18337_project_CL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "using Pkg\n",
    "# Pkg.add(\"StatProfilerHTML\")\n",
    "# using Cthulhu\n",
    "# using Polyester\n",
    "# using StatProfilerHTML\n",
    "# Pkg.add(\"BenchmarkTools\")\n",
    "using CategoricalArrays, DataFrames, GLM, StableRNGs,LinearAlgebra, CSV, Distances, Plots, BenchmarkTools,Polyester #, PyPlot, CairoMakie#, RData\n",
    "using Base.Threads\n",
    "println(nthreads())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×9 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">Slide_name</th><th style = \"text-align: left;\">sample</th><th style = \"text-align: left;\">fov</th><th style = \"text-align: left;\">slide_fov</th><th style = \"text-align: left;\">cell_unique_id</th><th style = \"text-align: left;\">cell_type</th><th style = \"text-align: left;\">x_coord</th><th style = \"text-align: left;\">y_coord</th><th style = \"text-align: left;\">total</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String7\" style = \"text-align: left;\">String7</th><th title = \"String31\" style = \"text-align: left;\">String31</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"String31\" style = \"text-align: left;\">String31</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">B10</td><td style = \"text-align: left;\">PDAC_B10</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">B10_1</td><td style = \"text-align: left;\">B10_1_1</td><td style = \"text-align: left;\">cancer</td><td style = \"text-align: right;\">-33671.0</td><td style = \"text-align: right;\">-9834.29</td><td style = \"text-align: right;\">410</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">B10</td><td style = \"text-align: left;\">PDAC_B10</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">B10_1</td><td style = \"text-align: left;\">B10_1_10</td><td style = \"text-align: left;\">CAF</td><td style = \"text-align: right;\">-33948.4</td><td style = \"text-align: right;\">-9831.95</td><td style = \"text-align: right;\">58</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccc}\n",
       "\t& Slide\\_name & sample & fov & slide\\_fov & cell\\_unique\\_id & cell\\_type & x\\_coord & y\\_coord & \\\\\n",
       "\t\\hline\n",
       "\t& String7 & String31 & Int64 & String15 & String15 & String31 & Float64 & Float64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & B10 & PDAC\\_B10 & 1 & B10\\_1 & B10\\_1\\_1 & cancer & -33671.0 & -9834.29 & $\\dots$ \\\\\n",
       "\t2 & B10 & PDAC\\_B10 & 1 & B10\\_1 & B10\\_1\\_10 & CAF & -33948.4 & -9831.95 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×9 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m Slide_name \u001b[0m\u001b[1m sample   \u001b[0m\u001b[1m fov   \u001b[0m\u001b[1m slide_fov \u001b[0m\u001b[1m cell_unique_id \u001b[0m\u001b[1m cell_type \u001b[0m\u001b[1m x_co\u001b[0m ⋯\n",
       "     │\u001b[90m String7    \u001b[0m\u001b[90m String31 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15  \u001b[0m\u001b[90m String15       \u001b[0m\u001b[90m String31  \u001b[0m\u001b[90m Floa\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │ B10         PDAC_B10      1  B10_1      B10_1_1         cancer     -336 ⋯\n",
       "   2 │ B10         PDAC_B10      1  B10_1      B10_1_10        CAF        -339\n",
       "\u001b[36m                                                               3 columns omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in meta data  \n",
    "project_path = \"/Users/cl256/Desktop/PDAC_CosMx/\"\n",
    "data_path = project_path * \"data/\"\n",
    "meta = select!(DataFrame(CSV.File(data_path *\"meta/cell_meta.csv\")), Not(:Column1))\n",
    "select!(meta, Not([\"l11\", \"l12\",\"l1\",\"krt19\",\"hsat2\"]))\n",
    "first(meta,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in expression files\n",
    "expr_D10 = DataFrame(CSV.File(data_path * \"old/D10_R5042_S2/D10_R5042_S2_exprMat_file.csv\")) \n",
    "insertcols!(expr_D10, :Slide_name => \"D10\")\n",
    "expr_B10 = DataFrame(CSV.File(data_path * \"old/B10_R1171_S2/B10_R1171_S2_exprMat_file.csv\")) \n",
    "insertcols!(expr_B10, :Slide_name => \"B10\")\n",
    "expr_C4 = DataFrame(CSV.File(data_path * \"old/C4_R5042_S1/C4_R5042_S1_exprMat_file.csv\")) \n",
    "insertcols!(expr_C4, :Slide_name => \"C4\")\n",
    "expr_tma1 = DataFrame(CSV.File(data_path * \"Flat files/Run5573_TMA1/Run5573_TMA1_exprMat_file.csv\")) \n",
    "insertcols!(expr_tma1, :Slide_name => \"TMA1\")\n",
    "expr_tma28 = DataFrame(CSV.File(data_path * \"Flat files/Run5573_TMA28/Run5573_TMA28_exprMat_file.csv\")) \n",
    "insertcols!(expr_tma28, :Slide_name => \"TMA28\")\n",
    "expr_tma31 = DataFrame(CSV.File(data_path * \"Flat files/Run5573_TMA31/Run5573_TMA31_exprMat_file.csv\")) \n",
    "insertcols!(expr_tma31, :Slide_name => \"TMA31\")\n",
    "expr_tma32 = DataFrame(CSV.File(data_path * \"Flat files/Run5584_TMA32/Run5584_TMA32_exprMat_file.csv\")) \n",
    "insertcols!(expr_tma32, :Slide_name => \"TMA32\")\n",
    "# combine transcript files \n",
    "expr = DataFrame()\n",
    "expr = vcat(expr, expr_D10, expr_B10,  expr_C4, expr_tma1, expr_tma28, expr_tma31, expr_tma32, cols=:union)\n",
    "expr[!, \"cell_unique_id\"] = string.(expr[!, \"Slide_name\"], \"_\", expr[!, \"fov\"], \"_\", expr[!, \"cell_ID\"])\n",
    "# drop some columns\n",
    "select!(expr, Not([\"Slide_name\", \"fov\",\"cell_ID\"]))\n",
    "# clear memory\n",
    "expr_D10 = 0\n",
    "expr_B10 = 0\n",
    "expr_C4 = 0\n",
    "expr_tma1 = 0\n",
    "expr_tma28 = 0\n",
    "expr_tma31 = 0\n",
    "expr_tma32 = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>DataFrameRow (994 columns)</span></div><div style = \"float: right;\"><span style = \"font-style: italic;\">894 columns omitted</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowLabel\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">Slide_name</th><th style = \"text-align: left;\">sample</th><th style = \"text-align: left;\">fov</th><th style = \"text-align: left;\">slide_fov</th><th style = \"text-align: left;\">cell_unique_id</th><th style = \"text-align: left;\">cell_type</th><th style = \"text-align: left;\">x_coord</th><th style = \"text-align: left;\">y_coord</th><th style = \"text-align: left;\">total</th><th style = \"text-align: left;\">AATK</th><th style = \"text-align: left;\">ABL1</th><th style = \"text-align: left;\">ABL2</th><th style = \"text-align: left;\">ACE</th><th style = \"text-align: left;\">ACE2</th><th style = \"text-align: left;\">ACKR1</th><th style = \"text-align: left;\">ACKR3</th><th style = \"text-align: left;\">ACKR4</th><th style = \"text-align: left;\">ACTA2</th><th style = \"text-align: left;\">ACTG2</th><th style = \"text-align: left;\">ACVR1</th><th style = \"text-align: left;\">ACVR1B</th><th style = \"text-align: left;\">ACVR2A</th><th style = \"text-align: left;\">ACVRL1</th><th style = \"text-align: left;\">ADGRA2</th><th style = \"text-align: left;\">ADGRA3</th><th style = \"text-align: left;\">ADGRB2</th><th style = \"text-align: left;\">ADGRB3</th><th style = \"text-align: left;\">ADGRD1</th><th style = \"text-align: left;\">ADGRE1</th><th style = \"text-align: left;\">ADGRE2</th><th style = \"text-align: left;\">ADGRE5</th><th style = \"text-align: left;\">ADGRF1</th><th style = \"text-align: left;\">ADGRF3</th><th style = \"text-align: left;\">ADGRF4</th><th style = \"text-align: left;\">ADGRF5</th><th style = \"text-align: left;\">ADGRG1</th><th style = \"text-align: left;\">ADGRG2</th><th style = \"text-align: left;\">ADGRG3</th><th style = \"text-align: left;\">ADGRG5</th><th style = \"text-align: left;\">ADGRG6</th><th style = \"text-align: left;\">ADGRL1</th><th style = \"text-align: left;\">ADGRL2</th><th style = \"text-align: left;\">ADGRL4</th><th style = \"text-align: left;\">ADGRV1</th><th style = \"text-align: left;\">ADIPOQ</th><th style = \"text-align: left;\">ADIRF</th><th style = \"text-align: left;\">ADM2</th><th style = \"text-align: left;\">ADORA2A</th><th style = \"text-align: left;\">AGR2</th><th style = \"text-align: left;\">AHI1</th><th style = \"text-align: left;\">AHR</th><th style = \"text-align: left;\">AKT1</th><th style = \"text-align: left;\">ALCAM</th><th style = \"text-align: left;\">ANGPT1</th><th style = \"text-align: left;\">ANGPT2</th><th style = \"text-align: left;\">ANGPT4</th><th style = \"text-align: left;\">ANGPTL1</th><th style = \"text-align: left;\">ANXA1</th><th style = \"text-align: left;\">ANXA2</th><th style = \"text-align: left;\">ANXA4</th><th style = \"text-align: left;\">APOA1</th><th style = \"text-align: left;\">APOB</th><th style = \"text-align: left;\">APOD</th><th style = \"text-align: left;\">APP</th><th style = \"text-align: left;\">AQP3</th><th style = \"text-align: