diff --git a/.gitignore b/.gitignore
index 6676234..ef9a788 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,22 +1,8 @@
-# ignore all plates as they are downloaded from figshare in instructions
-0.download_data/Plate_1
-0.download_data/Plate_2
-0.download_data/Plate_3
-0.download_data/Plate_3_prime
-0.download_data/Plate_4
-
 # ignore pycache
 __pycache__
 
-# ignore corrected plates if they are put into a separate folder
-Corrected_Images
-
-# ignore corrected plates images (like in download data)
-1.cellprofiler_ic/Corrected_Plate_1
-1.cellprofiler_ic/Corrected_Plate_2
-1.cellprofiler_ic/Corrected_Plate_3
-1.cellprofiler_ic/Corrected_Plate_3_prime
-1.cellprofiler_ic/Corrected_Plate_4
+# ignore VSCode files
+.vscode
 
 # ignore log files
 *logs
@@ -27,3 +13,21 @@ Corrected_Images
 
 # ignore jupyter files
 .ipynb_checkpoints/
+
+# ignore zip files (large)
+*.zip
+
+# ignore all plates as they are downloaded from figshare in instructions
+0.download_data/Plate_1
+0.download_data/Plate_2
+0.download_data/Plate_3
+0.download_data/Plate_3_prime
+0.download_data/Plate_4
+
+# ignore corrected plates images as they are generated in IC notebook
+Corrected_Images/
+Corrected_Plate_1
+Corrected_Plate_2
+Corrected_Plate_3
+Corrected_Plate_3_prime
+Corrected_Plate_4
diff --git a/4.analyze_data/README.md b/4.analyze_data/README.md
new file mode 100644
index 0000000..85ae9a8
--- /dev/null
+++ b/4.analyze_data/README.md
@@ -0,0 +1,21 @@
+# Analyze NF1 data
+
+In this module, we perform analysis on the data to uncover answers to our goals as specified in [the main README](../README.md).
+
+
+The analyses we perform are:
+
+1. Linear modeling per feature in each plate. The beta coefficients for WT treatment contribution and cell count contribution are plotted as a scatter plot.
+
+# R visualization environment
+
+To visualize results, we use R to create plots. 
+This means that we have a separate environment from the main Python environment specifically in this module that has an R kernel.
+To install this environment, perform the following steps:
+
+```bash
+# Make sure you are in the 4.analyze_data module to access the env file
+cd 4.analyze_data
+# Run this command in terminal to create the conda environment
+conda env create -f r_analysis_env.yml
+```
diff --git a/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb b/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb
new file mode 100644
index 0000000..bcef923
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb
@@ -0,0 +1,652 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Perform linear model per CellProfiler feature\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import libraries\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "from pycytominer.cyto_utils import infer_cp_features\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up paths and variables\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define inputs and outputs\n",
+    "data_dir = pathlib.Path(\"../3.processing_features/data/single_cell_profiles/\")\n",
+    "cp_file = pathlib.Path(data_dir, \"Plate_4_sc_normalized.parquet\")\n",
+    "\n",
+    "output_dir = pathlib.Path(\"./results\")\n",
+    "output_dir.mkdir(exist_ok=True)\n",
+    "output_cp_file = pathlib.Path(output_dir, \"linear_model_cp_features_plate4.tsv\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read in normalized data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We are testing 2304 CellProfiler features\n",
+      "(7502, 2321)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_WellRow</th>\n",
+       "      <th>Metadata_WellCol</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>Metadata_Site</th>\n",
+       "      <th>Metadata_number_of_singlecells</th>\n",
+       "      <th>Metadata_gene_name</th>\n",
+       "      <th>Metadata_genotype</th>\n",
+       "      <th>Metadata_seed_density</th>\n",
+       "      <th>Metadata_siRNA</th>\n",
+       "      <th>Metadata_RNAiMax</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_Variance_DAPI_3_02_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_DAPI_3_03_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_GFP_3_00_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_GFP_3_01_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_GFP_3_02_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_GFP_3_03_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_RFP_3_00_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_RFP_3_01_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_RFP_3_02_256</th>\n",
+       "      <th>Nuclei_Texture_Variance_RFP_3_03_256</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>115</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.015567</td>\n",
+       "      <td>-0.991013</td>\n",
+       "      <td>-0.427085</td>\n",
+       "      <td>-0.492375</td>\n",
+       "      <td>-0.505686</td>\n",
+       "      <td>-0.492483</td>\n",
+       "      <td>3.287944</td>\n",
+       "      <td>3.324396</td>\n",
+       "      <td>3.234285</td>\n",
+       "      <td>3.337852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>115</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.254595</td>\n",
+       "      <td>1.181604</td>\n",
+       "      <td>-0.467826</td>\n",
+       "      <td>-0.486035</td>\n",
+       "      <td>-0.481063</td>\n",
+       "      <td>-0.457716</td>\n",
+       "      <td>-0.661907</td>\n",
+       "      <td>-0.668559</td>\n",
+       "      <td>-0.634783</td>\n",
+       "      <td>-0.644279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>115</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.840056</td>\n",
+       "      <td>-0.882460</td>\n",
+       "      <td>-0.052123</td>\n",
+       "      <td>-0.097227</td>\n",
+       "      <td>-0.132127</td>\n",
+       "      <td>-0.097907</td>\n",
+       "      <td>0.252911</td>\n",
+       "      <td>0.251474</td>\n",
+       "      <td>0.337910</td>\n",
+       "      <td>0.296604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>115</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.856420</td>\n",
+       "      <td>1.740204</td>\n",
+       "      <td>0.198178</td>\n",
+       "      <td>0.191218</td>\n",
+       "      <td>0.173410</td>\n",
+       "      <td>0.210927</td>\n",
+       "      <td>-0.340967</td>\n",
+       "      <td>-0.328878</td>\n",
+       "      <td>-0.173260</td>\n",
+       "      <td>-0.293306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>115</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.399975</td>\n",
+       "      <td>-1.412046</td>\n",
+       "      <td>-0.597744</td>\n",
+       "      <td>-0.601125</td>\n",
+       "      <td>-0.628829</td>\n",
+       "      <td>-0.621264</td>\n",
+       "      <td>-0.491339</td>\n",
+       "      <td>-0.487596</td>\n",
+       "      <td>-0.440303</td>\n",
+       "      <td>-0.472316</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 2321 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_WellRow  Metadata_WellCol Metadata_Well Metadata_Site   \n",
+       "0                B                 2            B2            11  \\\n",
+       "1                B                 2            B2            11   \n",
+       "2                B                 2            B2            11   \n",
+       "3                B                 2            B2            11   \n",
+       "4                B                 2            B2            14   \n",
+       "\n",
+       "   Metadata_number_of_singlecells Metadata_gene_name Metadata_genotype   \n",
+       "0                             115                NF1                WT  \\\n",
+       "1                             115                NF1                WT   \n",
+       "2                             115                NF1                WT   \n",
+       "3                             115                NF1                WT   \n",
+       "4                             115                NF1                WT   \n",
+       "\n",
+       "   Metadata_seed_density Metadata_siRNA  Metadata_RNAiMax  ...   \n",
+       "0                   1000           None                 0  ...  \\\n",
+       "1                   1000           None                 0  ...   \n",
+       "2                   1000           None                 0  ...   \n",
+       "3                   1000           None                 0  ...   \n",
+       "4                   1000           None                 0  ...   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_DAPI_3_02_256   \n",
+       "0                              -1.015567  \\\n",
+       "1                               1.254595   \n",
+       "2                              -0.840056   \n",
+       "3                               1.856420   \n",
+       "4                              -1.399975   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_DAPI_3_03_256 Nuclei_Texture_Variance_GFP_3_00_256   \n",
+       "0                              -0.991013                            -0.427085  \\\n",
+       "1                               1.181604                            -0.467826   \n",
+       "2                              -0.882460                            -0.052123   \n",
+       "3                               1.740204                             0.198178   \n",
+       "4                              -1.412046                            -0.597744   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_GFP_3_01_256  Nuclei_Texture_Variance_GFP_3_02_256   \n",
+       "0                             -0.492375                             -0.505686  \\\n",
+       "1                             -0.486035                             -0.481063   \n",
+       "2                             -0.097227                             -0.132127   \n",
+       "3                              0.191218                              0.173410   \n",
+       "4                             -0.601125                             -0.628829   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_GFP_3_03_256  Nuclei_Texture_Variance_RFP_3_00_256   \n",
