0.0087437943987151	0.0011750182388298	-0.007234226804901	0.1290649328631211	0.0735138395974705	-0.0735138395974697	0.0019488516789734	0.0496009624900629	2.7102212124239538	other
+Cytoplasm_Correlation_K_GFP_DAPI	0.0055819277251202	0.0211011923569978	0.0053386202296831	-0.0897989309351734	-0.0318323738340108	0.0318323738340103	0.0296591819933104	0.0496009624900629	1.527840831078722	other
+Cytoplasm_Correlation_Manders_DAPI_GFP	0.0568267726108727	1.1102230246251563e-16	0.0105186745166461	-0.1424403356219332	0.0792410890424681	-0.0792410890424686	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cytoplasm_Correlation_Manders_GFP_DAPI	0.0017588441984072	0.4583991703870796	-0.0019730427438016	0.0442960425345264	0.0574854399610166	-0.0574854399610163	0.5348758158988939	0.0496009624900629	0.2717470380352514	other
+Cytoplasm_Correlation_Overlap_DAPI_GFP	0.0052213862560118	0.028970805994453	-0.0027778261739751	0.0348727177146356	-0.0322191738170465	0.0322191738170466	0.0398992950884459	0.0496009624900629	1.3990347770426366	other
+Cytoplasm_Correlation_RWC_DAPI_GFP	0.0250899351958763	1.1402523369952178e-10	0.0102176355639956	-0.1638920903800971	-0.0280999111537201	0.0280999111537192	3.15198324583678e-10	0.0496009624900629	9.501416099640595	other
+Cytoplasm_Correlation_RWC_GFP_DAPI	0.0040815277114809	0.0769340543175093	0.0045670669000405	-0.0768384367681438	-0.0273039563786379	0.0273039563786374	0.0984789824284766	0.0496009624900629	1.00665644756639	other
+Cells_Correlation_Correlation_DAPI_GFP	0.0120121827110191	5.223024593559167e-05	-0.0082808309727361	0.1441675777139306	0.0694567357186915	-0.0694567357186906	9.951748929043382e-05	0.0496009624900629	4.002100589255595	other
+Cells_Correlation_Costes_DAPI_GFP	0.1287306386618018	1.1102230246251563e-16	-0.0159824554903969	0.3647135469087196	0.489929153154612	-0.4899291531546089	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_Costes_GFP_DAPI	0.0725736544592836	1.1102230246251563e-16	-0.0026318368579147	0.1147914535206022	0.3059586759868545	-0.3059586759868531	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_K_DAPI_GFP	0.0136828962432701	1.0305116863840167e-05	-0.0069037054251493	0.1448310793092576	0.1593185470532011	-0.1593185470532	2.113823441505024e-05	0.0496009624900629	4.67493129024634	other
+Cells_Correlation_K_GFP_DAPI	0.0233561640861427	6.641966976417281e-10	0.0106113349978678	-0.2105839322869011	-0.195371542857158	0.1953715428571565	1.7525735590046503e-09	0.0496009624900629	8.756323744773926	other
+Cells_Correlation_Manders_DAPI_GFP	0.0293732964052595	1.4205303600078878e-12	0.0023528815075556	-0.0006521660881691	0.1461827762937873	-0.1461827762937869	4.457393913430156e-12	0.0496009624900629	11.35091898429622	other
+Cells_Correlation_Manders_GFP_DAPI	0.0288016347820732	2.556732603409273e-12	0.0054258488493823	-0.0601105034701542	0.095882513249416	-0.0958825132494161	7.894591895101505e-12	0.0496009624900629	11.102670315596974	other
+Cells_Correlation_Overlap_DAPI_GFP	0.0035468255613133	0.1196052879976049	0.0036878280018297	-0.0762026908630778	-0.0803167972663356	0.080316797266335	0.1494744234286537	0.0496009624900629	0.8254331131192995	other
+Cells_Correlation_RWC_DAPI_GFP	0.0360072716847414	1.4432899320127037e-15	-0.0131163880035569	0.2607626491605052	0.2434094004456848	-0.243409400445683	5.124341318246938e-15	0.0496009624900629	14.29036195080588	other
+Cells_Correlation_RWC_GFP_DAPI	0.0308150581209546	3.2163161023390785e-13	0.0046841958972369	-0.0440597428885552	0.1150220960993887	-0.1150220960993887	1.0430567024624778e-12	0.0496009624900629	11.98169208189254	other
+Nuclei_Correlation_Correlation_DAPI_GFP	0.0055975849365728	0.020811189718118	-0.0027688599181534	0.0668973607803623	0.1001597794611853	-0.1001597794611848	0.0293130133835243	0.0496009624900629	1.5329395337087124	other