left;\">AR</th><th style = \"text-align: left;\">AREG</th><th style = \"text-align: left;\">ARF1</th><th style = \"text-align: left;\">ARG1</th><th style = \"text-align: left;\">ARHGDIB</th><th style = \"text-align: left;\">ARTN</th><th style = \"text-align: left;\">ATF3</th><th style = \"text-align: left;\">ATG10</th><th style = \"text-align: left;\">ATG12</th><th style = \"text-align: left;\">ATG5</th><th style = \"text-align: left;\">ATM</th><th style = \"text-align: left;\">ATR</th><th style = \"text-align: left;\">AXL</th><th style = \"text-align: left;\">AZGP1</th><th style = \"text-align: left;\">AZU1</th><th style = \"text-align: left;\">B2M</th><th style = \"text-align: left;\">B3GNT7</th><th style = \"text-align: left;\">BAG3</th><th style = \"text-align: left;\">BATF3</th><th style = \"text-align: left;\">BAX</th><th style = \"text-align: left;\">BCL2</th><th style = \"text-align: left;\">BCL2L1</th><th style = \"text-align: left;\">BECN1</th><th style = \"text-align: left;\">BEST1</th><th style = \"text-align: left;\">BGN</th><th style = \"text-align: left;\">BID</th><th style = \"text-align: left;\">BIRC5</th><th style = \"text-align: left;\">BMP1</th><th style = \"text-align: left;\">BMP2</th><th style = \"text-align: left;\">BMP3</th><th style = \"text-align: left;\">BMP4</th><th style = \"text-align: left;\">BMP5</th><th style = \"text-align: left;\">BMP6</th><th style = \"text-align: left;\">BMP7</th><th style = \"text-align: left;\">BMPR1A</th><th style = \"text-align: right;\">&ctdot;</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowLabel\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String7\" style = \"text-align: left;\">String7</th><th title = \"String31\" style = \"text-align: left;\">String31</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"String31\" style = \"text-align: left;\">String31</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: right;\">&ctdot;</th></tr></thead><tbody><tr><td class = \"rowLabel\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">D10</td><td style = \"text-align: left;\">PDAC_D10</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">D10_1</td><td style = \"text-align: left;\">D10_1_1</td><td style = \"text-align: left;\">cancer</td><td style = \"text-align: right;\">-26922.5</td><td style = \"text-align: right;\">-20822.4</td><td style = \"text-align: right;\">232</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: 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right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">&ctdot;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccc}\n",
       "\t& Slide\\_name & sample & fov & slide\\_fov & cell\\_unique\\_id & cell\\_type & x\\_coord & y\\_coord & \\\\\n",
       "\t\\hline\n",
       "\t& String7 & String31 & Int64 & String15 & String15 & String31 & Float64 & Float64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & D10 & PDAC\\_D10 & 1 & D10\\_1 & D10\\_1\\_1 & cancer & -26922.5 & -20822.4 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1mDataFrameRow\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m Slide_name \u001b[0m\u001b[1m sample   \u001b[0m\u001b[1m fov   \u001b[0m\u001b[1m slide_fov \u001b[0m\u001b[1m cell_unique_id \u001b[0m\u001b[1m cell_type \u001b[0m\u001b[1m x_co\u001b[0m ⋯\n",
       "     │\u001b[90m String7    \u001b[0m\u001b[90m String31 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15  \u001b[0m\u001b[90m String15       \u001b[0m\u001b[90m String31  \u001b[0m\u001b[90m Floa\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │ D10         PDAC_D10      1  D10_1      D10_1_1         cancer     -269 ⋯\n",
       "\u001b[36m                                                             988 columns omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine meta and expr\n",
    "meta_expr = innerjoin(meta, expr, on = :cell_unique_id)\n",
    "first(meta_expr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>155×2 DataFrame</span></div><div style = \"float: right;\"><span style = \"font-style: italic;\">130 rows omitted</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">slide_fov</th><th style = \"text-align: left;\">count</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">B10_1</td><td style = \"text-align: right;\">3810</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">B10_3</td><td style = \"text-align: right;\">5230</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">C4_15</td><td style = \"text-align: right;\">2851</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">C4_16</td><td style = \"text-align: right;\">2011</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: left;\">C4_17</td><td style = \"text-align: right;\">3526</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: left;\">C4_18</td><td style = \"text-align: right;\">3577</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: left;\">C4_19</td><td style = \"text-align: right;\">3912</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: left;\">C4_20</td><td style = \"text-align: right;\">3302</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: left;\">C4_21</td><td style = \"text-align: right;\">3023</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: left;\">C4_22</td><td style = \"text-align: right;\">3011</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: left;\">C4_23</td><td style = \"text-align: right;\">2943</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: left;\">C4_24</td><td style = \"text-align: right;\">3358</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">13</td><td style = \"text-align: left;\">D10_1</td><td style = \"text-align: right;\">3030</td></tr><tr><td style = \"text-align: right;\">&vellip;</td><td style = \"text-align: right;\">&vellip;</td><td style = \"text-align: right;\">&vellip;</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">144</td><td style = \"text-align: left;\">C4_3</td><td style = \"text-align: right;\">3323</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">145</td><td style = \"text-align: left;\">C4_4</td><td style = \"text-align: right;\">3548</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">146</td><td style = \"text-align: left;\">C4_5</td><td style = \"text-align: right;\">2400</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">147</td><td style = \"text-align: left;\">C4_6</td><td style = \"text-align: right;\">1064</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">148</td><td style = \"text-align: left;\">C4_7</td><td style = \"text-align: right;\">4306</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">149</td><td style = \"text-align: left;\">C4_8</td><td style = \"text-align: right;\">3986</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">150</td><td style = \"text-align: left;\">C4_9</td><td style = \"text-align: right;\">3170</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">151</td><td style = \"text-align: left;\">C4_10</td><td style = \"text-align: right;\">2624</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">152</td><td style = \"text-align: left;\">C4_11</td><td style = \"text-align: right;\">1251</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">153</td><td style = \"text-align: left;\">C4_12</td><td style = \"text-align: right;\">5265</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">154</td><td style = \"text-align: left;\">C4_13</td><td style = \"text-align: right;\">4762</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">155</td><td style = \"text-align: left;\">C4_14</td><td style = \"text-align: right;\">3203</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& slide\\_fov & count\\\\\n",
       "\t\\hline\n",
       "\t& String15 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & B10\\_1 & 3810 \\\\\n",
       "\t2 & B10\\_3 & 5230 \\\\\n",
       "\t3 & C4\\_15 & 2851 \\\\\n",
       "\t4 & C4\\_16 & 2011 \\\\\n",
       "\t5 & C4\\_17 & 3526 \\\\\n",
       "\t6 & C4\\_18 & 3577 \\\\\n",
       "\t7 & C4\\_19 & 3912 \\\\\n",
       "\t8 & C4\\_20 & 3302 \\\\\n",
       "\t9 & C4\\_21 & 3023 \\\\\n",
       "\t10 & C4\\_22 & 3011 \\\\\n",
       "\t11 & C4\\_23 & 2943 \\\\\n",
       "\t12 & C4\\_24 & 3358 \\\\\n",
       "\t13 & D10\\_1 & 3030 \\\\\n",
       "\t14 & D10\\_2 & 2989 \\\\\n",
       "\t15 & D10\\_3 & 1635 \\\\\n",
       "\t16 & D10\\_4 & 2337 \\\\\n",
       "\t17 & D10\\_5 & 2382 \\\\\n",
       "\t18 & D10\\_6 & 1698 \\\\\n",
       "\t19 & D10\\_7 & 2763 \\\\\n",
       "\t20 & B10\\_4 & 2449 \\\\\n",
       "\t21 & D10\\_8 & 2401 \\\\\n",
       "\t22 & D10\\_9 & 1417 \\\\\n",
       "\t23 & D10\\_10 & 1428 \\\\\n",
       "\t24 & D10\\_11 & 2753 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m155×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m slide_fov \u001b[0m\u001b[1m count \u001b[0m\n",