+       "0                             -0.492483                              3.287944  \\\n",
+       "1                             -0.457716                             -0.661907   \n",
+       "2                             -0.097907                              0.252911   \n",
+       "3                              0.210927                             -0.340967   \n",
+       "4                             -0.621264                             -0.491339   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_RFP_3_01_256  Nuclei_Texture_Variance_RFP_3_02_256   \n",
+       "0                              3.324396                              3.234285  \\\n",
+       "1                             -0.668559                             -0.634783   \n",
+       "2                              0.251474                              0.337910   \n",
+       "3                             -0.328878                             -0.173260   \n",
+       "4                             -0.487596                             -0.440303   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_RFP_3_03_256  \n",
+       "0                              3.337852  \n",
+       "1                             -0.644279  \n",
+       "2                              0.296604  \n",
+       "3                             -0.293306  \n",
+       "4                             -0.472316  \n",
+       "\n",
+       "[5 rows x 2321 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "cp_df = pd.read_parquet(cp_file)\n",
+    "\n",
+    "# Remove rows where Metadata_genotype is \"HET\" to avoid using in the LM\n",
+    "cp_df = cp_df[cp_df[\"Metadata_genotype\"] != \"HET\"]\n",
+    "\n",
+    "# Define CellProfiler features\n",
+    "cp_features = infer_cp_features(cp_df)\n",
+    "\n",
+    "print(f\"We are testing {len(cp_features)} CellProfiler features\")\n",
+    "print(cp_df.shape)\n",
+    "cp_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up the dummy matrix between null and WT cell types\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7502, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_number_of_singlecells</th>\n",
+       "      <th>Null</th>\n",
+       "      <th>WT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>115</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>115</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>115</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>115</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>115</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Metadata_number_of_singlecells   Null    WT\n",
+       "0                             115  False  True\n",
+       "1                             115  False  True\n",
+       "2                             115  False  True\n",
+       "3                             115  False  True\n",
+       "4                             115  False  True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Setup linear modeling framework\n",
+    "variables = [\"Metadata_number_of_singlecells\"]\n",
+    "X = cp_df.loc[:, variables]\n",
+    "\n",
+    "# Add dummy matrix of categorical genotypes, excluding \"HET\"\n",
+    "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype)\n",
+    "\n",
+    "X = pd.concat([X, genotype_x], axis=1)\n",
+    "\n",
+    "print(X.shape)\n",
+    "X.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perform linear modeling per feature\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2304, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>r2_score</th>\n",
+       "      <th>cell_count_coef</th>\n",
+       "      <th>Null_coef</th>\n",
+       "      <th>WT_coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cytoplasm_AreaShape_Area</td>\n",
+       "      <td>0.001685</td>\n",
+       "      <td>-0.000522</td>\n",
+       "      <td>0.055565</td>\n",
+       "      <td>-0.055565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cytoplasm_AreaShape_BoundingBoxArea</td>\n",
+       "      <td>0.000344</td>\n",
+       "      <td>-0.000431</td>\n",
+       "      <td>0.019286</td>\n",
+       "      <td>-0.019286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cytoplasm_AreaShape_BoundingBoxMaximum_X</td>\n",
+       "      <td>0.000344</td>\n",
+       "      <td>0.000728</td>\n",
+       "      <td>0.018554</td>\n",
+       "      <td>-0.018554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Cytoplasm_AreaShape_BoundingBoxMaximum_Y</td>\n",
+       "      <td>0.000152</td>\n",
+       "      <td>-0.000232</td>\n",
+       "      <td>-0.020108</td>\n",
+       "      <td>0.020108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Cytoplasm_AreaShape_BoundingBoxMinimum_X</td>\n",
+       "      <td>0.000517</td>\n",
+       "      <td>0.000942</td>\n",
+       "      <td>0.015849</td>\n",
+       "      <td>-0.015849</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    feature  r2_score  cell_count_coef   \n",
+       "0                  Cytoplasm_AreaShape_Area  0.001685        -0.000522  \\\n",
+       "1       Cytoplasm_AreaShape_BoundingBoxArea  0.000344        -0.000431   \n",
+       "2  Cytoplasm_AreaShape_BoundingBoxMaximum_X  0.000344         0.000728   \n",
+       "3  Cytoplasm_AreaShape_BoundingBoxMaximum_Y  0.000152        -0.000232   \n",
+       "4  Cytoplasm_AreaShape_BoundingBoxMinimum_X  0.000517         0.000942   \n",
+       "\n",
+       "   Null_coef   WT_coef  \n",
+       "0   0.055565 -0.055565  \n",
+       "1   0.019286 -0.019286  \n",
+       "2   0.018554 -0.018554  \n",
+       "3  -0.020108  0.020108  \n",
+       "4   0.015849 -0.015849  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit linear model for each feature\n",
+    "lm_results = []\n",
+    "for cp_feature in cp_features:\n",
+    "    # Subset CP data to each individual feature (univariate test)\n",
+    "    cp_subset_df = cp_df.loc[:, cp_feature]\n",
+    "\n",
+    "    # Fit linear model\n",
+    "    lm = LinearRegression(fit_intercept=True)\n",
+    "    lm_result = lm.fit(X=X, y=cp_subset_df)\n",
+    "\n",
+    "    # Extract Beta coefficients\n",
+    "    # (contribution of feature to X covariates)\n",
+    "    coef = lm_result.coef_\n",
+    "\n",
+    "    # Estimate fit (R^2)\n",
+    "    r2_score = lm.score(X=X, y=cp_subset_df)\n",
+    "\n",
+    "    # Add results to a growing list\n",
+    "    lm_results.append([cp_feature, r2_score] + list(coef))\n",
+    "\n",
+    "# Convert results to a pandas DataFrame\n",
+    "lm_results = pd.DataFrame(\n",
+    "    lm_results,\n",
+    "    columns=[\"feature\", \"r2_score\", \"cell_count_coef\", \"Null_coef\", \"WT_coef\"],\n",
+    ")\n",
+    "\n",
+    "# Output file\n",
+    "lm_results.to_csv(output_cp_file, sep=\"\\t\", index=False)\n",
+    "\n",
+    "print(lm_results.shape)\n",
+    "lm_results.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check to make sure the processing worked\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7f08f2790>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Small exploration visualization\n",
+    "lm_results.plot(x=\"cell_count_coef\", y=\"WT_coef\", kind=\"scatter\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nf1_cellpainting_data",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/4.analyze_data/notebooks/linear_model/1.visualize_lm.ipynb b/4.analyze_data/notebooks/linear_model/1.visualize_lm.ipynb
new file mode 100644
index 0000000..a9fa680
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/1.visualize_lm.ipynb
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualize the linear model coefficients as scatter plots\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import libraries\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“package ‘ggplot2’ was built under R version 4.2.3”\n"
+     ]
+    }
+   ],
+   "source": [
+    "suppressPackageStartupMessages(library(ggplot2))\n",
+    "suppressPackageStartupMessages(library(dplyr))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set paths and load in TSV\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 1731    5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 6 × 5</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>feature</th><th scope=col>r2_score</th><th scope=col>cell_count_coef</th><th scope=col>Null_coef</th><th scope=col>WT_coef</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Cytoplasm_AreaShape_Area                </td><td>0.389572936</td><td>-0.017550700</td><td>-0.336014311</td><td> 0.336014311</td></tr>\n",
+       "\t<tr><td>Cytoplasm_AreaShape_BoundingBoxArea     </td><td>0.377129989</td><td>-0.020594709</td><td>-0.243760036</td><td> 0.243760036</td></tr>\n",
+       "\t<tr><td>Cytoplasm_AreaShape_BoundingBoxMaximum_X</td><td>0.001273655</td><td>-0.000678252</td><td>-0.026804703</td><td> 0.026804703</td></tr>\n",
+       "\t<tr><td>Cytoplasm_AreaShape_BoundingBoxMaximum_Y</td><td>0.005930619</td><td> 0.000715508</td><td>-0.096230103</td><td> 0.096230103</td></tr>\n",
+       "\t<tr><td>Cytoplasm_AreaShape_BoundingBoxMinimum_X</td><td>0.008106605</td><td> 0.003740672</td><td> 0.014582643</td><td>-0.014582643</td></tr>\n",
+       "\t<tr><td>Cytoplasm_AreaShape_BoundingBoxMinimum_Y</td><td>0.026489120</td><td> 0.007645777</td><td>-0.003374704</td><td> 0.003374704</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A tibble: 6 × 5\n",
+       "\\begin{tabular}{lllll}\n",
+       " feature & r2\\_score & cell\\_count\\_coef & Null\\_coef & WT\\_coef\\\\\n",
+       " <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t Cytoplasm\\_AreaShape\\_Area                 & 0.389572936 & -0.017550700 & -0.336014311 &  0.336014311\\\\\n",
+       "\t Cytoplasm\\_AreaShape\\_BoundingBoxArea      & 0.377129989 & -0.020594709 & -0.243760036 &  0.243760036\\\\\n",
+       "\t Cytoplasm\\_AreaShape\\_BoundingBoxMaximum\\_X & 0.001273655 & -0.000678252 & -0.026804703 &  0.026804703\\\\\n",
+       "\t Cytoplasm\\_AreaShape\\_BoundingBoxMaximum\\_Y & 0.005930619 &  0.000715508 & -0.096230103 &  0.096230103\\\\\n",