+Nuclei_Correlation_Costes_DAPI_GFP	0.0917366799612873	1.1102230246251563e-16	-0.0148851988489012	0.3298836164767035	0.415994629305783	-0.4159946293057804	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Nuclei_Correlation_K_DAPI_GFP	0.016042896725708	1.0133859220662345e-06	-0.0052536283477645	0.1225024387479125	0.1718156460245471	-0.171815646024546	2.2157082024838008e-06	0.0496009624900629	5.654487434555223	other
+Nuclei_Correlation_K_GFP_DAPI	0.0285387179234908	3.3494318429916348e-12	0.0073553503104055	-0.1690029733733402	-0.2302331058924449	0.2302331058924435	1.0314828566878748e-11	0.0496009624900629	10.986537985633367	other
+Nuclei_Correlation_Manders_DAPI_GFP	0.0053512008800226	0.0258562561936261	0.0030718253235459	-0.0405363457199373	0.0275093511301013	-0.0275093511301014	0.0359080304315789	0.0496009624900629	1.4448084154296303	other
+Nuclei_Correlation_Manders_GFP_DAPI	0.0007620641436754	0.8138200273288702	-0.001236679277527	0.0275934027714221	0.0353280921836848	-0.0353280921836845	0.8838587948819628	0.0496009624900629	0.0536171122515455	other
+Nuclei_Correlation_Overlap_DAPI_GFP	0.0184591607692713	9.185241267584132e-08	-0.0044333294839917	0.0498850683396133	-0.0751730352919696	0.0751730352919697	2.176339818707179e-07	0.0496009624900629	6.6622732919394245	other
+Nuclei_Correlation_RWC_DAPI_GFP	0.0189891962562755	5.4080493305797006e-08	-0.0038782622974246	0.1027773274118242	0.1776477320738891	-0.1776477320738881	1.291922895638484e-07	0.0496009624900629	6.888763405070014	other
+Nuclei_Correlation_RWC_GFP_DAPI	0.0233397702291401	6.753302361772738e-10	-0.003969839060068	0.1083301098895058	0.1947086060182426	-0.1947086060182416	1.777910213609558e-09	0.0496009624900629	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
new file mode 100644
index 0000000..5dbe9b7
--- /dev/null
+++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/data/significant_feature_compartments.tsv
@@ -0,0 +1,864 @@
+feature	r2_score	p_value	cell_count_coef	plate_coef	Null_coef	WT_coef	corrected_p_value	critical_threshold	neg_log_p	compartment
+Cytoplasm_Granularity_11_RFP	0.0083140810700031	0.0017553507473393	0.0020349194384395	-0.0152096139187058	0.0661476342826508	-0.0661476342826508	0.00285829203038	0.0496009624900629	2.543893401611021	actin
+Cytoplasm_Granularity_12_RFP	0.0170346819190725	3.793612946001446e-07	-0.0086265789374558	0.1743870117677087	0.1719627433359708	-0.1719627433359696	8.636048294720938e-07	0.0496009624900629	6.06368493765877	actin
+Cytoplasm_Granularity_13_RFP	0.0165343642920898	6.230874355361848e-07	-0.0013168894588963	0.0558714466217581	0.1466440481927371	-0.1466440481927365	1.3992350341537925e-06	0.0496009624900629	5.854109329494796	actin
+Cytoplasm_Granularity_14_RFP	0.0098501891702583	0.0004140812515598	0.0048833782463788	-0.0696969351968968	0.0221031201207769	-0.0221031201207772	0.0007182589532842	0.0496009624900629	3.143718951708868	actin
+Cytoplasm_Granularity_15_RFP	0.034125533803847	1.0436096431476471e-14	-0.0117844982540997	0.2411427339495568	0.2469237178768606	-0.2469237178768588	3.6060440348048167e-14	0.0496009624900629	13.442968974242577	actin
+Cytoplasm_Granularity_16_RFP	0.0053252103386409	0.0264527395149072	-0.0011101117449085	0.0381216351097182	0.0866696542894571	-0.0866696542894567	0.0365827648580458	0.0496009624900629	1.4367234744414588	actin
+Cytoplasm_Granularity_1_RFP	0.0797662447888054	1.1102230246251563e-16	0.0128883932375536	-0.2919905507118198	-0.3863669662163332	0.3863669662163309	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Granularity_2_RFP	0.0265761238729764	2.5021873462094387e-11	0.0055619175677227	-0.0646411039510882	0.0858438944240999	-0.0858438944241001	7.190691853834551e-11	0.0496009624900629	10.143229321877568	actin
+Cytoplasm_Granularity_3_RFP	0.0247453188850123	1.6195356167258979e-10	0.0058182685634424	-0.0713392817216137	0.0744939196170529	-0.0744939196170531	4.434624648629168e-10	0.0496009624900629	9.35314313342588	actin