       "     │\u001b[90m String15  \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼──────────────────\n",
       "   1 │ B10_1       3810\n",
       "   2 │ B10_3       5230\n",
       "   3 │ C4_15       2851\n",
       "   4 │ C4_16       2011\n",
       "   5 │ C4_17       3526\n",
       "   6 │ C4_18       3577\n",
       "   7 │ C4_19       3912\n",
       "   8 │ C4_20       3302\n",
       "  ⋮  │     ⋮        ⋮\n",
       " 149 │ C4_8        3986\n",
       " 150 │ C4_9        3170\n",
       " 151 │ C4_10       2624\n",
       " 152 │ C4_11       1251\n",
       " 153 │ C4_12       5265\n",
       " 154 │ C4_13       4762\n",
       " 155 │ C4_14       3203\n",
       "\u001b[36m        140 rows omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count cells per slide\n",
    "combine(groupby(meta_expr, [:Slide_name]), nrow => :count)\n",
    "combine(groupby(meta_expr, [:slide_fov]), nrow => :count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×994 DataFrame</span></div><div style = \"float: right;\"><span style = \"font-style: italic;\">894 columns omitted</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">Slide_name</th><th style = \"text-align: left;\">sample</th><th style = \"text-align: left;\">fov</th><th style = \"text-align: left;\">slide_fov</th><th style = \"text-align: left;\">cell_unique_id</th><th style = \"text-align: left;\">cell_type</th><th style = \"text-align: left;\">x_coord</th><th style = \"text-align: left;\">y_coord</th><th style = \"text-align: left;\">total</th><th style = \"text-align: left;\">AATK</th><th style = \"text-align: left;\">ABL1</th><th style = \"text-align: left;\">ABL2</th><th style = \"text-align: left;\">ACE</th><th style = \"text-align: left;\">ACE2</th><th style = \"text-align: left;\">ACKR1</th><th style = \"text-align: left;\">ACKR3</th><th style = \"text-align: left;\">ACKR4</th><th style = \"text-align: left;\">ACTA2</th><th style = \"text-align: left;\">ACTG2</th><th style = \"text-align: left;\">ACVR1</th><th style = \"text-align: left;\">ACVR1B</th><th style = \"text-align: left;\">ACVR2A</th><th style = \"text-align: left;\">ACVRL1</th><th style = \"text-align: left;\">ADGRA2</th><th style = \"text-align: left;\">ADGRA3</th><th style = \"text-align: left;\">ADGRB2</th><th style = \"text-align: left;\">ADGRB3</th><th style = \"text-align: left;\">ADGRD1</th><th style = \"text-align: left;\">ADGRE1</th><th style = \"text-align: left;\">ADGRE2</th><th style = \"text-align: left;\">ADGRE5</th><th style = \"text-align: left;\">ADGRF1</th><th style = \"text-align: left;\">ADGRF3</th><th style = \"text-align: left;\">ADGRF4</th><th style = \"text-align: left;\">ADGRF5</th><th style = \"text-align: left;\">ADGRG1</th><th style = \"text-align: left;\">ADGRG2</th><th style = \"text-align: left;\">ADGRG3</th><th style = \"text-align: left;\">ADGRG5</th><th style = \"text-align: left;\">ADGRG6</th><th style = \"text-align: left;\">ADGRL1</th><th style = \"text-align: left;\">ADGRL2</th><th style = \"text-align: left;\">ADGRL4</th><th style = \"text-align: left;\">ADGRV1</th><th style = \"text-align: left;\">ADIPOQ</th><th style = \"text-align: left;\">ADIRF</th><th style = \"text-align: left;\">ADM2</th><th style = \"text-align: left;\">ADORA2A</th><th style = \"text-align: left;\">AGR2</th><th style = \"text-align: left;\">AHI1</th><th style = \"text-align: left;\">AHR</th><th style = \"text-align: left;\">AKT1</th><th style = \"text-align: left;\">ALCAM</th><th style = \"text-align: left;\">ANGPT1</th><th style = \"text-align: left;\">ANGPT2</th><th style = \"text-align: left;\">ANGPT4</th><th style = \"text-align: left;\">ANGPTL1</th><th style = \"text-align: left;\">ANXA1</th><th style = \"text-align: left;\">ANXA2</th><th style = \"text-align: left;\">ANXA4</th><th style = \"text-align: left;\">APOA1</th><th style = \"text-align: left;\">APOB</th><th style = \"text-align: left;\">APOD</th><th style = \"text-align: left;\">APP</th><th style = \"text-align: left;\">AQP3</th><th style = \"text-align: left;\">AR</th><th style = \"text-align: left;\">AREG</th><th style = \"text-align: left;\">ARF1</th><th style = \"text-align: left;\">ARG1</th><th style = \"text-align: left;\">ARHGDIB</th><th style = \"text-align: left;\">ARTN</th><th style = \"text-align: left;\">ATF3</th><th style = \"text-align: left;\">ATG10</th><th style = \"text-align: left;\">ATG12</th><th style = \"text-align: left;\">ATG5</th><th style = \"text-align: left;\">ATM</th><th style = \"text-align: left;\">ATR</th><th style = \"text-align: left;\">AXL</th><th style = \"text-align: left;\">AZGP1</th><th style = \"text-align: left;\">AZU1</th><th style = \"text-align: left;\">B2M</th><th style = \"text-align: left;\">B3GNT7</th><th style = \"text-align: left;\">BAG3</th><th style = \"text-align: left;\">BATF3</th><th style = \"text-align: left;\">BAX</th><th style = \"text-align: left;\">BCL2</th><th style = \"text-align: left;\">BCL2L1</th><th style = \"text-align: left;\">BECN1</th><th style = \"text-align: left;\">BEST1</th><th style = \"text-align: left;\">BGN</th><th style = \"text-align: left;\">BID</th><th style = \"text-align: left;\">BIRC5</th><th style = \"text-align: left;\">BMP1</th><th style = \"text-align: left;\">BMP2</th><th style = \"text-align: left;\">BMP3</th><th style = \"text-align: left;\">BMP4</th><th style = \"text-align: left;\">BMP5</th><th style = \"text-align: left;\">BMP6</th><th style = \"text-align: left;\">BMP7</th><th style = \"text-align: left;\">BMPR1A</th><th style = \"text-align: right;\">&ctdot;</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String7\" style = \"text-align: left;\">String7</th><th title = \"String31\" style = \"text-align: left;\">String31</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"String15\" style = \"text-align: left;\">String15</th><th title = \"String31\" style = \"text-align: left;\">String31</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: right;\">&ctdot;</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">TMA28</td><td style = \"text-align: left;\">PDAC_TMA28_fov9.10</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">TMA28_9</td><td style = \"text-align: left;\">TMA28_9_1</td><td style = \"text-align: left;\">cancer</td><td style = \"text-align: right;\">18302.0</td><td style = \"text-align: right;\">1667.84</td><td style = \"text-align: right;\">349</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">&ctdot;</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">TMA28</td><td style = \"text-align: left;\">PDAC_TMA28_fov9.10</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">TMA28_9</td><td style = \"text-align: left;\">TMA28_9_2</td><td style = \"text-align: left;\">cancer</td><td style = \"text-align: right;\">18299.0</td><td style = \"text-align: right;\">1666.58</td><td style = \"text-align: right;\">224</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">0</td><td style = \"text-align: right;\">&ctdot;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccc}\n",
       "\t& Slide\\_name & sample & fov & slide\\_fov & cell\\_unique\\_id & cell\\_type & x\\_coord & \\\\\n",
       "\t\\hline\n",
       "\t& String7 & String31 & Int64 & String15 & String15 & String31 & Float64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & TMA28 & PDAC\\_TMA28\\_fov9.10 & 9 & TMA28\\_9 & TMA28\\_9\\_1 & cancer & 18302.0 & $\\dots$ \\\\\n",
       "\t2 & TMA28 & PDAC\\_TMA28\\_fov9.10 & 9 & TMA28\\_9 & TMA28\\_9\\_2 & cancer & 18299.0 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×994 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m Slide_name \u001b[0m\u001b[1m sample             \u001b[0m\u001b[1m fov   \u001b[0m\u001b[1m slide_fov \u001b[0m\u001b[1m cell_unique_id \u001b[0m\u001b[1m cell_\u001b[0m ⋯\n",
       "     │\u001b[90m String7    \u001b[0m\u001b[90m String31           \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15  \u001b[0m\u001b[90m String15       \u001b[0m\u001b[90m Strin\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │ TMA28       PDAC_TMA28_fov9.10      9  TMA28_9    TMA28_9_1       cance ⋯\n",
       "   2 │ TMA28       PDAC_TMA28_fov9.10      9  TMA28_9    TMA28_9_2       cance\n",
       "\u001b[36m                                                             989 columns omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cancer cells:(1316, 994)\n",
      "caf cells:(690, 994)\n",
      "distance matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "690×1316 Matrix{Float64}:\n",
       " 762.084  758.936   103.542  278.314  …  578.869   586.56    591.83\n",
       " 162.697  163.182   771.447  586.745     877.747   879.077   871.609\n",