+       "\t Cytoplasm\\_AreaShape\\_BoundingBoxMinimum\\_X & 0.008106605 &  0.003740672 &  0.014582643 & -0.014582643\\\\\n",
+       "\t Cytoplasm\\_AreaShape\\_BoundingBoxMinimum\\_Y & 0.026489120 &  0.007645777 & -0.003374704 &  0.003374704\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 6 × 5\n",
+       "\n",
+       "| feature &lt;chr&gt; | r2_score &lt;dbl&gt; | cell_count_coef &lt;dbl&gt; | Null_coef &lt;dbl&gt; | WT_coef &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| Cytoplasm_AreaShape_Area                 | 0.389572936 | -0.017550700 | -0.336014311 |  0.336014311 |\n",
+       "| Cytoplasm_AreaShape_BoundingBoxArea      | 0.377129989 | -0.020594709 | -0.243760036 |  0.243760036 |\n",
+       "| Cytoplasm_AreaShape_BoundingBoxMaximum_X | 0.001273655 | -0.000678252 | -0.026804703 |  0.026804703 |\n",
+       "| Cytoplasm_AreaShape_BoundingBoxMaximum_Y | 0.005930619 |  0.000715508 | -0.096230103 |  0.096230103 |\n",
+       "| Cytoplasm_AreaShape_BoundingBoxMinimum_X | 0.008106605 |  0.003740672 |  0.014582643 | -0.014582643 |\n",
+       "| Cytoplasm_AreaShape_BoundingBoxMinimum_Y | 0.026489120 |  0.007645777 | -0.003374704 |  0.003374704 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  feature                                  r2_score    cell_count_coef\n",
+       "1 Cytoplasm_AreaShape_Area                 0.389572936 -0.017550700   \n",
+       "2 Cytoplasm_AreaShape_BoundingBoxArea      0.377129989 -0.020594709   \n",
+       "3 Cytoplasm_AreaShape_BoundingBoxMaximum_X 0.001273655 -0.000678252   \n",
+       "4 Cytoplasm_AreaShape_BoundingBoxMaximum_Y 0.005930619  0.000715508   \n",
+       "5 Cytoplasm_AreaShape_BoundingBoxMinimum_X 0.008106605  0.003740672   \n",
+       "6 Cytoplasm_AreaShape_BoundingBoxMinimum_Y 0.026489120  0.007645777   \n",
+       "  Null_coef    WT_coef     \n",
+       "1 -0.336014311  0.336014311\n",
+       "2 -0.243760036  0.243760036\n",
+       "3 -0.026804703  0.026804703\n",
+       "4 -0.096230103  0.096230103\n",
+       "5  0.014582643 -0.014582643\n",
+       "6 -0.003374704  0.003374704"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# path to tsv with LM coefficients\n",
+    "lm_results_dir <- file.path(\"./results\")\n",
+    "lm_file <- file.path(lm_results_dir, \"./linear_model_cp_features_plate1.tsv\")\n",
+    "\n",
+    "# save path for figure\n",
+    "lm_fig_dir <- file.path(\"./figures\")\n",
+    "lm_fig <- file.path(lm_fig_dir, \"linear_model_cp_features_plate1.png\")\n",
+    "\n",
+    "# Load and process linear model data\n",
+    "lm_df <- readr::read_tsv(\n",
+    "    lm_file,\n",
+    "    col_types = readr::cols(.default = \"d\", feature = \"c\")\n",
+    ")\n",
+    "\n",
+    "print(dim(lm_df))\n",
+    "head(lm_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up the data frame for visualization\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mExpected 6 pieces. Additional pieces discarded in 468 rows [1, 2, 3, 4, 5, 6,\n",
+      "7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, ...].”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mExpected 6 pieces. Missing pieces filled with `NA` in 723 rows [34, 35, 47, 51,\n",
+      "63, 66, 68, 72, 73, 74, 76, 79, 81, 83, 88, 89, 94, 99, 100, 105, ...].”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 1731   12\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 10 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>feature</th><th scope=col>compartment</th><th scope=col>feature_group</th><th scope=col>measurement</th><th scope=col>channel</th><th scope=col>parameter1</th><th scope=col>parameter2</th><th scope=col>r2_score</th><th scope=col>cell_count_coef</th><th scope=col>Null_coef</th><th scope=col>WT_coef</th><th scope=col>channel_cleaned</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Cells_Texture_SumEntropy_GFP_3_02_256                </td><td>Cells    </td><td>Texture           </td><td>SumEntropy         </td><td>GFP</td><td>3</td><td>02</td><td>0.2074251</td><td>-0.007895167</td><td> 0.6145610</td><td>-0.6145610</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_SumEntropy_GFP_3_03_256                </td><td>Cells    </td><td>Texture           </td><td>SumEntropy         </td><td>GFP</td><td>3</td><td>03</td><td>0.2036442</td><td>-0.008115417</td><td> 0.6121762</td><td>-0.6121762</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_SumEntropy_GFP_3_00_256                </td><td>Cells    </td><td>Texture           </td><td>SumEntropy         </td><td>GFP</td><td>3</td><td>00</td><td>0.2053192</td><td>-0.007834732</td><td> 0.6112067</td><td>-0.6112067</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_SumEntropy_GFP_3_01_256                </td><td>Cells    </td><td>Texture           </td><td>SumEntropy         </td><td>GFP</td><td>3</td><td>01</td><td>0.2025519</td><td>-0.008142424</td><td> 0.6110672</td><td>-0.6110672</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_AngularSecondMoment_GFP_3_02_256       </td><td>Cells    </td><td>Texture           </td><td>AngularSecondMoment</td><td>GFP</td><td>3</td><td>02</td><td>0.2601552</td><td> 0.001782800</td><td>-0.5947110</td><td> 0.5947110</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_AngularSecondMoment_GFP_3_00_256       </td><td>Cells    </td><td>Texture           </td><td>AngularSecondMoment</td><td>GFP</td><td>3</td><td>00</td><td>0.2536100</td><td> 0.002067132</td><td>-0.5918340</td><td> 0.5918340</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cytoplasm_Texture_AngularSecondMoment_GFP_3_02_256   </td><td>Cytoplasm</td><td>Texture           </td><td>AngularSecondMoment</td><td>GFP</td><td>3</td><td>02</td><td>0.2641807</td><td> 0.001286384</td><td>-0.5914474</td><td> 0.5914474</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_AngularSecondMoment_GFP_3_01_256       </td><td>Cells    </td><td>Texture           </td><td>AngularSecondMoment</td><td>GFP</td><td>3</td><td>01</td><td>0.2531856</td><td> 0.002016927</td><td>-0.5906073</td><td> 0.5906073</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cells_Texture_AngularSecondMoment_GFP_3_03_256       </td><td>Cells    </td><td>Texture           </td><td>AngularSecondMoment</td><td>GFP</td><td>3</td><td>03</td><td>0.2602418</td><td> 0.001495697</td><td>-0.5904066</td><td> 0.5904066</td><td>ER   </td></tr>\n",
+       "\t<tr><td>Cytoplasm_RadialDistribution_ZernikeMagnitude_RFP_1_1</td><td>Cytoplasm</td><td>RadialDistribution</td><td>ZernikeMagnitude   </td><td>RFP</td><td>1</td><td>1 </td><td>0.3264574</td><td>-0.036032933</td><td> 0.5902856</td><td>-0.5902856</td><td>actin</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A tibble: 10 × 12\n",
+       "\\begin{tabular}{llllllllllll}\n",
+       " feature & compartment & feature\\_group & measurement & channel & parameter1 & parameter2 & r2\\_score & cell\\_count\\_coef & Null\\_coef & WT\\_coef & channel\\_cleaned\\\\\n",
+       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t Cells\\_Texture\\_SumEntropy\\_GFP\\_3\\_02\\_256                 & Cells     & Texture            & SumEntropy          & GFP & 3 & 02 & 0.2074251 & -0.007895167 &  0.6145610 & -0.6145610 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_SumEntropy\\_GFP\\_3\\_03\\_256                 & Cells     & Texture            & SumEntropy          & GFP & 3 & 03 & 0.2036442 & -0.008115417 &  0.6121762 & -0.6121762 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_SumEntropy\\_GFP\\_3\\_00\\_256                 & Cells     & Texture            & SumEntropy          & GFP & 3 & 00 & 0.2053192 & -0.007834732 &  0.6112067 & -0.6112067 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_SumEntropy\\_GFP\\_3\\_01\\_256                 & Cells     & Texture            & SumEntropy          & GFP & 3 & 01 & 0.2025519 & -0.008142424 &  0.6110672 & -0.6110672 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_AngularSecondMoment\\_GFP\\_3\\_02\\_256        & Cells     & Texture            & AngularSecondMoment & GFP & 3 & 02 & 0.2601552 &  0.001782800 & -0.5947110 &  0.5947110 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_AngularSecondMoment\\_GFP\\_3\\_00\\_256        & Cells     & Texture            & AngularSecondMoment & GFP & 3 & 00 & 0.2536100 &  0.002067132 & -0.5918340 &  0.5918340 & ER   \\\\\n",
+       "\t Cytoplasm\\_Texture\\_AngularSecondMoment\\_GFP\\_3\\_02\\_256    & Cytoplasm & Texture            & AngularSecondMoment & GFP & 3 & 02 & 0.2641807 &  0.001286384 & -0.5914474 &  0.5914474 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_AngularSecondMoment\\_GFP\\_3\\_01\\_256        & Cells     & Texture            & AngularSecondMoment & GFP & 3 & 01 & 0.2531856 &  0.002016927 & -0.5906073 &  0.5906073 & ER   \\\\\n",
+       "\t Cells\\_Texture\\_AngularSecondMoment\\_GFP\\_3\\_03\\_256        & Cells     & Texture            & AngularSecondMoment & GFP & 3 & 03 & 0.2602418 &  0.001495697 & -0.5904066 &  0.5904066 & ER   \\\\\n",
+       "\t Cytoplasm\\_RadialDistribution\\_ZernikeMagnitude\\_RFP\\_1\\_1 & Cytoplasm & RadialDistribution & ZernikeMagnitude    & RFP & 1 & 1  & 0.3264574 & -0.036032933 &  0.5902856 & -0.5902856 & actin\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 10 × 12\n",
+       "\n",
+       "| feature &lt;chr&gt; | compartment &lt;chr&gt; | feature_group &lt;chr&gt; | measurement &lt;chr&gt; | channel &lt;chr&gt; | parameter1 &lt;chr&gt; | parameter2 &lt;chr&gt; | r2_score &lt;dbl&gt; | cell_count_coef &lt;dbl&gt; | Null_coef &lt;dbl&gt; | WT_coef &lt;dbl&gt; | channel_cleaned &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Cells_Texture_SumEntropy_GFP_3_02_256                 | Cells     | Texture            | SumEntropy          | GFP | 3 | 02 | 0.2074251 | -0.007895167 |  0.6145610 | -0.6145610 | ER    |\n",
+       "| Cells_Texture_SumEntropy_GFP_3_03_256                 | Cells     | Texture            | SumEntropy          | GFP | 3 | 03 | 0.2036442 | -0.008115417 |  0.6121762 | -0.6121762 | ER    |\n",