+Cytoplasm_Granularity_4_RFP	0.0237508762052655	4.450164681202296e-10	0.0064129908206857	-0.0842132596546174	0.0591334779903649	-0.0591334779903652	1.1795984463186907e-09	0.0496009624900629	8.928265808140978	actin
+Cytoplasm_Granularity_5_RFP	0.0152669315254935	2.179214255049189e-06	0.0056152854233033	-0.0771713489077849	0.0376466898321505	-0.0376466898321508	4.676650185050108e-06	0.0496009624900629	5.330065114244632	actin
+Cytoplasm_Granularity_6_RFP	0.0135583503755253	1.163737040010826e-05	0.0050243723061764	-0.0672611482336932	0.041049393082168	-0.0410493930821682	2.378694900444664e-05	0.0496009624900629	4.623661258488416	actin
+Cytoplasm_Granularity_7_RFP	0.0068161411470623	0.006973486447476	0.0014019186773225	-0.0053777353069579	0.0665650786828842	-0.066565078682884	0.0106529181125259	0.0496009624900629	1.9725314113261163	actin
+Cytoplasm_Granularity_8_RFP	0.0091773954485366	0.0007819895285646	0.001378595879782	-0.00155702630656	0.0808152695319881	-0.0808152695319879	0.0013138782093539	0.0496009624900629	2.881444890070697	actin
+Cytoplasm_Granularity_9_RFP	0.0118764346599471	5.95448596415471e-05	0.0020014519906748	-0.009960290523731	0.0856360697971641	-0.085636069797164	0.0001120447034746	0.0496009624900629	3.950608668411528	actin
+Cytoplasm_Intensity_IntegratedIntensityEdge_RFP	0.0410414795005028	1.1102230246251563e-16	0.0024254421868306	-0.0942501363970267	-0.2344703644148723	0.2344703644148713	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Intensity_IntegratedIntensity_RFP	0.0783838235819537	1.1102230246251563e-16	-0.0059602684243162	0.0411874016199504	-0.2075817341685001	0.2075817341684998	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	actin
+Cytoplasm_Intensity_LowerQuartileIntensity_RFP	0.0117074913163893	7.008198051117187e-05	0.0080573271541586	-0.1388208934280313	-0.0615911889616559	0.061591188961655	0.0001310228331295	0.0496009624900629	3.882653013977188	actin
+Cytoplasm_Intensity_MADIntensity_RFP	0.0120110505928773	5.2287397473871344e-05	0.0063345186776424	-0.096996550023088	0.0015532221785253	-0.0015532221785258	9.951748929043382e-05	0.0496009624900629	4.002100589255595	actin
+Cytoplasm_Intensity_MassDisplacement_RFP	0.0068526086027598	0.0067459980964077	-0.0063225542477175	0.1111022845202778	0.0572621905582353	-0.0572621905582346	0.0103325907518857	0.0496009624900629	1.985790771589916	actin
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+Cytoplasm_Correlation_Overlap_DAPI_GFP	0.0052213862560118	0.028970805994453	-0.0027778261739751	0.0348727177146356	-0.0322191738170465	0.0322191738170466	0.0398992950884459	0.0496009624900629	1.3990347770426366	other
+Cytoplasm_Correlation_RWC_DAPI_GFP	0.0250899351958763	1.1402523369952178e-10	0.0102176355639956	-0.1638920903800971	-0.0280999111537201	0.0280999111537192	3.15198324583678e-10	0.0496009624900629	9.501416099640595	other
+Cells_Correlation_Correlation_DAPI_GFP	0.0120121827110191	5.223024593559167e-05	-0.0082808309727361	0.1441675777139306	0.0694567357186915	-0.0694567357186906	9.951748929043382e-05	0.0496009624900629	4.002100589255595	other
+Cells_Correlation_Costes_DAPI_GFP	0.1287306386618018	1.1102230246251563e-16	-0.0159824554903969	0.3647135469087196	0.489929153154612	-0.4899291531546089	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_Costes_GFP_DAPI	0.0725736544592836	1.1102230246251563e-16	-0.0026318368579147	0.1147914535206022	0.3059586759868545	-0.3059586759868531	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Cells_Correlation_K_DAPI_GFP	0.0136828962432701	1.0305116863840167e-05	-0.0069037054251493	0.1448310793092576	0.1593185470532011	-0.1593185470532	2.113823441505024e-05	0.0496009624900629	4.67493129024634	other