       " 159.598  159.486   746.108  562.338     849.421   850.821   843.435\n",
       " 176.36   174.142   638.002  458.053     747.509   749.447   742.715\n",
       " 590.114  586.84    346.726  301.431     355.474   360.847   359.6\n",
       " 319.692  319.521   838.105  665.016  …  820.521   820.439   811.509\n",
       " 399.8    399.885   913.945  744.277     849.813   848.977   839.38\n",
       " 399.359  399.153   889.067  722.118     814.707   813.858   804.252\n",
       " 404.66   404.468   893.925  727.239     816.435   815.535   805.888\n",
       " 682.746  679.539   428.969  411.575     241.882   247.153   245.836\n",
       "   ⋮                                  ⋱                        ⋮\n",
       " 624.176  623.342   980.493  841.488     731.391   728.272   717.189\n",
       " 831.179  828.206   635.593  624.495      96.6627   94.1613   83.6634\n",
       " 642.739  642.33   1048.71   902.5       816.329   813.181   802.082\n",
       " 878.994  875.944   627.869  636.975      29.071    27.8901   20.2378\n",
       " 805.365  802.452   646.134  622.866  …  137.78    134.984   124.202\n",
       " 751.194  748.446   677.165  627.757     230.423   227.4     216.421\n",
       " 658.969  658.664  1074.17   927.004     840.606   837.37    826.231\n",
       " 877.32   874.287   635.584  641.815      38.2685   35.1862   24.8798\n",
       " 706.617  704.12    727.796  653.649     331.044   327.796   316.683"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "radii = [10:10:30;]\n",
    "\n",
    "# for each fov \n",
    "# one test fov to start\n",
    "test_sample = subset(meta_expr, :slide_fov => ByRow(==(\"TMA28_9\")); skipmissing=true)\n",
    "display(first(test_sample,2))\n",
    "    # get all cancer cells, all CAF cells, all ECs, and all macrophages\n",
    "    canc = subset(test_sample, :cell_type => ByRow(==(\"cancer\")))\n",
    "    cafs = subset(test_sample, :cell_type => ByRow(==(\"CAF\")))\n",
    "    # display(size(canc) + size(cafs))\n",
    "    println(\"cancer cells:\", size(canc))\n",
    "    # display(first(canc,2))\n",
    "    println(\"caf cells:\", size(cafs))\n",
    "    # for each of the receptor cell types (cafs for now)\n",
    "    # calculate pairwise distances between each cancer cell and each receptor type\n",
    "    # dm_cafs = DataFrame(LinearAlgebra.Matrix([\n",
    "    println(\"distance matrix:\")\n",
    "    dm_cafs = LinearAlgebra.Matrix([\n",
    "        euclidean(cafs[i,[\"x_coord\",\"y_coord\"]],canc[j,[\"x_coord\",\"y_coord\"]])\n",
    "        for i in 1:size(cafs)[1], j in 1:size(canc)[1]\n",
    "    ])\n",
    "    # , :auto)    \n",
    "    # for each receptor cell, find cancer cells in different radii away\n",
    "    # for (r in 1:length(radii)) {\n",
    "    #     cancer_cells_within_radius = dm_cafs < r\n",
    "    # }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive_lm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  6.389 ms (124126 allocations: 9.51 MiB)\n",
      "number of threads: 4\n",
      "naive_lm_thread\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1.311 ms (56627 allocations: 8.53 MiB)\n",
      "beta_manual\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  4.581 ms (105226 allocations: 3.37 MiB)\n",
      "beta_manual_thread\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  715.333 μs (36227 allocations: 2.32 MiB)\n",
      "x_save\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  4.375 ms (105028 allocations: 2.30 MiB)\n",
      "x_save_thread\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  741.458 μs (36329 allocations: 1.26 MiB)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>100×2 DataFrame</span></div><div style = \"float: right;\"><span style = \"font-style: italic;\">75 rows omitted</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">gene</th><th style = \"text-align: left;\">beta</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Symbol\" style = \"text-align: left;\">Symbol</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">AATK</td><td style = \"text-align: right;\">0.006264</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">ABL1</td><td style = \"text-align: right;\">-0.002617</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">ABL2</td><td style = \"text-align: right;\">0.00708</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">ACE</td><td style = \"text-align: right;\">0.0006557</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: left;\">ACE2</td><td style = \"text-align: right;\">-0.0106</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: left;\">ACKR1</td><td style = \"text-align: right;\">0.003357</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: left;\">ACKR3</td><td style = \"text-align: right;\">0.0001644</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: left;\">ACKR4</td><td style = \"text-align: right;\">-0.000818</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: left;\">ACTA2</td><td style = \"text-align: right;\">0.02972</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: left;\">ACTG2</td><td style = \"text-align: right;\">-0.005196</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: left;\">ACVR1</td><td style = \"text-align: right;\">-0.00319</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: left;\">ACVR1B</td><td style = \"text-align: right;\">0.002008</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">13</td><td style = \"text-align: left;\">ACVR2A</td><td style = \"text-align: right;\">-0.000121</td></tr><tr><td style = \"text-align: right;\">&vellip;</td><td style = \"text-align: right;\">&vellip;</td><td style = \"text-align: right;\">&vellip;</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">89</td><td style = \"text-align: left;\">BMP6</td><td style = \"text-align: right;\">0.0007787</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">90</td><td style = \"text-align: left;\">BMP7</td><td style = \"text-align: right;\">0.002499</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">91</td><td style = \"text-align: left;\">BMPR1A</td><td style = \"text-align: right;\">-0.006218</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">92</td><td style = \"text-align: left;\">BMPR2</td><td style = \"text-align: right;\">-0.001262</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">93</td><td style = \"text-align: left;\">BMX</td><td style = \"text-align: right;\">-0.002619</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">94</td><td style = \"text-align: left;\">BRCA1</td><td style = \"text-align: right;\">-0.005074</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">95</td><td style = \"text-align: left;\">BST1</td><td style = \"text-align: right;\">-0.006546</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">96</td><td style = \"text-align: left;\">BST2</td><td style = \"text-align: right;\">-0.01682</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">97</td><td style = \"text-align: left;\">BTG1</td><td style = \"text-align: right;\">0.003403</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">98</td><td style = \"text-align: left;\">BTK</td><td style = \"text-align: right;\">-0.005524</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">99</td><td style = \"text-align: left;\">C11orf96</td><td style = \"text-align: right;\">-0.02933</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">100</td><td style = \"text-align: left;\">C1QA</td><td style = \"text-align: right;\">0.00397</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& gene & beta\\\\\n",
       "\t\\hline\n",
       "\t& Symbol & Float16\\\\\n",
       "\t\\hline\n",
       "\t1 & AATK & 0.006264 \\\\\n",
       "\t2 & ABL1 & -0.002617 \\\\\n",
       "\t3 & ABL2 & 0.00708 \\\\\n",
       "\t4 & ACE & 0.0006557 \\\\\n",
       "\t5 & ACE2 & -0.0106 \\\\\n",
       "\t6 & ACKR1 & 0.003357 \\\\\n",
       "\t7 & ACKR3 & 0.0001644 \\\\\n",
       "\t8 & ACKR4 & -0.000818 \\\\\n",
       "\t9 & ACTA2 & 0.02972 \\\\\n",
       "\t10 & ACTG2 & -0.005196 \\\\\n",
       "\t11 & ACVR1 & -0.00319 \\\\\n",
       "\t12 & ACVR1B & 0.002008 \\\\\n",
       "\t13 & ACVR2A & -0.000121 \\\\\n",
       "\t14 & ACVRL1 & 0.001229 \\\\\n",
       "\t15 & ADGRA2 & -0.01223 \\\\\n",
       "\t16 & ADGRA3 & -0.000817 \\\\\n",
       "\t17 & ADGRB2 & -0.005646 \\\\\n",
       "\t18 & ADGRB3 & 0.00393 \\\\\n",
       "\t19 & ADGRD1 & 0.00946 \\\\\n",
       "\t20 & ADGRE1 & -0.002537 \\\\\n",
       "\t21 & ADGRE2 & -0.00618 \\\\\n",
       "\t22 & ADGRE5 & -0.002169 \\\\\n",
       "\t23 & ADGRF1 & -0.00577 \\\\\n",
       "\t24 & ADGRF3 & -0.002823 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m100×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m gene     \u001b[0m\u001b[1m beta       \u001b[0m\n",