+       "| Cells_Texture_SumEntropy_GFP_3_00_256                 | Cells     | Texture            | SumEntropy          | GFP | 3 | 00 | 0.2053192 | -0.007834732 |  0.6112067 | -0.6112067 | ER    |\n",
+       "| Cells_Texture_SumEntropy_GFP_3_01_256                 | Cells     | Texture            | SumEntropy          | GFP | 3 | 01 | 0.2025519 | -0.008142424 |  0.6110672 | -0.6110672 | ER    |\n",
+       "| Cells_Texture_AngularSecondMoment_GFP_3_02_256        | Cells     | Texture            | AngularSecondMoment | GFP | 3 | 02 | 0.2601552 |  0.001782800 | -0.5947110 |  0.5947110 | ER    |\n",
+       "| Cells_Texture_AngularSecondMoment_GFP_3_00_256        | Cells     | Texture            | AngularSecondMoment | GFP | 3 | 00 | 0.2536100 |  0.002067132 | -0.5918340 |  0.5918340 | ER    |\n",
+       "| Cytoplasm_Texture_AngularSecondMoment_GFP_3_02_256    | Cytoplasm | Texture            | AngularSecondMoment | GFP | 3 | 02 | 0.2641807 |  0.001286384 | -0.5914474 |  0.5914474 | ER    |\n",
+       "| Cells_Texture_AngularSecondMoment_GFP_3_01_256        | Cells     | Texture            | AngularSecondMoment | GFP | 3 | 01 | 0.2531856 |  0.002016927 | -0.5906073 |  0.5906073 | ER    |\n",
+       "| Cells_Texture_AngularSecondMoment_GFP_3_03_256        | Cells     | Texture            | AngularSecondMoment | GFP | 3 | 03 | 0.2602418 |  0.001495697 | -0.5904066 |  0.5904066 | ER    |\n",
+       "| Cytoplasm_RadialDistribution_ZernikeMagnitude_RFP_1_1 | Cytoplasm | RadialDistribution | ZernikeMagnitude    | RFP | 1 | 1  | 0.3264574 | -0.036032933 |  0.5902856 | -0.5902856 | actin |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   feature                                               compartment\n",
+       "1  Cells_Texture_SumEntropy_GFP_3_02_256                 Cells      \n",
+       "2  Cells_Texture_SumEntropy_GFP_3_03_256                 Cells      \n",
+       "3  Cells_Texture_SumEntropy_GFP_3_00_256                 Cells      \n",
+       "4  Cells_Texture_SumEntropy_GFP_3_01_256                 Cells      \n",
+       "5  Cells_Texture_AngularSecondMoment_GFP_3_02_256        Cells      \n",
+       "6  Cells_Texture_AngularSecondMoment_GFP_3_00_256        Cells      \n",
+       "7  Cytoplasm_Texture_AngularSecondMoment_GFP_3_02_256    Cytoplasm  \n",
+       "8  Cells_Texture_AngularSecondMoment_GFP_3_01_256        Cells      \n",
+       "9  Cells_Texture_AngularSecondMoment_GFP_3_03_256        Cells      \n",
+       "10 Cytoplasm_RadialDistribution_ZernikeMagnitude_RFP_1_1 Cytoplasm  \n",
+       "   feature_group      measurement         channel parameter1 parameter2\n",
+       "1  Texture            SumEntropy          GFP     3          02        \n",
+       "2  Texture            SumEntropy          GFP     3          03        \n",
+       "3  Texture            SumEntropy          GFP     3          00        \n",
+       "4  Texture            SumEntropy          GFP     3          01        \n",
+       "5  Texture            AngularSecondMoment GFP     3          02        \n",
+       "6  Texture            AngularSecondMoment GFP     3          00        \n",
+       "7  Texture            AngularSecondMoment GFP     3          02        \n",
+       "8  Texture            AngularSecondMoment GFP     3          01        \n",
+       "9  Texture            AngularSecondMoment GFP     3          03        \n",
+       "10 RadialDistribution ZernikeMagnitude    RFP     1          1         \n",
+       "   r2_score  cell_count_coef Null_coef  WT_coef    channel_cleaned\n",
+       "1  0.2074251 -0.007895167     0.6145610 -0.6145610 ER             \n",
+       "2  0.2036442 -0.008115417     0.6121762 -0.6121762 ER             \n",
+       "3  0.2053192 -0.007834732     0.6112067 -0.6112067 ER             \n",
+       "4  0.2025519 -0.008142424     0.6110672 -0.6110672 ER             \n",
+       "5  0.2601552  0.001782800    -0.5947110  0.5947110 ER             \n",
+       "6  0.2536100  0.002067132    -0.5918340  0.5918340 ER             \n",
+       "7  0.2641807  0.001286384    -0.5914474  0.5914474 ER             \n",
+       "8  0.2531856  0.002016927    -0.5906073  0.5906073 ER             \n",
+       "9  0.2602418  0.001495697    -0.5904066  0.5904066 ER             \n",
+       "10 0.3264574 -0.036032933     0.5902856 -0.5902856 actin          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Arrange by absolute value coefficient\n",
+    "# Split out components of feature name for visualization\n",
+    "lm_df <- lm_df %>%\n",
+    "    dplyr::arrange(desc(abs(Null_coef))) %>%\n",
+    "    tidyr::separate(\n",
+    "        feature,\n",
+    "        into = c(\n",
+    "            \"compartment\",\n",
+    "            \"feature_group\",\n",
+    "            \"measurement\",\n",
+    "            \"channel\",\n",
+    "            \"parameter1\",\n",
+    "            \"parameter2\"\n",
+    "        ),\n",
+    "        sep = \"_\",\n",
+    "        remove = FALSE\n",
+    "    ) %>%\n",
+    "    dplyr::mutate(channel_cleaned = channel)\n",
+    "\n",
+    "# Clean channel for visualization and interpretation\n",
+    "lm_df$channel_cleaned <-\n",
+    "    dplyr::recode(\n",
+    "        lm_df$channel_cleaned,\n",
+    "        \"DAPI\" = \"nuclei\",\n",
+    "        \"GFP\" = \"ER\",\n",
+    "        \"RFP\" = \"actin\",\n",
+    "        .default = \"other\",\n",
+    "        .missing = \"other\"\n",
+    "    )\n",
+    "\n",
+    "# Print to make sure that the above clean up worked\n",
+    "print(dim(lm_df))\n",
+    "head(lm_df, 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize LM as scatter plot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Specify order so that the organelles match the correct color (red, green, blue)\n",
+    "color_order <- c(\"actin\", \"ER\", \"nuclei\", \"other\")\n",
+    "\n",
+    "# Plot the linear model coefficients for the WT genotype contribution\n",
+    "# Positive coeff = more likely to be WT cell if feature increases\n",
+    "# Negative coeff = more likely to be Null cell if feature increases\n",
+    "lm_fig_gg <- (\n",
+    "    ggplot(lm_df, aes(x = cell_count_coef, y = WT_coef))\n",
+    "    +\n",
+    "        geom_point(aes(size = r2_score, color = factor(channel_cleaned, levels = color_order)), alpha = 0.7)\n",
+    "        +\n",
+    "        geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\")\n",
+    "        +\n",
+    "        geom_density2d(color = \"black\", show.legend = FALSE)\n",
+    "        +\n",
+    "        theme_bw()\n",
+    "        +\n",
+    "        guides(\n",
+    "            color = guide_legend(title = \"Channel\\n(if applicable)\", order = 1),\n",
+    "            size = guide_legend(title = \"R2 score\")\n",
+    "        )\n",
+    "        +\n",
+    "        ylab(\"WT genotype contribution (LM beta coefficient)\")\n",
+    "        +\n",
+    "        xlab(\"Cell count contribution (LM beta coefficient)\")\n",
+    "        +\n",
+    "        ggtitle(\"Scatter plot of linear model coefficients per CellProfiler feature\\n for Plate 1\") # nolint\n",
+    ")\n",
+    "\n",
+    "# Output figure\n",
+    "ggsave(lm_fig, lm_fig_gg, dpi = 500, height = 6, width = 6)\n",
+    "\n",
+    "# Show figure\n",
+    "lm_fig_gg\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.2.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/4.analyze_data/notebooks/linear_model/figures/linear_model_cp_features_plate1.png b/4.analyze_data/notebooks/linear_model/figures/linear_model_cp_features_plate1.png
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diff --git a/4.analyze_data/notebooks/linear_model/figures/linear_model_cp_features_plate2.png b/4.analyze_data/notebooks/linear_model/figures/linear_model_cp_features_plate2.png
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@@ -0,0 +1,1732 @@
+feature	r2_score	cell_count_coef	Null_coef	WT_coef
+Cytoplasm_AreaShape_Area	0.389572935786452	-0.01755070039291648	-0.33601431131024784	0.33601431131024795
+Cytoplasm_AreaShape_BoundingBoxArea	0.37712998930280517	-0.020594709211684468	-0.24376003602015694	0.24376003602015706
+Cytoplasm_AreaShape_BoundingBoxMaximum_X	0.0012736551590678546	-0.0006782520191247732	-0.026804702751417282	0.026804702751417296
+Cytoplasm_AreaShape_BoundingBoxMaximum_Y	0.005930618693278045	0.0007155080241384694	-0.09623010258738013	0.09623010258738017
+Cytoplasm_AreaShape_BoundingBoxMinimum_X	0.008106604632228787	0.0037406716271279794	0.014582643344838761	-0.014582643344838768
+Cytoplasm_AreaShape_BoundingBoxMinimum_Y	0.026489120432215962	0.00764577724257719	-0.0033747035443796695	0.0033747035443796713
+Cytoplasm_AreaShape_Center_X	0.0007634527439290828	0.0014139003553762326	-0.004990787538111709	0.004990787538111711
+Cytoplasm_AreaShape_Center_Y	0.004553499782280035	0.004074106844337117	-0.04441431156143394	0.04441431156143395
+Cytoplasm_AreaShape_Compactness	0.0624118279270226	0.01565493401019513	-0.22258752596736658	0.22258752596736664
+Cytoplasm_AreaShape_ConvexArea	0.37611326566884096	-0.017978380899414934	-0.3116513297280246	0.3116513297280247
+Cytoplasm_AreaShape_Eccentricity	0.012305745914520605	-0.006783892275418703	0.0799107782315419	-0.07991077823154193
+Cytoplasm_AreaShape_EquivalentDiameter	0.37482393821935267	-0.0188182759005628	-0.2886877631210668	0.28868776312106686
+Cytoplasm_AreaShape_EulerNumber	1.0	0.0	0.0	0.0
+Cytoplasm_AreaShape_Extent	0.024247003232714892	0.009280120983423368	-0.09393242189957049	0.09393242189957053
+Cytoplasm_AreaShape_FormFactor	0.06017672322490153	-0.015464505893826433	0.24293462970118546	-0.2429346297011855
+Cytoplasm_AreaShape_MajorAxisLength	0.2474317232102009	-0.018869450093937798	-0.13541851998404497	0.13541851998404503
+Cytoplasm_AreaShape_MaxFeretDiameter	0.3156446945767474	-0.018163366211423344	-0.24131331485422391	0.24131331485422403
+Cytoplasm_AreaShape_MaximumRadius	0.31876490661306556	-0.016653724529064676	-0.2842916888841115	0.2842916888841116
+Cytoplasm_AreaShape_MeanRadius	0.34341560822572725	-0.01944275567994958	-0.23831696493015053	0.23831696493015064