+Cells_Correlation_K_GFP_DAPI	0.0233561640861427	6.641966976417281e-10	0.0106113349978678	-0.2105839322869011	-0.195371542857158	0.1953715428571565	1.7525735590046503e-09	0.0496009624900629	8.756323744773926	other
+Cells_Correlation_Manders_DAPI_GFP	0.0293732964052595	1.4205303600078878e-12	0.0023528815075556	-0.0006521660881691	0.1461827762937873	-0.1461827762937869	4.457393913430156e-12	0.0496009624900629	11.35091898429622	other
+Cells_Correlation_Manders_GFP_DAPI	0.0288016347820732	2.556732603409273e-12	0.0054258488493823	-0.0601105034701542	0.095882513249416	-0.0958825132494161	7.894591895101505e-12	0.0496009624900629	11.102670315596974	other
+Cells_Correlation_RWC_DAPI_GFP	0.0360072716847414	1.4432899320127037e-15	-0.0131163880035569	0.2607626491605052	0.2434094004456848	-0.243409400445683	5.124341318246938e-15	0.0496009624900629	14.29036195080588	other
+Cells_Correlation_RWC_GFP_DAPI	0.0308150581209546	3.2163161023390785e-13	0.0046841958972369	-0.0440597428885552	0.1150220960993887	-0.1150220960993887	1.0430567024624778e-12	0.0496009624900629	11.98169208189254	other
+Nuclei_Correlation_Correlation_DAPI_GFP	0.0055975849365728	0.020811189718118	-0.0027688599181534	0.0668973607803623	0.1001597794611853	-0.1001597794611848	0.0293130133835243	0.0496009624900629	1.5329395337087124	other
+Nuclei_Correlation_Costes_DAPI_GFP	0.0917366799612873	1.1102230246251563e-16	-0.0148851988489012	0.3298836164767035	0.415994629305783	-0.4159946293057804	4.0280279112181464e-16	0.0496009624900629	15.394907528772336	other
+Nuclei_Correlation_K_DAPI_GFP	0.016042896725708	1.0133859220662345e-06	-0.0052536283477645	0.1225024387479125	0.1718156460245471	-0.171815646024546	2.2157082024838008e-06	0.0496009624900629	5.654487434555223	other
+Nuclei_Correlation_K_GFP_DAPI	0.0285387179234908	3.3494318429916348e-12	0.0073553503104055	-0.1690029733733402	-0.2302331058924449	0.2302331058924435	1.0314828566878748e-11	0.0496009624900629	10.986537985633367	other
+Nuclei_Correlation_Manders_DAPI_GFP	0.0053512008800226	0.0258562561936261	0.0030718253235459	-0.0405363457199373	0.0275093511301013	-0.0275093511301014	0.0359080304315789	0.0496009624900629	1.4448084154296303	other
+Nuclei_Correlation_Overlap_DAPI_GFP	0.0184591607692713	9.185241267584132e-08	-0.0044333294839917	0.0498850683396133	-0.0751730352919696	0.0751730352919697	2.176339818707179e-07	0.0496009624900629	6.6622732919394245	other
+Nuclei_Correlation_RWC_DAPI_GFP	0.0189891962562755	5.4080493305797006e-08	-0.0038782622974246	0.1027773274118242	0.1776477320738891	-0.1776477320738881	1.291922895638484e-07	0.0496009624900629	6.888763405070014	other
+Nuclei_Correlation_RWC_GFP_DAPI	0.0233397702291401	6.753302361772738e-10	-0.003969839060068	0.1083301098895058	0.1947086060182426	-0.1947086060182416	1.777910213609558e-09	0.0496009624900629	8.750090175153497	other
diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/figures/combined_plates_lm.png b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/figures/combined_plates_lm.png
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diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/figures/combined_plates_sig_lm.png b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/figures/combined_plates_sig_lm.png
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diff --git a/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/figures/feature_wt_contribution_and_significance_with_correlation.png b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/figures/feature_wt_contribution_and_significance_with_correlation.png
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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..bc56f3c
--- /dev/null
+++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/make_volcano.ipynb
@@ -0,0 +1,266 @@
+{
+ "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",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-16T22:25:16.860807Z",