       "     │\u001b[90m Symbol   \u001b[0m\u001b[90m Float16    \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │ AATK       0.006264\n",
       "   2 │ ABL1      -0.002617\n",
       "   3 │ ABL2       0.00708\n",
       "   4 │ ACE        0.0006557\n",
       "   5 │ ACE2      -0.0106\n",
       "   6 │ ACKR1      0.003357\n",
       "   7 │ ACKR3      0.0001644\n",
       "   8 │ ACKR4     -0.000818\n",
       "  ⋮  │    ⋮          ⋮\n",
       "  94 │ BRCA1     -0.005074\n",
       "  95 │ BST1      -0.006546\n",
       "  96 │ BST2      -0.01682\n",
       "  97 │ BTG1       0.003403\n",
       "  98 │ BTK       -0.005524\n",
       "  99 │ C11orf96  -0.02933\n",
       " 100 │ C1QA       0.00397\n",
       "\u001b[36m             85 rows omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = 2\n",
    "cancer_cells_within_radius = dm_cafs .<= radii[r]\n",
    "n_cafs = nrow(DataFrame(dm_cafs, :auto))\n",
    "canc_indices = Any[]\n",
    "canc_cts = Int16[]\n",
    "i = 0\n",
    "for row in eachrow(dm_cafs)\n",
    "    i = i+1\n",
    "    if r == 1\n",
    "        canc_index = findall(.<=(radii[r]), row)\n",
    "        push!(canc_indices, canc_index)\n",
    "    else\n",
    "    # find all cancer cells in that radius distance ring\n",
    "        canc_index = findall((row.<=(radii[r])) .&  (row.>(radii[r-1])))\n",
    "        push!(canc_indices, canc_index)\n",
    "    end \n",
    "    # get number of cancer cells\n",
    "    canc_ct = length(canc_indices[i])\n",
    "    push!(canc_cts, canc_ct)\n",
    "end \n",
    "\n",
    "# naive implementation \n",
    "function naive_lm(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    # lm_res = DataFrame(gene = Symbol[], beta = Float16[])\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    for i in 1:num_genes # for every gene\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            # get gene expression in that CAF \n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        # display(caf_gene_exp_all)\n",
    "        df = DataFrame(x = canc_cts, y = caf_gene_exp_all)\n",
    "        model = lm(@formula(y ~ x), df)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = coef(model)[2]\n",
    "        # push!(lm_res, [gene, coef(model)[2]])\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "function naive_lm_thread(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    # Threads.@threads for gene in names(expr)[1:985] # for every gene\n",
    "    # Threads.@threads for i in 1:num_genes # for every gene\n",
    "    @batch per=thread for i in 1:num_genes # for every gene\n",
    "            # caf_gene_exp_all = Int16[]\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        df = DataFrame(x = canc_cts, y = caf_gene_exp_all)\n",
    "        model = lm(@formula(y ~ x), df)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = coef(model)[2]\n",
    "        # push!(lm_res, [names(expr)[i], coef(model)[2]])\n",
    "        # push!(lm_res, [gene, coef(model)[2],stderror(model)[2]])\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "# calculate beta by re-implementation\n",
    "function beta_manual(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    # for gene in names(expr)[1:985] # for every gene\n",
    "    for i in 1:num_genes # for every gene\n",
    "        x_mean = mean(canc_cts)\n",
    "        top_x = canc_cts .- x_mean\n",
    "        bottom = sum((canc_cts .- x_mean).^2)\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            # get gene expression in that CAF \n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        # beta = top/bottom\n",
    "        # push!(lm_res, [gene, beta])\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "# with threading\n",
    "function beta_manual_thread(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    @batch per=thread for i in 1:num_genes # for every gene\n",
    "        x_mean = mean(canc_cts)\n",
    "        top_x = canc_cts .- x_mean\n",
    "        bottom = sum((canc_cts .- x_mean).^2)\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "# beta calculation by re-implementation and save x-related terms \n",
    "function x_save(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    x_mean = mean(canc_cts)\n",
    "    # println(x_mean)\n",
    "    top_x = canc_cts .- x_mean\n",
    "    bottom = sum((canc_cts .- x_mean).^2)\n",
    "    for i in 1:num_genes # for every gene\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            # get gene expression in that CAF \n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        # beta = top/bottom\n",
    "        # push!(lm_res, [gene, beta])\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "function x_save_thread(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    x_mean = mean(canc_cts)\n",
    "    top_x = canc_cts .- x_mean\n",
    "    bottom = sum((canc_cts .- x_mean).^2)\n",
    "    @batch per=thread for i in 1:num_genes # for every gene\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "n_expr = Symbol.(names(expr)) # change strings of gene names to Symbol \n",
    "num_genes = 100\n",
    "println(\"naive_lm\")\n",
    "@btime naive_lm($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"number of threads: \",nthreads())\n",
    "println(\"naive_lm_thread\")\n",
    "@btime naive_lm_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"beta_manual\")\n",
    "@btime beta_manual($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"beta_manual_thread\")\n",
    "@btime beta_manual_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"x_save\")\n",
    "@btime x_save($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"x_save_thread\")\n",
    "@btime x_save_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @profilehtml naive_lm_thread(canc_cts,num_genes,expr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive_lm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  65.115 ms (1222887 allocations: 93.65 MiB)\n",
      "number of threads: 4\n",
      "naive_lm_thread\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12.332 ms (557539 allocations: 84.02 MiB)\n",
      "beta_manual\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  44.247 ms (1036248 allocations: 33.22 MiB)\n",
      "beta_manual_thread\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  6.659 ms (356599 allocations: 22.85 MiB)\n",
      "x_save\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  44.102 ms (1034280 allocations: 22.53 MiB)\n",
      "x_save_thread\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  7.468 ms (357586 allocations: 12.27 MiB)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>985×2 DataFrame</span></div><div style = \"float: right;\"><span style = \"font-style: italic;\">960 rows omitted</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">gene</th><th style = \"text-align: left;\">beta</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Symbol\" style = \"text-align: left;\">Symbol</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">AATK</td><td style = \"text-align: right;\">0.006264</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">ABL1</td><td style = \"text-align: right;\">-0.002617</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">ABL2</td><td style = \"text-align: right;\">0.00708</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">ACE</td><td style = \"text-align: right;\">0.0006557</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: left;\">ACE2</td><td style = \"text-align: right;\">-0.0106</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: left;\">ACKR1</td><td style = \"text-align: right;\">0.003357</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: left;\">ACKR3</td><td style = \"text-align: right;\">0.0001644</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: left;\">ACKR4</td><td style = \"text-align: right;\">-0.000818</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: left;\">ACTA2</td><td style = \"text-align: right;\">0.02972</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: left;\">ACTG2</td><td style = \"text-align: right;\">-0.005196</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: left;\">ACVR1</td><td style = \"text-align: right;\">-0.00319</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: left;\">ACVR1B</td><td style = \"text-align: right;\">0.002008</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">13</td><td style = \"text-align: left;\">ACVR2A</td><td style = \"text-align: right;\">-0.000121</td></tr><tr><td style = \"text-align: right;\">&vellip;</td><td style = \"text-align: right;\">&vellip;</td><td style = \"text-align: right;\">&vellip;</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">974</td><td style = \"text-align: left;\">NegPrb12</td><td