+Cytoplasm_AreaShape_MedianRadius	0.3346423830086137	-0.020150772667955733	-0.20890804303751231	0.2089080430375124
+Cytoplasm_AreaShape_MinFeretDiameter	0.22392122172746964	-0.014255256117135719	-0.23064394649721837	0.23064394649721842
+Cytoplasm_AreaShape_MinorAxisLength	0.1672691840708016	-0.01350567940237169	-0.1680447969896973	0.16804479698969735
+Cytoplasm_AreaShape_Orientation	0.001087786759626841	0.0020800611727804395	-0.03369134354049348	0.033691343540493496
+Cytoplasm_AreaShape_Perimeter	0.18749013709180118	-0.0048816920651813265	-0.3941275765601705	0.39412757656017067
+Cytoplasm_AreaShape_Solidity	0.00046838973360296965	-0.0013142278490359456	0.027809474817079516	-0.027809474817079523
+Cytoplasm_AreaShape_Zernike_0_0	0.027739313031624135	0.007908767196858088	-0.006511521293459553	0.0065115212934595545
+Cytoplasm_AreaShape_Zernike_1_1	0.03583240849062308	-0.01165492240365786	0.23459722133806454	-0.2345972213380646
+Cytoplasm_AreaShape_Zernike_2_0	0.052798775790455554	0.007953398387862265	-0.3424073184457337	0.3424073184457338
+Cytoplasm_AreaShape_Zernike_2_2	0.03044238517715947	0.0007911242672439925	0.18050844863209697	-0.18050844863209703
+Cytoplasm_AreaShape_Zernike_3_1	0.010681110919731562	-0.006401968421187291	0.0822760911563763	-0.08227609115637631
+Cytoplasm_AreaShape_Zernike_3_3	0.01441420499109436	0.0014834161248678478	0.10673790744181214	-0.10673790744181219
+Cytoplasm_AreaShape_Zernike_4_0	0.0005736977641263019	0.001242653892478299	-0.005026273513497995	0.005026273513497997
+Cytoplasm_AreaShape_Zernike_4_2	0.002839659321576482	0.002857246043113852	-0.0151757076523194	0.015175707652319405
+Cytoplasm_AreaShape_Zernike_4_4	0.01820105100159164	0.0006352063477827453	0.1391632779448264	-0.13916327794482647
+Cytoplasm_AreaShape_Zernike_5_1	0.03043451154559329	0.007927704724620278	0.0058043374397928355	-0.005804337439792838
+Cytoplasm_AreaShape_Zernike_5_3	0.022302914166490884	-0.0008900257576927465	0.17961093287861885	-0.1796109328786189
+Cytoplasm_AreaShape_Zernike_5_5	0.014915680490526828	-0.0024647612667874413	0.16852962391647303	-0.16852962391647308
+Cytoplasm_AreaShape_Zernike_6_0	0.04139101816676338	0.00730107855119901	0.06744140381646126	-0.06744140381646128
+Cytoplasm_AreaShape_Zernike_6_2	0.0012977117851048492	0.001556589788703359	0.003903868465414696	-0.0039038684654146972
+Cytoplasm_AreaShape_Zernike_6_4	0.002214463926075383	0.0016190308580794099	0.017554992361032286	-0.017554992361032293
+Cytoplasm_AreaShape_Zernike_6_6	0.0022553840160708383	0.0018380273660147715	0.011845060760201302	-0.011845060760201306
+Cytoplasm_AreaShape_Zernike_7_1	0.014848405185646896	0.002208885798728666	0.09383671271321106	-0.0938367127132111
+Cytoplasm_AreaShape_Zernike_7_3	0.0037956656257297494	-0.0023272898709335577	0.09256012852641679	-0.09256012852641682
+Cytoplasm_AreaShape_Zernike_7_5	0.009348831665359936	-0.005224717663310536	0.14013846226676524	-0.1401384622667653
+Cytoplasm_AreaShape_Zernike_7_7	0.009091663413252293	-0.005715042524281299	0.12565811963789061	-0.12565811963789064
+Cytoplasm_AreaShape_Zernike_8_0	0.031104014815965475	0.0045548265328213145	0.10487423122842304	-0.10487423122842308
+Cytoplasm_AreaShape_Zernike_8_2	0.018354244218063087	0.0017842126370500536	0.1182553845230567	-0.11825538452305673
+Cytoplasm_AreaShape_Zernike_8_4	0.0006115309039530503	-0.0013594821697919515	0.008587391424292493	-0.008587391424292495
+Cytoplasm_AreaShape_Zernike_8_6	0.008780222665883408	-0.003245352178663181	-0.03435903087823877	0.034359030878238786
+Cytoplasm_AreaShape_Zernike_8_8	0.006566873183603605	0.004260898124729298	-0.019399153359057772	0.01939915335905778
+Cytoplasm_AreaShape_Zernike_9_1	0.019233194452282998	-0.007149023227253768	0.027193951314031538	-0.027193951314031548
+Cytoplasm_AreaShape_Zernike_9_3	0.005715066973587257	-0.003059170887418327	0.11404107175871875	-0.11404107175871878
+Cytoplasm_AreaShape_Zernike_9_5	0.0017792945693287932	0.0025666173904967038	-0.02930546914469226	0.029305469144692266
+Cytoplasm_AreaShape_Zernike_9_7	0.004328220591482301	-0.0033653053997766366	0.01183341083011679	-0.011833410830116796
+Cytoplasm_AreaShape_Zernike_9_9	0.01352699458563944	-0.004959553408999138	0.17564744911311722	-0.1756474491131173
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.051816557047751344	-0.00957307252919701	-0.0331830828110381	0.03318308281103811
+Cytoplasm_Correlation_Correlation_DAPI_RFP	0.0030865996606677104	0.003440218648852205	-0.044092604511272164	0.04409260451127218
+Cytoplasm_Correlation_Correlation_GFP_RFP	0.045551079093629654	0.002694746635983485	0.18860462025981978	-0.18860462025981983
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.11136113671093018	-0.021009886064181776	0.3190522543537899	-0.31905225435379003
+Cytoplasm_Correlation_Costes_DAPI_RFP	0.17828631721091026	-0.021563699950353322	0.07452461556586255	-0.07452461556586258
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.01970204988496227	0.008755600039597258	-0.1650161259784926	0.16501612597849266
+Cytoplasm_Correlation_Costes_GFP_RFP	0.07682835506578112	-0.016945840596863413	0.1992584803775671	-0.19925848037756716
+Cytoplasm_Correlation_Costes_RFP_DAPI	0.27219864669248695	0.021807760369396603	0.08031045592164794	-0.08031045592164797
+Cytoplasm_Correlation_Costes_RFP_GFP	0.03818654751504236	-0.00893318091467513	-0.00466410145146846	0.004664101451468462
+Cytoplasm_Correlation_K_DAPI_GFP	0.22108439702017646	-0.029605791601549008	0.45043607241620487	-0.45043607241620504
+Cytoplasm_Correlation_K_DAPI_RFP	0.3207204224584903	-0.03474420405583177	0.4177649393427828	-0.417764939342783
+Cytoplasm_Correlation_K_GFP_DAPI	0.19861469917121555	0.027828571409423616	-0.38190581855482963	0.38190581855482975
+Cytoplasm_Correlation_K_GFP_RFP	0.2841081738882374	-0.03203387587727169	0.3400688185265464	-0.3400688185265465
+Cytoplasm_Correlation_K_RFP_DAPI	0.2336968077835755	0.03030216473335242	-0.4322820518985634	0.43228205189856356
+Cytoplasm_Correlation_K_RFP_GFP	0.2288648456822715	0.02998247244835947	-0.42695666486005	0.42695666486005013
+Cytoplasm_Correlation_Manders_DAPI_GFP	0.01348433166583174	0.004621249848854508	0.02512369624383798	-0.025123696243837985
+Cytoplasm_Correlation_Manders_DAPI_RFP	0.0023115419729005104	2.692230486421432e-05	0.05297585886180296	-0.05297585886180298
+Cytoplasm_Correlation_Manders_GFP_DAPI	0.021065527741658263	-0.009075053967763628	0.16827659965681493	-0.168276599656815
+Cytoplasm_Correlation_Manders_GFP_RFP	0.004734955558926335	-0.0006380554338341385	0.08596696808794846	-0.08596696808794849
+Cytoplasm_Correlation_Manders_RFP_DAPI	0.03195496616386473	0.0004941899933478856	0.19039289549012828	-0.19039289549012833
+Cytoplasm_Correlation_Manders_RFP_GFP	0.36183390616952327	0.018876809699179535	0.27349319403698763	-0.27349319403698774
+Cytoplasm_Correlation_Overlap_DAPI_GFP	0.0077554430389159545	0.0002135416455157235	0.09430311206544406	-0.09430311206544409
+Cytoplasm_Correlation_Overlap_DAPI_RFP	0.042002366956394255	-0.01291962213415666	0.20279853198364423	-0.2027985319836443
+Cytoplasm_Correlation_Overlap_GFP_RFP	0.032139511977901436	0.011293453755763834	-0.17365697882708747	0.17365697882708755
+Cytoplasm_Correlation_RWC_DAPI_GFP	0.23248922152671125	0.02677311747696252	-0.18025743111383447	0.18025743111383455
+Cytoplasm_Correlation_RWC_DAPI_RFP	0.07861499095300295	-0.015639094950869882	0.10829168545930468	-0.1082916854593047
+Cytoplasm_Correlation_RWC_GFP_DAPI	0.012322812739313793	0.0031674032906453173	0.05860848933930081	-0.05860848933930082
+Cytoplasm_Correlation_RWC_GFP_RFP	0.020630059730777695	-0.00827546157610464	0.19913024335042678	-0.19913024335042684
+Cytoplasm_Correlation_RWC_RFP_DAPI	0.04526697889002296	0.005243374307929792	0.13253060035181757	-0.1325306003518176
+Cytoplasm_Correlation_RWC_RFP_GFP	0.30951636242949443	0.012426700484160695	0.3764597336854384	-0.37645973368543856
+Cytoplasm_Granularity_10_DAPI	0.019491621925217695	-0.005703180970805081	0.21068099647228267	-0.2106809964722827
+Cytoplasm_Granularity_10_GFP	0.014273952308374693	-0.007507658135388553	0.11050338506056684	-0.11050338506056688
+Cytoplasm_Granularity_10_RFP	0.017347957824066618	-0.00797347466496354	0.17036358001182741	-0.17036358001182747
+Cytoplasm_Granularity_11_DAPI	0.016630620878514413	-0.001806295454642632	0.16899358832004835	-0.1689935883200484
+Cytoplasm_Granularity_11_GFP	0.003857648013364412	-0.0030694595079539953	0.09265714642500886	-0.09265714642500888
+Cytoplasm_Granularity_11_RFP	0.022171767430813172	0.0009680677713752655	0.1488444605567157	-0.14884446055671577
+Cytoplasm_Granularity_12_DAPI	0.002722176958237621	-0.002819586592447431	0.07561637829700749	-0.07561637829700751
+Cytoplasm_Granularity_12_GFP	0.004609337745768438	-0.0027041647721771215	-0.014618647970759239	0.014618647970759242
+Cytoplasm_Granularity_12_RFP	0.027011739659579037	0.0009634062264952278	0.1661764468645633	-0.16617644686456332
+Cytoplasm_Granularity_13_DAPI	0.013232660069121382	-0.007176559457582546	0.13513093126664552	-0.13513093126664558
+Cytoplasm_Granularity_13_GFP	0.0007379297311288502	0.0012627591668717705	-0.04087853514080784	0.04087853514080785
+Cytoplasm_Granularity_13_RFP	0.047824405853215235	0.0019504960443929708	0.2088800113678705	-0.20888001136787057
+Cytoplasm_Granularity_14_DAPI	0.0036425305129899055	-0.00313500326935613	0.08887811193697187	-0.0888781119369719
+Cytoplasm_Granularity_14_GFP	0.01800430254713592	0.0067683513980286115	-0.19926556059471173	0.19926556059471182
+Cytoplasm_Granularity_14_RFP	0.17054276003906577	-0.025991449770570652	0.4509469423800153	-0.45094694238001554
+Cytoplasm_Granularity_15_DAPI	0.01868203679822522	0.0026801013100149654	-0.18779886510095872	0.18779886510095878
+Cytoplasm_Granularity_15_GFP	0.01425652340584227	-0.004186470125548052	-0.04235668497951843	0.042356684979518444
+Cytoplasm_Granularity_15_RFP	0.034540856490932526	0.007738271927936084	0.029567085758993694	-0.029567085758993705
+Cytoplasm_Granularity_16_DAPI	0.028064995305054574	-0.004616407993215211	-0.09253316769447945	0.09253316769447947
+Cytoplasm_Granularity_16_GFP	0.039460910034650354	-0.0072396605580060495	-0.06268549440405016	0.06268549440405019