+     "iopub.status.busy": "2023-06-16T22:25:16.860700Z",
+     "iopub.status.idle": "2023-06-16T22:25:16.868103Z",
+     "shell.execute_reply": "2023-06-16T22:25:16.867749Z"
+    },
+    "jukit_cell_id": "SyZ3qa8iz3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'°°°\\n## Imports\\n°°°'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %%--%%| <qGnYViiwRD|SyZ3qa8iz3>\n",
+    "r\"\"\"°°°\n",
+    "## Imports\n",
+    "°°°\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-16T22:25:16.897944Z",
+     "iopub.status.busy": "2023-06-16T22:25:16.897539Z",
+     "iopub.status.idle": "2023-06-16T22:25:17.391335Z",
+     "shell.execute_reply": "2023-06-16T22:25:17.391014Z"
+    },
+    "jukit_cell_id": "FKd1dIo45E"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "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": "2023-06-16T22:25:17.393166Z",
+     "iopub.status.busy": "2023-06-16T22:25:17.392844Z",
+     "iopub.status.idle": "2023-06-16T22:25:17.395502Z",
+     "shell.execute_reply": "2023-06-16T22:25:17.395240Z"
+    },
+    "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": "Akha4gM4Km"
+   },
+   "source": [
+    "## Defining Paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-16T22:25:17.396688Z",
+     "iopub.status.busy": "2023-06-16T22:25:17.396431Z",
+     "iopub.status.idle": "2023-06-16T22:25:17.398343Z",
+     "shell.execute_reply": "2023-06-16T22:25:17.398092Z"
+    },
+    "jukit_cell_id": "CbTwVL3qAB"
+   },
+   "outputs": [],
+   "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\n",
+    "\n",
+    "output_path = (\n",
+    "    output_path / \"feature_wt_contribution_and_significance_with_correlation.png\"\n",
+    ")"
+   ]
+  },
+  {
+   "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": "2023-06-16T22:25:17.399550Z",
+     "iopub.status.busy": "2023-06-16T22:25:17.399277Z",
+     "iopub.status.idle": "2023-06-16T22:25:17.404486Z",
+     "shell.execute_reply": "2023-06-16T22:25:17.404207Z"
+    },
+    "jukit_cell_id": "aYwhtTNAgx"
+   },
+   "outputs": [],
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "LKIM4ZTgdx"
+   },
+   "source": [
+    "## Plot the WT contribution for our features vs the Morphology Feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-16T22:25:17.405726Z",
+     "iopub.status.busy": "2023-06-16T22:25:17.405499Z",
+     "iopub.status.idle": "2023-06-16T22:25:17.860624Z",
+     "shell.execute_reply": "2023-06-16T22:25:17.860271Z"
+    },
+    "jukit_cell_id": "KxriUeQ8di"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set significance threshold and fold change threshold\n",
+    "significance_threshold = -np.log10(concatenated_df[\"critical_threshold\"].iloc[0])\n",
+    "colors = [\"#ca0020\", \"#f4a582\", \"#92c5de\", \"#0571b0\"]\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=\"neg_log_p\",\n",
+    "    size=\"r2_score\",\n",
+    "    hue=\"compartment\",\n",
+    "    palette=colors,\n",
+    ")\n",
+    "\n",
+    "# Add threshold lines\n",
+    "plt.axhline(\n",
+    "    significance_threshold,\n",
+    "    color=\"black\",\n",
+    "    linestyle=\"--\",\n",
+    "    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(output_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "python",
+   "language": "python",
+   "name": "python3"
+  },
+  "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
+}
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..80f33d1
--- /dev/null
+++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/make_volcano.py
@@ -0,0 +1,118 @@
+#!/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]:
+
+
+# %%--%%| <qGnYViiwRD|SyZ3qa8iz3>
+r"""°°°
+## Imports
+°°°"""
+
+
+# In[ ]:
+
+
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+# ## Finding the git root directory to reference paths on any system
+
+# 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.")