style = \"text-align: right;\">-0.001842</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">975</td><td style = \"text-align: left;\">NegPrb13</td><td style = \"text-align: right;\">-0.0003266</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">976</td><td style = \"text-align: left;\">NegPrb14</td><td style = \"text-align: right;\">0.001023</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">977</td><td style = \"text-align: left;\">NegPrb15</td><td style = \"text-align: right;\">0.00221</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">978</td><td style = \"text-align: left;\">NegPrb16</td><td style = \"text-align: right;\">0.00266</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">979</td><td style = \"text-align: left;\">NegPrb18</td><td style = \"text-align: right;\">0.001637</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">980</td><td style = \"text-align: left;\">NegPrb19</td><td style = \"text-align: right;\">-0.001965</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">981</td><td style = \"text-align: left;\">NegPrb20</td><td style = \"text-align: right;\">-0.0045</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">982</td><td style = \"text-align: left;\">NegPrb21</td><td style = \"text-align: right;\">0.002947</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">983</td><td style = \"text-align: left;\">NegPrb22</td><td style = \"text-align: right;\">0.001188</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">984</td><td style = \"text-align: left;\">NegPrb23</td><td style = \"text-align: right;\">0.001597</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">985</td><td style = \"text-align: left;\">NegPrb17</td><td style = \"text-align: right;\">0.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& gene & beta\\\\\n",
       "\t\\hline\n",
       "\t& Symbol & Float16\\\\\n",
       "\t\\hline\n",
       "\t1 & AATK & 0.006264 \\\\\n",
       "\t2 & ABL1 & -0.002617 \\\\\n",
       "\t3 & ABL2 & 0.00708 \\\\\n",
       "\t4 & ACE & 0.0006557 \\\\\n",
       "\t5 & ACE2 & -0.0106 \\\\\n",
       "\t6 & ACKR1 & 0.003357 \\\\\n",
       "\t7 & ACKR3 & 0.0001644 \\\\\n",
       "\t8 & ACKR4 & -0.000818 \\\\\n",
       "\t9 & ACTA2 & 0.02972 \\\\\n",
       "\t10 & ACTG2 & -0.005196 \\\\\n",
       "\t11 & ACVR1 & -0.00319 \\\\\n",
       "\t12 & ACVR1B & 0.002008 \\\\\n",
       "\t13 & ACVR2A & -0.000121 \\\\\n",
       "\t14 & ACVRL1 & 0.001229 \\\\\n",
       "\t15 & ADGRA2 & -0.01223 \\\\\n",
       "\t16 & ADGRA3 & -0.000817 \\\\\n",
       "\t17 & ADGRB2 & -0.005646 \\\\\n",
       "\t18 & ADGRB3 & 0.00393 \\\\\n",
       "\t19 & ADGRD1 & 0.00946 \\\\\n",
       "\t20 & ADGRE1 & -0.002537 \\\\\n",
       "\t21 & ADGRE2 & -0.00618 \\\\\n",
       "\t22 & ADGRE5 & -0.002169 \\\\\n",
       "\t23 & ADGRF1 & -0.00577 \\\\\n",
       "\t24 & ADGRF3 & -0.002823 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m985×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m gene     \u001b[0m\u001b[1m beta       \u001b[0m\n",
       "     │\u001b[90m Symbol   \u001b[0m\u001b[90m Float16    \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │ AATK       0.006264\n",
       "   2 │ ABL1      -0.002617\n",
       "   3 │ ABL2       0.00708\n",
       "   4 │ ACE        0.0006557\n",
       "   5 │ ACE2      -0.0106\n",
       "   6 │ ACKR1      0.003357\n",
       "   7 │ ACKR3      0.0001644\n",
       "   8 │ ACKR4     -0.000818\n",
       "  ⋮  │    ⋮          ⋮\n",
       " 979 │ NegPrb18   0.001637\n",
       " 980 │ NegPrb19  -0.001965\n",
       " 981 │ NegPrb20  -0.0045\n",
       " 982 │ NegPrb21   0.002947\n",
       " 983 │ NegPrb22   0.001188\n",
       " 984 │ NegPrb23   0.001597\n",
       " 985 │ NegPrb17   0.0\n",
       "\u001b[36m            970 rows omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = 2\n",
    "cancer_cells_within_radius = dm_cafs .<= radii[r]\n",
    "n_cafs = nrow(DataFrame(dm_cafs, :auto))\n",
    "canc_indices = Any[]\n",
    "canc_cts = Int16[]\n",
    "i = 0\n",
    "for row in eachrow(dm_cafs)\n",
    "    i = i+1\n",
    "    if r == 1\n",
    "        canc_index = findall(.<=(radii[r]), row)\n",
    "        push!(canc_indices, canc_index)\n",
    "    else\n",
    "    # find all cancer cells in that radius distance ring\n",
    "        canc_index = findall((row.<=(radii[r])) .&  (row.>(radii[r-1])))\n",
    "        push!(canc_indices, canc_index)\n",
    "    end \n",
    "    # get number of cancer cells\n",
    "    canc_ct = length(canc_indices[i])\n",
    "    push!(canc_cts, canc_ct)\n",
    "end \n",
    "\n",
    "# naive implementation \n",
    "function naive_lm(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    # lm_res = DataFrame(gene = Symbol[], beta = Float16[])\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    for i in 1:num_genes # for every gene\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            # get gene expression in that CAF \n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        # display(caf_gene_exp_all)\n",
    "        df = DataFrame(x = canc_cts, y = caf_gene_exp_all)\n",
    "        model = lm(@formula(y ~ x), df)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = coef(model)[2]\n",
    "        # push!(lm_res, [gene, coef(model)[2]])\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "    return Nothing\n",
    "end \n",
    "\n",
    "function naive_lm_thread(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    # Threads.@threads for gene in names(expr)[1:985] # for every gene\n",
    "    # Threads.@threads for i in 1:num_genes # for every gene\n",
    "    @batch per=thread for i in 1:num_genes # for every gene\n",
    "            # caf_gene_exp_all = Int16[]\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        df = DataFrame(x = canc_cts, y = caf_gene_exp_all)\n",
    "        model = lm(@formula(y ~ x), df)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = coef(model)[2]\n",
    "        # push!(lm_res, [names(expr)[i], coef(model)[2]])\n",
    "        # push!(lm_res, [gene, coef(model)[2],stderror(model)[2]])\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "# calculate beta by re-implementation\n",
    "function beta_manual(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    # for gene in names(expr)[1:985] # for every gene\n",
    "    for i in 1:num_genes # for every gene\n",
    "        x_mean = mean(canc_cts)\n",
    "        top_x = canc_cts .- x_mean\n",
    "        bottom = sum((canc_cts .- x_mean).^2)\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            # get gene expression in that CAF \n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        # beta = top/bottom\n",
    "        # push!(lm_res, [gene, beta])\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "# with threading\n",
    "function beta_manual_thread(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    @batch per=thread for i in 1:num_genes # for every gene\n",
    "        x_mean = mean(canc_cts)\n",
    "        top_x = canc_cts .- x_mean\n",
    "        bottom = sum((canc_cts .- x_mean).^2)\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "# beta calculation by re-implementation and save x-related terms \n",
    "function x_save(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    x_mean = mean(canc_cts)\n",
    "    # println(x_mean)\n",
    "    top_x = canc_cts .- x_mean\n",
    "    bottom = sum((canc_cts .- x_mean).^2)\n",
    "    for i in 1:num_genes # for every gene\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            # get gene expression in that CAF \n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            # push!(caf_gene_exp_all, caf_gene_exp)\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        # beta = top/bottom\n",
    "        # push!(lm_res, [gene, beta])\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "function x_save_thread(n_cafs,cafs,canc_cts,n_expr,num_genes)\n",
    "    gene = Vector{Symbol}(undef, num_genes)\n",
    "    beta = Vector{Float16}(undef, num_genes)\n",
    "    x_mean = mean(canc_cts)\n",
    "    top_x = canc_cts .- x_mean\n",
    "    bottom = sum((canc_cts .- x_mean).^2)\n",
    "    @batch per=thread for i in 1:num_genes # for every gene\n",
    "        caf_gene_exp_all = Vector{Int16}(undef,n_cafs)\n",
    "        for caf_index in 1:n_cafs # for every CAF\n",
    "            caf_gene_exp = cafs[caf_index,n_expr[i]]\n",
    "            caf_gene_exp_all[caf_index] = caf_gene_exp\n",
    "        end \n",
    "        y = caf_gene_exp_all\n",
    "        y_mean = mean(y)\n",
    "        top_y = y .- y_mean\n",
    "        top = sum(transpose(top_x) * top_y)\n",
    "        gene[i] = n_expr[i]\n",