+Cytoplasm_Granularity_16_RFP	0.05803484672837578	0.0045891531168567885	0.18070256035233478	-0.18070256035233484
+Cytoplasm_Granularity_1_DAPI	0.19374454898271598	-0.010067066481950571	-0.2924363357372069	0.292436335737207
+Cytoplasm_Granularity_1_GFP	0.06384973675397776	0.01028385552424319	-0.3812628547960626	0.38126285479606276
+Cytoplasm_Granularity_1_RFP	0.04868720177911401	0.005337445233994943	-0.31296880580063247	0.3129688058006326
+Cytoplasm_Granularity_2_DAPI	0.18537794394069984	-0.008783786026157656	-0.3101472363751028	0.3101472363751029
+Cytoplasm_Granularity_2_GFP	0.0387354927607354	0.012090638987100678	-0.24558587138818938	0.2455858713881895
+Cytoplasm_Granularity_2_RFP	0.04755703200181982	0.013581821153225922	-0.25845984577983233	0.2584598457798324
+Cytoplasm_Granularity_3_DAPI	0.0073779116667234135	-0.0014968720988537934	-0.06743276021675523	0.06743276021675526
+Cytoplasm_Granularity_3_GFP	0.05778802218954837	0.014424487669573332	-0.15174911797673554	0.1517491179767356
+Cytoplasm_Granularity_3_RFP	0.07437810831600322	0.017147700844300683	-0.2545441076043534	0.2545441076043535
+Cytoplasm_Granularity_4_DAPI	0.009962490583552586	0.004060068282766943	0.01888649381155113	-0.018886493811551136
+Cytoplasm_Granularity_4_GFP	0.025869280338423506	0.008911642278787124	-0.05919526576256111	0.05919526576256113
+Cytoplasm_Granularity_4_RFP	0.04773885684638479	0.013064258124226708	-0.13470658813098693	0.134706588130987
+Cytoplasm_Granularity_5_DAPI	0.029312598444216187	0.008061391039227383	-0.004212961142087744	0.0042129611420877455
+Cytoplasm_Granularity_5_GFP	0.02739468781003851	0.010191606681138417	-0.12555604792720826	0.12555604792720831
+Cytoplasm_Granularity_5_RFP	0.03833444402817232	0.0096205855837039	-0.01968958242513674	0.019689582425136746
+Cytoplasm_Granularity_6_DAPI	0.04956186627372117	0.011836552869982178	-0.05896414534916758	0.05896414534916759
+Cytoplasm_Granularity_6_GFP	0.017429034753679518	0.00334640913474652	0.08002349505332189	-0.08002349505332192
+Cytoplasm_Granularity_6_RFP	0.0313830104628553	0.009980201084094302	-0.07341466468831052	0.07341466468831055
+Cytoplasm_Granularity_7_DAPI	0.04943776714266079	0.010949464902244247	-0.023279637360103328	0.023279637360103335
+Cytoplasm_Granularity_7_GFP	0.021885252859452198	-0.0010954438491658045	0.1809876790303656	-0.18098767903036567
+Cytoplasm_Granularity_7_RFP	0.02730240985663035	0.003950039482750336	0.10580328039602221	-0.10580328039602227
+Cytoplasm_Granularity_8_DAPI	0.03815576937255927	0.0020457680400173812	0.1808185292401362	-0.18081852924013625
+Cytoplasm_Granularity_8_GFP	0.036861457784020724	-0.007918816354363678	0.2898109468324992	-0.2898109468324993
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+Cells_Granularity_10_DAPI	0.08557300813290503	-0.01731141416695574	0.16844149292133187	-0.16844149292133193
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+Nuclei_Children_Cytoplasm_Count	1.0	0.0	0.0	0.0
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+Nuclei_Texture_SumVariance_DAPI_3_02_256	0.18842429128598204	0.016239749256816854	0.12482495731050455	-0.12482495731050461
+Nuclei_Texture_SumVariance_DAPI_3_03_256	0.14548332286382837	0.011617561149956856	0.1826191926488288	-0.18261919264882887
+Nuclei_Texture_SumVariance_GFP_3_00_256	0.030915065476903925	-0.007156239971311371	0.2652961027809695	-0.26529610278096954
+Nuclei_Texture_SumVariance_GFP_3_01_256	0.02903012284075801	-0.007133775829888539	0.25727313367623333	-0.25727313367623345
+Nuclei_Texture_SumVariance_GFP_3_02_256	0.03273701880177371	-0.0073235344243224075	0.27294458722521686	-0.2729445872252169
+Nuclei_Texture_SumVariance_GFP_3_03_256	0.03550246009360447	-0.007509771440667168	0.28404449549574573	-0.28404449549574584
+Nuclei_Texture_SumVariance_RFP_3_00_256	0.05443571776241796	-0.014672074273510576	0.21830927075333917	-0.21830927075333922
+Nuclei_Texture_SumVariance_RFP_3_01_256	0.05294143271344154	-0.0144566078158097	0.21234546958247552	-0.21234546958247558
+Nuclei_Texture_SumVariance_RFP_3_02_256	0.049401493688751685	-0.013920181793702318	0.19672476450346432	-0.1967247645034644
+Nuclei_Texture_SumVariance_RFP_3_03_256	0.049045169822312995	-0.013904479889518382	0.20228511396328036	-0.20228511396328042
+Nuclei_Texture_Variance_DAPI_3_00_256	0.26270921730572416	0.02022410799838906	0.1161809205548599	-0.11618092055485994
+Nuclei_Texture_Variance_DAPI_3_01_256	0.2555967311748386	0.020066559781798183	0.11100512882261174	-0.11100512882261176
+Nuclei_Texture_Variance_DAPI_3_02_256	0.26325899173444167	0.020671022584495827	0.10327584147069135	-0.1032758414706914
+Nuclei_Texture_Variance_DAPI_3_03_256	0.2570706073094081	0.019642554173187707	0.1258783268028535	-0.12587832680285355
+Nuclei_Texture_Variance_GFP_3_00_256	0.030320315641456985	-0.007151889743142654	0.2628109270584591	-0.2628109270584592
+Nuclei_Texture_Variance_GFP_3_01_256	0.029855036087757192	-0.0072360056213245745	0.26090375325752224	-0.26090375325752235
+Nuclei_Texture_Variance_GFP_3_02_256	0.03221162683688494	-0.0073446697152543036	0.2708527614519391	-0.2708527614519392
+Nuclei_Texture_Variance_GFP_3_03_256	0.03216020481733939	-0.007182494169253913	0.2704066928742185	-0.27040669287421853
+Nuclei_Texture_Variance_RFP_3_00_256	0.05641424791614591	-0.014853859731753887	0.20682272374512384	-0.2068227237451239
+Nuclei_Texture_Variance_RFP_3_01_256	0.05536121634918634	-0.014700678986915999	0.20283054892189312	-0.20283054892189317
+Nuclei_Texture_Variance_RFP_3_02_256	0.05515833157496575	-0.014678014242223179	0.2030829837765042	-0.20308298377650424
+Nuclei_Texture_Variance_RFP_3_03_256	0.05540343733680764	-0.01470083851724762	0.20212902379905745	-0.2021290237990575
diff --git a/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate2.tsv b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate2.tsv
new file mode 100644
index 0000000..0b53cee
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate2.tsv
@@ -0,0 +1,1732 @@
+feature	r2_score	cell_count_coef	Null_coef	WT_coef
+Cytoplasm_AreaShape_Area	0.17731250508476637	-0.015517203234271242	-0.23378258983859646	0.23378258983859646
+Cytoplasm_AreaShape_BoundingBoxArea	0.15487330225112317	-0.014896605107559713	-0.21225655837252932	0.2122565583725293
+Cytoplasm_AreaShape_BoundingBoxMaximum_X	0.0007708634682550564	0.0008951416522268898	-0.034657334120680706	0.034657334120680706
+Cytoplasm_AreaShape_BoundingBoxMaximum_Y	0.0028361562839360754	0.0005922949497653765	-0.05935985767894211	0.05935985767894211
+Cytoplasm_AreaShape_BoundingBoxMinimum_X	0.006048483932609372	0.004463702926390761	0.014090578838866663	-0.014090578838866665
+Cytoplasm_AreaShape_BoundingBoxMinimum_Y	0.0061906150932710124	0.004850582900915674	0.006824882854122034	-0.0068248828541220335
+Cytoplasm_AreaShape_Center_X	0.0012076565763337044	0.0026058239099194377	-0.0098900904674234	0.0098900904674234
+Cytoplasm_AreaShape_Center_Y	0.000958569808072296	0.0025750086387218343	-0.024867587312000457	0.024867587312000457
+Cytoplasm_AreaShape_Compactness	0.08719654811552535	0.0072751143906827345	-0.356911124424484	0.356911124424484
+Cytoplasm_AreaShape_ConvexArea	0.16741927270260493	-0.014764681570636198	-0.2320626878465618	0.2320626878465618
+Cytoplasm_AreaShape_Eccentricity	0.0021468726129246773	-0.00381399102274571	0.043575339300725276	-0.04357533930072528
+Cytoplasm_AreaShape_EquivalentDiameter	0.1690598282572141	-0.015237170750593757	-0.22693516041704231	0.22693516041704231
+Cytoplasm_AreaShape_EulerNumber	0.0010569072840904692	-0.0016437912676502827	0.042110931487583415	-0.042110931487583415
+Cytoplasm_AreaShape_Extent	0.00020250148340483776	-0.0007707400803392433	0.018419970086195656	-0.018419970086195656
+Cytoplasm_AreaShape_FormFactor	0.062459126692823164	-0.00783995429973381	0.31096288919903053	-0.31096288919903053
+Cytoplasm_AreaShape_MajorAxisLength	0.11406423002406685	-0.01427646818384898	-0.15771510136048292	0.15771510136048292
+Cytoplasm_AreaShape_MaxFeretDiameter	0.12788805918882173	-0.014186375856744428	-0.18242356084108374	0.18242356084108374
+Cytoplasm_AreaShape_MaximumRadius	0.11242574032161723	-0.015059588039805632	-0.14135509918596026	0.14135509918596026
+Cytoplasm_AreaShape_MeanRadius	0.0998562876593414	-0.016139973586961093	-0.09757284708054217	0.09757284708054217
+Cytoplasm_AreaShape_MedianRadius	0.09604996810727806	-0.016773997650982517	-0.07714230851523586	0.07714230851523586
+Cytoplasm_AreaShape_MinFeretDiameter	0.13282631174125548	-0.011885091816289344	-0.22594811119453012	0.22594811119453012
+Cytoplasm_AreaShape_MinorAxisLength	0.12432656413611998	-0.01041725932134034	-0.23436699899017693	0.2343669989901769
+Cytoplasm_AreaShape_Orientation	0.0032331950961398404	-0.003891907481792108	0.07042116265925827	-0.07042116265925827
+Cytoplasm_AreaShape_Perimeter	0.18417141777995782	-0.006188737876188549	-0.3697770167459369	0.3697770167459369
+Cytoplasm_AreaShape_Solidity	0.0013910334216669096	-0.003097463017108773	0.03195710442530034	-0.03195710442530034
+Cytoplasm_AreaShape_Zernike_0_0	0.0002472189529187929	0.0012880026640607282	-0.015203556732575996	0.015203556732575998
+Cytoplasm_AreaShape_Zernike_1_1	0.0008729354456993299	0.0006985093004129394	0.022042419624899598	-0.022042419624899598
+Cytoplasm_AreaShape_Zernike_2_0	0.009781345501940764	-0.00230290885181747	-0.07425249754097532	0.07425249754097532
+Cytoplasm_AreaShape_Zernike_2_2	0.0014213527563022854	-0.0006145830780238371	0.04350931527794558	-0.04350931527794559
+Cytoplasm_AreaShape_Zernike_3_1	0.002228404261734207	-0.0017151673096228	0.0597229164752657	-0.059722916475265696
+Cytoplasm_AreaShape_Zernike_3_3	0.006189928475948792	0.0006456265713483868	0.07333457275084733	-0.07333457275084733
+Cytoplasm_AreaShape_Zernike_4_0	0.005413215781591485	0.0037959845396423517	0.021984585226650002	-0.021984585226650002
+Cytoplasm_AreaShape_Zernike_4_2	0.0016127058344734824	-0.002856999195643467	0.006454208900522897	-0.006454208900522897
+Cytoplasm_AreaShape_Zernike_4_4	0.013471471811804636	-0.0017835131062395844	0.1331575341081041	-0.13315753410810413