+
+
+# ## Defining Paths
+
+# In[ ]:
+
+
+output_path = Path("figures")
+
+output_path.mkdir(
+    parents=True, exist_ok=True
+)  # Create the parent directories if they don't exist
+
+output_path = (
+    output_path / "feature_wt_contribution_and_significance_with_correlation.png"
+)
+
+
+# ## 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(concatenated_df["critical_threshold"].iloc[0])
+colors = ["#ca0020", "#f4a582", "#92c5de", "#0571b0"]
+
+# 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="neg_log_p",
+    size="r2_score",
+    hue="compartment",
+    palette=colors,
+)
+
+# Add threshold lines
+plt.axhline(
+    significance_threshold,
+    color="black",
+    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)
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..860fae0
--- /dev/null
+++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/nbconverted/seperate_compartments.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Here the features are seperated according to compartments for later analysis
+
+# In[1]:
+
+
+# %%--%%| <qGnYViiwRD|SyZ3qa8iz3>
+r"""°°°
+## Imports
+°°°"""
+
+
+# In[ ]:
+
+
+import pandas as pd
+from pathlib import Path
+import itertools
+
+
+# ## Finding the git root directory to reference paths on any system
+
+# 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
+
+sig_output_path = output_path / "significant_feature_compartments.tsv"
+output_path = output_path / "feature_compartments.tsv"
+
+
+# ## 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
+#
+# 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[ ]:
+
+
+# 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 compartment 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, sep="\t", index=False)
+
+# Define the critical threshold
+critical_threshold = concatenated_df["critical_threshold"].iloc[0]
+
+# Create a dataframe with only significant models
+concatenated_df.loc[concatenated_df["corrected_p_value"] <= critical_threshold].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
new file mode 100644
index 0000000..ce93d61
--- /dev/null
+++ b/3.interpret_models/linear_reg_plate_1_2_norm_data/feature_importances/seperate_compartments.ipynb
@@ -0,0 +1,368 @@
+{
+ "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": "2023-06-16T22:25:18.759159Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.759051Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.764784Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.764461Z"
+    },
+    "jukit_cell_id": "SyZ3qa8iz3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'°°°\\n## Imports\\n°°°'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %%--%%| <qGnYViiwRD|SyZ3qa8iz3>\n",
+    "r\"\"\"°°°\n",
+    "## Imports\n",
+    "°°°\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-16T22:25:18.766292Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.766075Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.936627Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.936184Z"
+    },
+    "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": "2023-06-16T22:25:18.938290Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.937957Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.940645Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.940279Z"
+    },
+    "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": "2023-06-16T22:25:18.941916Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.941701Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.943850Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.943538Z"
+    },
+    "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": "2023-06-16T22:25:18.945264Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.944957Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.950039Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.949744Z"
+    },
+    "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": "2023-06-16T22:25:18.951352Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.951107Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.954874Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.954519Z"
+    },
+    "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": "2023-06-16T22:25:18.956286Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.956075Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.962119Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.961789Z"
+    },
+    "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": "2023-06-16T22:25:18.963518Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.963263Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.967690Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.967364Z"
+    },
+    "jukit_cell_id": "iTJOXFvhuT"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_31715/1993350327.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_31715/1993350327.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": "2023-06-16T22:25:18.969015Z",
+     "iopub.status.busy": "2023-06-16T22:25:18.968812Z",
+     "iopub.status.idle": "2023-06-16T22:25:18.984975Z",
+     "shell.execute_reply": "2023-06-16T22:25:18.984671Z"
+    },
+    "jukit_cell_id": "NUwlraBlOz"
+   },
+   "outputs": [],
+   "source": [
+    "concatenated_df.to_csv(output_path, sep=\"\\t\", index=False)\n",
+    "\n",
+    "# Define the critical threshold\n",
+    "critical_threshold = concatenated_df[\"critical_threshold\"].iloc[0]\n",
+    "\n",
+    "# Create a dataframe with only significant models\n",
+    "concatenated_df.loc[concatenated_df[\"corrected_p_value\"] <= critical_threshold].to_csv(\n",
+    "    sig_output_path, sep=\"\\t\", index=False\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "python",
+   "language": "python",
+   "name": "python3"
+  },
+  "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
+}
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