    "        beta[i] = top/bottom\n",
    "    end \n",
    "    lm_res=DataFrame(;gene,beta)\n",
    "end \n",
    "\n",
    "n_expr = Symbol.(names(expr)) # change strings of gene names to Symbol \n",
    "num_genes = 985\n",
    "println(\"naive_lm\")\n",
    "@btime naive_lm($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"number of threads: \",nthreads())\n",
    "println(\"naive_lm_thread\")\n",
    "@btime naive_lm_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"beta_manual\")\n",
    "@btime beta_manual($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"beta_manual_thread\")\n",
    "@btime beta_manual_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"x_save\")\n",
    "flush(stdout)\n",
    "@btime x_save($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "println(\"x_save_thread\")\n",
    "@btime x_save_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "too few cells\n",
      "4\n",
      "too few cells\n",
      "5\n",
      "too few cells\n",
      "6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7×8 DataFrame\u001b[0m\n",
      "\u001b[1m Row \u001b[0m│\u001b[1m fov   \u001b[0m\u001b[1m n_caf \u001b[0m\u001b[1m naive_lm \u001b[0m\u001b[1m naive_lm_thread \u001b[0m\u001b[1m beta_manual \u001b[0m\u001b[1m beta_manual_thread \u001b[0m\u001b[1m x_save   \u001b[0m\u001b[1m x_save_thread \u001b[0m\n",
      "     │\u001b[90m Int16 \u001b[0m\u001b[90m Int16 \u001b[0m\u001b[90m Float16  \u001b[0m\u001b[90m Float16         \u001b[0m\u001b[90m Float16     \u001b[0m\u001b[90m Float16            \u001b[0m\u001b[90m Float16  \u001b[0m\u001b[90m Float16       \u001b[0m\n",
      "─────┼───────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "   1 │     2    608  0.005665        0.001137      0.003815           0.000576   0.003742      0.0006056\n",
      "   2 │     6    436  0.004227        0.000914      0.00246            0.000402   0.002419      0.0004187\n",
      "   3 │     7    790  0.007103        0.001378      0.005184           0.00079    0.005077      0.0008216\n",
      "   4 │     8    539  0.00522         0.001042      0.00337            0.000498   0.00332       0.000521\n",
      "   5 │     9    690  0.006355        0.001244      0.004475           0.0006733  0.004383      0.0007076\n",
      "   6 │    10    642  0.006004        0.001183      0.004105           0.000617   0.004044      0.0006466\n",
      "   7 │    11    735  0.006702        0.0013075     0.004795           0.000724   0.0047        0.0007606"
     ]
    }
   ],
   "source": [
    "test_slide = subset(meta_expr, :Slide_name => ByRow(==(\"TMA28\")); skipmissing=true)\n",
    "n_expr = Symbol.(names(expr)) # change strings of gene names to Symbol \n",
    "num_genes = 100\n",
    "\n",
    "# for each fov\n",
    "time_per_fov = DataFrame(fov = Int16[], n_caf = Int16[], naive_lm = Float16[], naive_lm_thread = Float16[], beta_manual = Float16[], beta_manual_thread = Float16[], x_save = Float16[], x_save_thread = Float16[])\n",
    "# print(time_per_fov)\n",
    "for FOV_here in unique(test_slide[:,\"fov\"])[1:10]\n",
    "    println(FOV_here)\n",
    "    fov_expr = subset(test_slide, :fov => ByRow(==(FOV_here)))\n",
    "    # get all cancer cells and all CAF cells\n",
    "    canc = subset(fov_expr, :cell_type => ByRow(==(\"cancer\")))\n",
    "    cafs = subset(fov_expr, :cell_type => ByRow(==(\"CAF\")))\n",
    "    if nrow(canc) < 100 || nrow(cafs) < 100\n",
    "        println(\"too few cells\")\n",
    "    else\n",
    "        n_cafs = nrow(cafs)\n",
    "        dm_cafs = LinearAlgebra.Matrix([\n",
    "            euclidean(cafs[i,[\"x_coord\",\"y_coord\"]],canc[j,[\"x_coord\",\"y_coord\"]])\n",
    "            for i in 1:size(cafs)[1], j in 1:size(canc)[1]\n",
    "        ])\n",
    "        r = 2\n",
    "        cancer_cells_within_radius = dm_cafs .<= radii[r]\n",
    "        canc_indices = Any[]\n",
    "        canc_cts = Int16[]\n",
    "        i = 0\n",
    "        for row in eachrow(dm_cafs)\n",
    "            i = i+1\n",
    "            if r == 1\n",
    "                canc_index = findall(.<=(radii[r]), row)\n",
    "                push!(canc_indices, canc_index)\n",
    "            else\n",
    "            # find all cancer cells in that radius distance ring\n",
    "                canc_index = findall((row.<=(radii[r])) .&  (row.>(radii[r-1])))\n",
    "                push!(canc_indices, canc_index)\n",
    "            end \n",
    "            # get number of cancer cells\n",
    "            canc_ct = length(canc_indices[i])\n",
    "            push!(canc_cts, canc_ct)\n",
    "        end \n",
    "        naive_lm_time = @belapsed naive_lm($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        naive_lm_thread_time = @belapsed naive_lm_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        beta_manual_time = @belapsed beta_manual($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        beta_manual_thread_time = @belapsed beta_manual_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        x_save_time = @belapsed x_save($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        x_save_thread_time = @belapsed x_save_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "\n",
    "        push!(time_per_fov, [Int(FOV_here), nrow(cafs), naive_lm_time, naive_lm_thread_time, beta_manual_time, beta_manual_thread_time, x_save_time, x_save_thread_time]) \n",
    "    end \n",
    "end \n",
    "print(time_per_fov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>7×8 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">fov</th><th style = \"text-align: left;\">n_caf</th><th style = \"text-align: left;\">naive_lm</th><th style = \"text-align: left;\">naive_lm_thread</th><th style = \"text-align: left;\">beta_manual</th><th style = \"text-align: left;\">beta_manual_thread</th><th style = \"text-align: left;\">x_save</th><th style = \"text-align: left;\">x_save_thread</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int16\" style = \"text-align: left;\">Int16</th><th title = \"Int16\" style = \"text-align: left;\">Int16</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th><th title = \"Float16\" style = \"text-align: left;\">Float16</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">608</td><td style = \"text-align: right;\">0.005833</td><td style = \"text-align: right;\">0.001128</td><td style = \"text-align: right;\">0.003807</td><td style = \"text-align: right;\">0.000571</td><td style = \"text-align: right;\">0.003902</td><td style = \"text-align: right;\">0.000627</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">436</td><td style = \"text-align: right;\">0.004265</td><td style = \"text-align: right;\">0.0009055</td><td style = \"text-align: right;\">0.00246</td><td style = \"text-align: right;\">0.0003977</td><td style = \"text-align: right;\">0.002405</td><td style = \"text-align: right;\">0.0004342</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">790</td><td style = \"text-align: right;\">0.00717</td><td style = \"text-align: right;\">0.001367</td><td style = \"text-align: right;\">0.00517</td><td style = \"text-align: right;\">0.0007915</td><td style = \"text-align: right;\">0.00508</td><td style = \"text-align: right;\">0.0008554</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">539</td><td style = \"text-align: right;\">0.005245</td><td style = \"text-align: right;\">0.001035</td><td style = \"text-align: right;\">0.0034</td><td style = \"text-align: right;\">0.000491</td><td style = \"text-align: right;\">0.003292</td><td style = \"text-align: right;\">0.0005207</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">690</td><td style = \"text-align: right;\">0.00643</td><td style = \"text-align: right;\">0.001238</td><td style = \"text-align: right;\">0.00444</td><td style = \"text-align: right;\">0.0006685</td><td style = \"text-align: right;\">0.004383</td><td style = \"text-align: right;\">0.000712</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">642</td><td style = \"text-align: right;\">0.0061</td><td style = \"text-align: right;\">0.001174</td><td style = \"text-align: right;\">0.00411</td><td style = \"text-align: right;\">0.00061</td><td style = \"text-align: right;\">0.004078</td><td style = \"text-align: right;\">0.000646</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">735</td><td style = \"text-align: right;\">0.006744</td><td style = \"text-align: right;\">0.001284</td><td style = \"text-align: right;\">0.004787</td><td style = \"text-align: right;\">0.00072</td><td style = \"text-align: right;\">0.004677</td><td style = \"text-align: right;\">0.0007625</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccc}\n",
       "\t& fov & n\\_caf & naive\\_lm & naive\\_lm\\_thread & beta\\_manual & beta\\_manual\\_thread & x\\_save & \\\\\n",