+Cytoplasm_AreaShape_Zernike_5_1	0.002843561935188421	0.00442331612616566	-0.04666294900350364	0.04666294900350364
+Cytoplasm_AreaShape_Zernike_5_3	0.004169965573887935	-0.0012518449044713346	0.07591323371978212	-0.07591323371978212
+Cytoplasm_AreaShape_Zernike_5_5	0.00473039915448914	0.001001922722908833	0.05914294973251241	-0.05914294973251241
+Cytoplasm_AreaShape_Zernike_6_0	0.0013426863850348747	0.0010435918866119674	0.024909637531086812	-0.024909637531086812
+Cytoplasm_AreaShape_Zernike_6_2	0.003619785851969315	-0.0009223684399317262	0.0690081771160188	-0.0690081771160188
+Cytoplasm_AreaShape_Zernike_6_4	0.009388699667493339	-0.0025913765552101732	0.11827674298786783	-0.11827674298786785
+Cytoplasm_AreaShape_Zernike_6_6	0.006084115903490783	-0.0016699404796340813	0.09273242424044542	-0.09273242424044542
+Cytoplasm_AreaShape_Zernike_7_1	0.004845288775736423	0.005775258049844514	-0.05085624739441555	0.05085624739441555
+Cytoplasm_AreaShape_Zernike_7_3	0.002105334301458983	0.0019502714173012183	0.021246145778679917	-0.02124614577867992
+Cytoplasm_AreaShape_Zernike_7_5	0.0018430630772288126	-0.0025367418116243492	0.05520724773033979	-0.05520724773033979
+Cytoplasm_AreaShape_Zernike_7_7	0.002369653548399242	-0.0013220382455608592	0.05953121681745116	-0.05953121681745116
+Cytoplasm_AreaShape_Zernike_8_0	0.00022441565872677316	0.00016891653497122627	0.013453008604540798	-0.013453008604540797
+Cytoplasm_AreaShape_Zernike_8_2	0.0022798266137687984	0.00397034542731402	-0.03933344083958053	0.03933344083958053
+Cytoplasm_AreaShape_Zernike_8_4	0.00206415676986349	0.00024507248450073856	0.04371049364025268	-0.04371049364025268
+Cytoplasm_AreaShape_Zernike_8_6	0.0008112501814018236	-0.0023561165064763666	0.019755560149232406	-0.019755560149232406
+Cytoplasm_AreaShape_Zernike_8_8	0.0023692173560827534	-0.0021801653703477455	0.06274053161019406	-0.06274053161019406
+Cytoplasm_AreaShape_Zernike_9_1	0.0007779450785642128	-0.0011715821063706794	-0.013150081665435506	0.013150081665435505
+Cytoplasm_AreaShape_Zernike_9_3	0.0033981799476104912	0.004311647928020394	-0.014551935122588521	0.014551935122588521
+Cytoplasm_AreaShape_Zernike_9_5	0.0041519128987363985	-0.0021109533194098905	0.08058217732141748	-0.0805821773214175
+Cytoplasm_AreaShape_Zernike_9_7	0.001754709472994942	-0.0008959932078454405	0.049795414146513436	-0.049795414146513436
+Cytoplasm_AreaShape_Zernike_9_9	0.0012836059119115673	0.0003613897094415363	0.032653056333077066	-0.032653056333077066
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.011958747934096459	-0.005490206915157053	0.14163701292284422	-0.14163701292284422
+Cytoplasm_Correlation_Correlation_DAPI_RFP	0.012336222608932634	-0.0038474747379204184	-0.06546304796557201	0.06546304796557202
+Cytoplasm_Correlation_Correlation_GFP_RFP	1.246706358670746e-05	-6.228353504484574e-05	0.004108467353582324	-0.004108467353582324
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.0959109508042767	-0.00638820463624033	0.366619806351988	-0.366619806351988
+Cytoplasm_Correlation_Costes_DAPI_RFP	0.11308557753454473	-0.011151348586884989	0.421140398940097	-0.4211403989400971
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.09912635828500149	-0.0002890538508215047	0.3227564989394345	-0.3227564989394345
+Cytoplasm_Correlation_Costes_GFP_RFP	0.16806915831561853	-0.005168449801585329	0.46176246410980193	-0.46176246410980193
+Cytoplasm_Correlation_Costes_RFP_DAPI	0.1357701595350007	0.00028703403220608765	0.37169750494911247	-0.37169750494911247
+Cytoplasm_Correlation_Costes_RFP_GFP	0.06815772216342253	-0.007658496086982337	0.3222524306690868	-0.3222524306690869
+Cytoplasm_Correlation_K_DAPI_GFP	0.023768438362793742	0.0012172502093327987	0.14422262791682638	-0.1442226279168264
+Cytoplasm_Correlation_K_DAPI_RFP	0.03717450894905572	0.00323490801616422	0.16068112099997822	-0.16068112099997822
+Cytoplasm_Correlation_K_GFP_DAPI	0.04185084983189513	0.00028541312773649487	-0.21064254742106497	0.21064254742106497
+Cytoplasm_Correlation_K_GFP_RFP	0.01776803586166098	0.006731777842956589	0.04261769789715746	-0.04261769789715746
+Cytoplasm_Correlation_K_RFP_DAPI	0.050587644767570716	-0.003290086912463911	-0.19324777174119562	0.19324777174119562
+Cytoplasm_Correlation_K_RFP_GFP	0.04342855425262937	-0.0075758807339346	-0.11732014653427955	0.11732014653427957
+Cytoplasm_Correlation_Manders_DAPI_GFP	0.01109030853928994	0.0009737798839191613	0.09696095435709748	-0.09696095435709748
+Cytoplasm_Correlation_Manders_DAPI_RFP	0.0030715152404485746	-0.000901696294193227	-0.0465286184500216	0.0465286184500216
+Cytoplasm_Correlation_Manders_GFP_DAPI	0.05593098845236533	0.0020387963641322403	0.21937184433679924	-0.21937184433679927
+Cytoplasm_Correlation_Manders_GFP_RFP	0.000567032836705339	0.0012223823017997537	0.00723470193184758	-0.0072347019318475805
+Cytoplasm_Correlation_Manders_RFP_DAPI	0.030342681302663044	0.005515330271808477	0.11043359780251324	-0.11043359780251323
+Cytoplasm_Correlation_Manders_RFP_GFP	0.04559209514884721	0.014420420182003582	-0.012630336930955232	0.012630336930955232
+Cytoplasm_Correlation_Overlap_DAPI_GFP	0.00015494145615813082	0.0008804941151886719	-0.015158439570076253	0.015158439570076255
+Cytoplasm_Correlation_Overlap_DAPI_RFP	0.008798883766977461	-0.006888424947649171	0.02177881353892534	-0.02177881353892534
+Cytoplasm_Correlation_Overlap_GFP_RFP	0.030636239632249262	-0.005741633161171053	-0.10801188474143558	0.10801188474143558
+Cytoplasm_Correlation_RWC_DAPI_GFP	0.0037005076351819266	0.005059266285069763	-0.048448171781092814	0.048448171781092814
+Cytoplasm_Correlation_RWC_DAPI_RFP	0.003917180881694127	0.0013750564800574263	-0.07463119793652069	0.07463119793652069
+Cytoplasm_Correlation_RWC_GFP_DAPI	0.0431353232992292	0.0046520457682348556	0.15834947024507096	-0.15834947024507096
+Cytoplasm_Correlation_RWC_GFP_RFP	0.0026040395513831083	0.0040951460429477895	-0.02593249644371204	0.02593249644371204
+Cytoplasm_Correlation_RWC_RFP_DAPI	0.03348700938959781	0.00769202114675856	0.0861859283297919	-0.08618592832979191
+Cytoplasm_Correlation_RWC_RFP_GFP	0.051008697934918135	0.015892985930494687	-0.03115355095485261	0.03115355095485261
+Cytoplasm_Granularity_10_DAPI	0.022345527482351857	-0.005094992554973013	-0.08937017483548121	0.08937017483548121
+Cytoplasm_Granularity_10_GFP	0.0016360267774568893	0.0008458572921334958	-0.047957508891703346	0.04795750889170335
+Cytoplasm_Granularity_10_RFP	0.007871779809187518	0.005920840431103678	-0.003372409301928993	0.0033724093019289925
+Cytoplasm_Granularity_11_DAPI	0.002325986892177445	0.0016647102715040645	-0.060674405199550474	0.06067440519955048
+Cytoplasm_Granularity_11_GFP	0.006731449802551914	0.004927652081009924	-0.10527681711226503	0.10527681711226504
+Cytoplasm_Granularity_11_RFP	0.012832414642342749	0.0016013939632342768	0.09798708572386897	-0.09798708572386897
+Cytoplasm_Granularity_12_DAPI	0.0005575333131044857	0.00039179385555800355	-0.02729949543928098	0.02729949543928098
+Cytoplasm_Granularity_12_GFP	0.0019017690059549563	0.00333973406189887	-0.04954443756774109	0.04954443756774109
+Cytoplasm_Granularity_12_RFP	0.0033923032789670016	-0.0001683519132673836	0.060784785646811404	-0.06078478564681141
+Cytoplasm_Granularity_13_DAPI	0.0011076278271363593	0.0004800822456167788	-0.03794882400373408	0.03794882400373408
+Cytoplasm_Granularity_13_GFP	0.0038220391389615083	-0.0018856480680192577	0.07666746442138243	-0.07666746442138245
+Cytoplasm_Granularity_13_RFP	0.0001979383686284697	0.0011586601258102153	-0.013187856426280387	0.013187856426280387
+Cytoplasm_Granularity_14_DAPI	0.0016872867173610695	-0.0033666423930795737	0.025422682036445286	-0.025422682036445286
+Cytoplasm_Granularity_14_GFP	0.003132191608614998	0.000620518184652994	0.0503786493163943	-0.0503786493163943
+Cytoplasm_Granularity_14_RFP	0.009083517965053467	0.00414746069336572	0.042487268510702524	-0.042487268510702524
+Cytoplasm_Granularity_15_DAPI	0.0017077561369083227	-0.003214169646970864	0.016168321020544007	-0.016168321020544007
+Cytoplasm_Granularity_15_GFP	0.0014480921699141502	0.0006807823677707167	0.03119429395315307	-0.031194293953153065
+Cytoplasm_Granularity_15_RFP	0.008173259394735899	-0.007179969204234641	0.09663197017853664	-0.09663197017853664
+Cytoplasm_Granularity_16_DAPI	0.006262210534441759	0.006576710259936406	-0.06042737455850147	0.06042737455850147
+Cytoplasm_Granularity_16_GFP	0.009285070458732947	-0.007781572505385526	0.05173684810298895	-0.05173684810298895
+Cytoplasm_Granularity_16_RFP	0.0029468502019933673	0.003774119997367974	-0.006173418076212573	0.0061734180762125735
+Cytoplasm_Granularity_1_DAPI	0.04434731870818831	-0.006269881641946072	-0.13928335442655312	0.13928335442655312
+Cytoplasm_Granularity_1_GFP	0.051288286103116754	0.0031287200091849603	-0.25718895686299004	0.25718895686299004
+Cytoplasm_Granularity_1_RFP	0.006697756944944566	0.004135331528640041	-0.10600777245326731	0.10600777245326731
+Cytoplasm_Granularity_2_DAPI	0.024665022037873796	-0.006740547399570095	-0.07161485283998273	0.07161485283998273
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+Cells_Granularity_10_DAPI	0.034234350063003904	-0.0062905369087576405	-0.11085833455774718	0.1108583345577472
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+Nuclei_Children_Cells_Count	1.0	0.0	0.0	0.0
+Nuclei_Children_Cytoplasm_Count	1.0	0.0	0.0	0.0
+Nuclei_Correlation_Correlation_DAPI_GFP	0.008252247106903421	-0.00014339673021030691	0.09369353512839343	-0.09369353512839343
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+Nuclei_Texture_SumAverage_DAPI_3_03_256	0.04567438309538874	0.007201878781436633	0.12901609237060685	-0.12901609237060685
+Nuclei_Texture_SumAverage_GFP_3_00_256	0.04471503641363517	0.003865454961177688	0.1723212076510521	-0.17232120765105213
+Nuclei_Texture_SumAverage_GFP_3_01_256	0.0446633028276171	0.003852693550457209	0.17235208403682395	-0.17235208403682395
+Nuclei_Texture_SumAverage_GFP_3_02_256	0.044589455876665296	0.0038691998087816877	0.17196512725807314	-0.17196512725807314
+Nuclei_Texture_SumAverage_GFP_3_03_256	0.0447069865847638	0.003821830522074212	0.17284213899831796	-0.17284213899831796