       "\t\\hline\n",
       "\t& Int16 & Int16 & Float16 & Float16 & Float16 & Float16 & Float16 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 2 & 608 & 0.005833 & 0.001128 & 0.003807 & 0.000571 & 0.003902 & $\\dots$ \\\\\n",
       "\t2 & 6 & 436 & 0.004265 & 0.0009055 & 0.00246 & 0.0003977 & 0.002405 & $\\dots$ \\\\\n",
       "\t3 & 7 & 790 & 0.00717 & 0.001367 & 0.00517 & 0.0007915 & 0.00508 & $\\dots$ \\\\\n",
       "\t4 & 8 & 539 & 0.005245 & 0.001035 & 0.0034 & 0.000491 & 0.003292 & $\\dots$ \\\\\n",
       "\t5 & 9 & 690 & 0.00643 & 0.001238 & 0.00444 & 0.0006685 & 0.004383 & $\\dots$ \\\\\n",
       "\t6 & 10 & 642 & 0.0061 & 0.001174 & 0.00411 & 0.00061 & 0.004078 & $\\dots$ \\\\\n",
       "\t7 & 11 & 735 & 0.006744 & 0.001284 & 0.004787 & 0.00072 & 0.004677 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m7×8 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m fov   \u001b[0m\u001b[1m n_caf \u001b[0m\u001b[1m naive_lm \u001b[0m\u001b[1m naive_lm_thread \u001b[0m\u001b[1m beta_manual \u001b[0m\u001b[1m beta_manual_threa\u001b[0m ⋯\n",
       "     │\u001b[90m Int16 \u001b[0m\u001b[90m Int16 \u001b[0m\u001b[90m Float16  \u001b[0m\u001b[90m Float16         \u001b[0m\u001b[90m Float16     \u001b[0m\u001b[90m Float16          \u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │     2    608  0.005833        0.001128      0.003807           0.000571 ⋯\n",
       "   2 │     6    436  0.004265        0.0009055     0.00246            0.000397\n",
       "   3 │     7    790  0.00717         0.001367      0.00517            0.000791\n",
       "   4 │     8    539  0.005245        0.001035      0.0034             0.000491\n",
       "   5 │     9    690  0.00643         0.001238      0.00444            0.000668 ⋯\n",
       "   6 │    10    642  0.0061          0.001174      0.00411            0.00061\n",
       "   7 │    11    735  0.006744        0.001284      0.004787           0.00072\n",
       "\u001b[36m                                                               3 columns omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# manually enter results from r\n",
    "# time_per_fov[!,:r_lm] = [0.236186981201172,0.231831073760986,0.237574815750122,0.255863904953003,0.271380186080933, #2,6,7,8,9\n",
    "                        # 0.356481075286865,0.241353034973145,0.232091903686523,0.2480309009552,0.244919061660767, #10,11,12,13,14\n",
    "                        # 0.363600969314575, 0.237097024917603, 0.224981069564819,0.237607955932617,0.358481884002686, #15,18,19,20,21 \n",
    "                        # 0.251603126525879, 0.221576929092407, 0.221559047698975# 22,24,25\n",
    "                        # ]\n",
    "display(time_per_fov)\n",
    "\n",
    "# plot(time_per_fov[!,\"n_caf\"], [time_per_fov[!,\"r_lm\"] time_per_fov[!,\"naive_lm\"] time_per_fov[!,\"beta_manual\"] time_per_fov[!,\"x_save\"]], title=\"TMA28\", seriestype=:scatter,label=[\"r_lm\" \"naive_lm\" \"beta_manual\" \"x_save\"],xaxis = (\"Number of CAFs in FOV\"),yaxis=(\"Time (seconds)\"))\n",
    "plot(time_per_fov[!,\"n_caf\"], [time_per_fov[!,\"naive_lm\"] time_per_fov[!,\"naive_lm_thread\"] time_per_fov[!,\"beta_manual\"] time_per_fov[!,\"beta_manual_thread\"] time_per_fov[!,\"x_save\"] time_per_fov[!,\"x_save_thread\"]], title=\"TMA28\", seriestype=:scatter,label=[\"naive_lm\" \"naive_lm_thread\" \"beta_manual\" \"beta_manual_thread\" \"x_save\" \"x_save_thread\"],xaxis = (\"Number of CAFs in FOV\"),yaxis=(\"Time (seconds)\"))\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "too few cells\n",
      "4\n",
      "too few cells\n",
      "5\n",
      "too few cells\n",
      "6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7×8 DataFrame\u001b[0m\n",
      "\u001b[1m Row \u001b[0m│\u001b[1m fov   \u001b[0m\u001b[1m n_caf \u001b[0m\u001b[1m naive_lm \u001b[0m\u001b[1m naive_lm_thread \u001b[0m\u001b[1m beta_manual \u001b[0m\u001b[1m beta_manual_thread \u001b[0m\u001b[1m x_save  \u001b[0m\u001b[1m x_save_thread \u001b[0m\n",
      "     │\u001b[90m Int16 \u001b[0m\u001b[90m Int16 \u001b[0m\u001b[90m Float16  \u001b[0m\u001b[90m Float16         \u001b[0m\u001b[90m Float16     \u001b[0m\u001b[90m Float16            \u001b[0m\u001b[90m Float16 \u001b[0m\u001b[90m Float16       \u001b[0m\n",
      "─────┼──────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "   1 │     2    608   0.0584           0.01169      0.04102            0.00589   0.03818       0.006023\n",
      "   2 │     6    436   0.04218          0.00934      0.0261             0.00405   0.0243        0.004158\n",
      "   3 │     7    790   0.0708           0.01411      0.05444            0.00793   0.0504        0.00821\n",
      "   4 │     8    539   0.0525           0.0107       0.03546            0.005005  0.0334        0.00519\n",
      "   5 │     9    690   0.0636           0.01282      0.04654            0.006775  0.0438        0.007042\n",
      "   6 │    10    642   0.05972          0.01211      0.0428             0.006336  0.0406        0.006435\n",
      "   7 │    11    735   0.0666           0.0134       0.0496             0.00739   0.0465        0.00758"
     ]
    }
   ],
   "source": [
    "test_slide = subset(meta_expr, :Slide_name => ByRow(==(\"TMA28\")); skipmissing=true)\n",
    "n_expr = Symbol.(names(expr)) # change strings of gene names to Symbol \n",
    "num_genes = 985\n",
    "\n",
    "# for each fov\n",
    "time_per_fov = DataFrame(fov = Int16[], n_caf = Int16[], naive_lm = Float16[], naive_lm_thread = Float16[], beta_manual = Float16[], beta_manual_thread = Float16[], x_save = Float16[], x_save_thread = Float16[])\n",
    "# print(time_per_fov)\n",
    "for FOV_here in unique(test_slide[:,\"fov\"])[1:10]\n",
    "    println(FOV_here)\n",
    "    fov_expr = subset(test_slide, :fov => ByRow(==(FOV_here)))\n",
    "    # get all cancer cells and all CAF cells\n",
    "    canc = subset(fov_expr, :cell_type => ByRow(==(\"cancer\")))\n",
    "    cafs = subset(fov_expr, :cell_type => ByRow(==(\"CAF\")))\n",
    "    if nrow(canc) < 100 || nrow(cafs) < 100\n",
    "        println(\"too few cells\")\n",
    "    else\n",
    "        n_cafs = nrow(cafs)\n",
    "        dm_cafs = LinearAlgebra.Matrix([\n",
    "            euclidean(cafs[i,[\"x_coord\",\"y_coord\"]],canc[j,[\"x_coord\",\"y_coord\"]])\n",
    "            for i in 1:size(cafs)[1], j in 1:size(canc)[1]\n",
    "        ])\n",
    "        r = 2\n",
    "        cancer_cells_within_radius = dm_cafs .<= radii[r]\n",
    "        canc_indices = Any[]\n",
    "        canc_cts = Int16[]\n",
    "        i = 0\n",
    "        for row in eachrow(dm_cafs)\n",
    "            i = i+1\n",
    "            if r == 1\n",
    "                canc_index = findall(.<=(radii[r]), row)\n",
    "                push!(canc_indices, canc_index)\n",
    "            else\n",
    "            # find all cancer cells in that radius distance ring\n",
    "                canc_index = findall((row.<=(radii[r])) .&  (row.>(radii[r-1])))\n",
    "                push!(canc_indices, canc_index)\n",
    "            end \n",
    "            # get number of cancer cells\n",
    "            canc_ct = length(canc_indices[i])\n",
    "            push!(canc_cts, canc_ct)\n",
    "        end \n",
    "        naive_lm_time = @belapsed naive_lm($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        naive_lm_thread_time = @belapsed naive_lm_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        beta_manual_time = @belapsed beta_manual($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        beta_manual_thread_time = @belapsed beta_manual_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        x_save_time = @belapsed x_save($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "        x_save_thread_time = @belapsed x_save_thread($n_cafs,$cafs,$canc_cts,$n_expr,$num_genes)\n",
    "\n",
    "        push!(time_per_fov, [Int(FOV_here), nrow(cafs), naive_lm_time, naive_lm_thread_time, beta_manual_time, beta_manual_thread_time, x_save_time, x_save_thread_time]) \n",
    "    end \n",
    "end \n",
    "print(time_per_fov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   ],
   "source": [
    "plot(time_per_fov[!,\"n_caf\"], [time_per_fov[!,\"naive_lm\"] time_per_fov[!,\"naive_lm_thread\"] time_per_fov[!,\"beta_manual\"] time_per_fov[!,\"beta_manual_thread\"] time_per_fov[!,\"x_save\"] time_per_fov[!,\"x_save_thread\"]], title=\"TMA28\", seriestype=:scatter,label=[\"naive_lm\" \"naive_lm_thread\" \"beta_manual\" \"beta_manual_thread\" \"x_save\" \"x_save_thread\"],xaxis = (\"Number of CAFs in FOV\"),yaxis=(\"Time (seconds)\"))\n"
   ]
  }
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