+Nuclei_Texture_SumAverage_RFP_3_00_256	0.038230254165597	0.005101737224347731	0.1394090875911702	-0.1394090875911702
+Nuclei_Texture_SumAverage_RFP_3_01_256	0.038003054204015396	0.0051110217253013705	0.13865885346321838	-0.13865885346321838
+Nuclei_Texture_SumAverage_RFP_3_02_256	0.038135764545594086	0.0050889512208174845	0.13932539758397958	-0.13932539758397958
+Nuclei_Texture_SumAverage_RFP_3_03_256	0.03789978443514652	0.005035287048851347	0.13941183323423434	-0.13941183323423434
+Nuclei_Texture_SumEntropy_DAPI_3_00_256	0.021620944539225806	-0.0004532289335497713	0.15371644749328028	-0.15371644749328028
+Nuclei_Texture_SumEntropy_DAPI_3_01_256	0.025986595494261522	0.00016321577872911028	0.16224707796625684	-0.16224707796625684
+Nuclei_Texture_SumEntropy_DAPI_3_02_256	0.021751352108934308	-0.0006017613578031193	0.1555232736503889	-0.1555232736503889
+Nuclei_Texture_SumEntropy_DAPI_3_03_256	0.02514964761531535	-0.0001754741719505438	0.16285637386634674	-0.16285637386634674
+Nuclei_Texture_SumEntropy_GFP_3_00_256	0.053085942680985765	0.005533340063825296	0.17074571776844055	-0.17074571776844055
+Nuclei_Texture_SumEntropy_GFP_3_01_256	0.053768680107491806	0.00561773462654548	0.17118643710266604	-0.17118643710266607
+Nuclei_Texture_SumEntropy_GFP_3_02_256	0.0525075896118502	0.0055707463642099145	0.16890890205461445	-0.16890890205461445
+Nuclei_Texture_SumEntropy_GFP_3_03_256	0.05449537803370286	0.005429719920343771	0.17534290612237982	-0.17534290612237982
+Nuclei_Texture_SumEntropy_RFP_3_00_256	0.03298550930568411	0.010568926187516913	0.029793804744800353	-0.029793804744800353
+Nuclei_Texture_SumEntropy_RFP_3_01_256	0.03294049309079938	0.010104323946097818	0.03944186298388678	-0.03944186298388678
+Nuclei_Texture_SumEntropy_RFP_3_02_256	0.03328972204057312	0.009931893866194634	0.04426933019537263	-0.04426933019537263
+Nuclei_Texture_SumEntropy_RFP_3_03_256	0.03193265841886184	0.010120439743501226	0.0352509192903395	-0.0352509192903395
+Nuclei_Texture_SumVariance_DAPI_3_00_256	0.035682852946026467	0.005820533242481573	0.12210255992882119	-0.12210255992882119
+Nuclei_Texture_SumVariance_DAPI_3_01_256	0.03669995164964601	0.005718651378721892	0.12649208313754415	-0.12649208313754418
+Nuclei_Texture_SumVariance_DAPI_3_02_256	0.036188230812237765	0.0062390889175074045	0.11740890194238304	-0.11740890194238306
+Nuclei_Texture_SumVariance_DAPI_3_03_256	0.039614971428146184	0.006227590371215318	0.12727347745159467	-0.1272734774515947
+Nuclei_Texture_SumVariance_GFP_3_00_256	0.036424298924643383	0.005396618123839048	0.13028847808419697	-0.13028847808419697
+Nuclei_Texture_SumVariance_GFP_3_01_256	0.03578304052692971	0.005223128230772266	0.13089960919493276	-0.1308996091949328
+Nuclei_Texture_SumVariance_GFP_3_02_256	0.03564876547376572	0.005232667016345292	0.13038351799311745	-0.13038351799311745
+Nuclei_Texture_SumVariance_GFP_3_03_256	0.03577304462277453	0.005231530725398917	0.13075380597096478	-0.13075380597096478
+Nuclei_Texture_SumVariance_RFP_3_00_256	0.01535116913675083	0.005186376350497696	0.058717373519147464	-0.05871737351914747
+Nuclei_Texture_SumVariance_RFP_3_01_256	0.014465159793033755	0.004969407970777743	0.058085452219236625	-0.05808545221923663
+Nuclei_Texture_SumVariance_RFP_3_02_256	0.0161224505198464	0.005201449913393583	0.06206894045958364	-0.062068940459583645
+Nuclei_Texture_SumVariance_RFP_3_03_256	0.01474037680586504	0.005020191384072531	0.05857326155898428	-0.058573261558984284
+Nuclei_Texture_Variance_DAPI_3_00_256	0.03460058836811897	0.006194885204903445	0.11339633594698306	-0.11339633594698308
+Nuclei_Texture_Variance_DAPI_3_01_256	0.03503777502469385	0.006274130875189517	0.11350623069663618	-0.11350623069663619
+Nuclei_Texture_Variance_DAPI_3_02_256	0.03474024867820924	0.006313748852363251	0.11202366635684857	-0.11202366635684857
+Nuclei_Texture_Variance_DAPI_3_03_256	0.03557615886045096	0.006269401248223783	0.1151668980499932	-0.11516689804999318
+Nuclei_Texture_Variance_GFP_3_00_256	0.0371656471141536	0.005475137033983533	0.131271328348614	-0.131271328348614
+Nuclei_Texture_Variance_GFP_3_01_256	0.036853627332568806	0.005420296587689568	0.13116712188939617	-0.1311671218893962
+Nuclei_Texture_Variance_GFP_3_02_256	0.036579660977431816	0.005355268393076186	0.13130865385039692	-0.13130865385039692
+Nuclei_Texture_Variance_GFP_3_03_256	0.03669216202167713	0.005338408872389725	0.1318620636067103	-0.1318620636067103
+Nuclei_Texture_Variance_RFP_3_00_256	0.01681072800094896	0.005466063275341441	0.060794319915751016	-0.06079431991575101
+Nuclei_Texture_Variance_RFP_3_01_256	0.01667551622626884	0.005452171544141244	0.06041224895235926	-0.06041224895235927
+Nuclei_Texture_Variance_RFP_3_02_256	0.017188407744354484	0.005464913338306577	0.06251798317572724	-0.06251798317572724
+Nuclei_Texture_Variance_RFP_3_03_256	0.016579500973593064	0.0053682422786007706	0.06138389437330928	-0.06138389437330929
diff --git a/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate3.tsv b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate3.tsv
new file mode 100644
index 0000000..e0e10f1
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate3.tsv
@@ -0,0 +1,2305 @@
+feature	r2_score	cell_count_coef	Null_coef	WT_coef
+Cytoplasm_AreaShape_Area	0.10664234694832819	-0.0017149094069903315	-0.05997327800729947	0.059973278007299485
+Cytoplasm_AreaShape_BoundingBoxArea	0.10512900975842321	-0.0016093691760082928	-0.08117070357438459	0.0811707035743846
+Cytoplasm_AreaShape_BoundingBoxMaximum_X	0.001132039866595158	-0.0001467872719718856	-0.01905408552828943	0.019054085528289435
+Cytoplasm_AreaShape_BoundingBoxMaximum_Y	0.0038702787009923245	-0.0003248598945843522	-0.011759623868523778	0.01175962386852378
+Cytoplasm_AreaShape_BoundingBoxMinimum_X	0.000932689262473696	0.00016247610686461345	-0.0009557397287287468	0.000955739728728747
+Cytoplasm_AreaShape_BoundingBoxMinimum_Y	0.001238217858931634	0.00017943139438753264	0.009985641577551678	-0.00998564157755168
+Cytoplasm_AreaShape_Center_X	0.00011066365420253632	1.0800575347351357e-05	-0.010311360994365328	0.01031136099436533
+Cytoplasm_AreaShape_Center_Y	0.00017580758238866512	-7.03422061364751e-05	-0.0012281282038893154	0.0012281282038893154
+Cytoplasm_AreaShape_Compactness	0.1162975690710516	0.0017984549285851776	-0.05881694923893023	0.05881694923893024
+Cytoplasm_AreaShape_ConvexArea	0.08742428990162365	-0.0014886437958129863	-0.07366218781421435	0.07366218781421435
+Cytoplasm_AreaShape_Eccentricity	0.008163876662763303	-0.00047902502506785367	-0.01029881343042744	0.010298813430427442
+Cytoplasm_AreaShape_EquivalentDiameter	0.10733349617223487	-0.0016892873617457157	-0.057547975518492944	0.05754797551849295
+Cytoplasm_AreaShape_EulerNumber	0.0013802226805010864	0.00021771767042402598	-0.018504490432831908	0.01850449043283191
+Cytoplasm_AreaShape_Extent	0.004533332294454073	-0.00023104939971622723	0.05085352407466365	-0.050853524074663654
+Cytoplasm_AreaShape_FormFactor	0.12650486563380559	-0.001869948085699541	0.09049887645328916	-0.09049887645328918
+Cytoplasm_AreaShape_MajorAxisLength	0.08483343064431836	-0.001442008535210248	-0.06362546836347024	0.06362546836347024
+Cytoplasm_AreaShape_MaxFeretDiameter	0.11450264747890626	-0.001697110714067671	-0.08908489964724861	0.08908489964724862
+Cytoplasm_AreaShape_MaximumRadius	0.13417596734780834	-0.0019278111337948452	0.01427461185224881	-0.01427461185224881
+Cytoplasm_AreaShape_MeanRadius	0.1583877080450622	-0.0020977897947299456	0.020209588797803765	-0.020209588797803768
+Cytoplasm_AreaShape_MedianRadius	0.16219556524502599	-0.0021369515045611773	0.012820039482977372	-0.012820039482977373
+Cytoplasm_AreaShape_MinFeretDiameter	0.03830693518066142	-0.0010068409402326213	-0.041451885772290975	0.04145188577229098
+Cytoplasm_AreaShape_MinorAxisLength	0.02250435780450888	-0.0007537291853763868	-0.045070047553886744	0.04507004755388675
+Cytoplasm_AreaShape_Orientation	0.0006999836717176411	2.5464885129687066e-05	0.02602575724852569	-0.02602575724852569
+Cytoplasm_AreaShape_Perimeter	0.010380357708871246	-0.0001974728759655949	-0.09426369984571986	0.09426369984571989
+Cytoplasm_AreaShape_Solidity	0.034492946587009854	-0.0009462239534241074	0.035314987401597706	-0.035314987401597706
+Cytoplasm_AreaShape_Zernike_0_0	0.004731811330388691	0.0001956389289222896	0.05804071769904938	-0.058040717699049386
+Cytoplasm_AreaShape_Zernike_1_1	0.003657614748765803	-0.0001485670217082923	0.0542096703177327	-0.05420967031773272
+Cytoplasm_AreaShape_Zernike_2_0	0.009749990832027544	-0.0005144230696131536	-0.008833572144824726	0.008833572144824726
+Cytoplasm_AreaShape_Zernike_2_2	0.007949690046160196	2.9871842607667138e-05	0.08910824662139774	-0.08910824662139775
+Cytoplasm_AreaShape_Zernike_3_1	0.0034417221975303613	-0.0001748103568891879	0.048891460918469146	-0.04889146091846915
+Cytoplasm_AreaShape_Zernike_3_3	0.0033619254072944083	0.00019538869540860815	0.04504882443833007	-0.045048824438330075
+Cytoplasm_AreaShape_Zernike_4_0	0.0018265429530089916	0.00019654307601098186	-0.02253475480225188	0.022534754802251884
+Cytoplasm_AreaShape_Zernike_4_2	0.004551434907141272	-0.00023428055647289414	0.05119046338833494	-0.051190463388334946
+Cytoplasm_AreaShape_Zernike_4_4	0.007821317104240277	1.7090435543178157e-05	0.0881020463515853	-0.0881020463515853
+Cytoplasm_AreaShape_Zernike_5_1	0.0041268554427589255	0.00034835404137090203	-0.0014082874730454364	0.0014082874730454366
+Cytoplasm_AreaShape_Zernike_5_3	0.0017768466802428051	0.00016297767515958554	0.02854307506089248	-0.028543075060892487
+Cytoplasm_AreaShape_Zernike_5_5	0.004664965290564593	0.00023547789350441062	0.05173951023943646	-0.051739510239436466
+Cytoplasm_AreaShape_Zernike_6_0	0.00428819564353311	0.00035440389664917476	-0.008855618412369096	0.008855618412369098
+Cytoplasm_AreaShape_Zernike_6_2	0.00020329341460134653	6.762596615548567e-05	-0.0065527227625230625	0.006552722762523063
+Cytoplasm_AreaShape_Zernike_6_4	0.0029124728979975645	-7.264784976211664e-05	0.05195751207060288	-0.05195751207060289
+Cytoplasm_AreaShape_Zernike_6_6	0.006034857032779395	9.62979146791953e-05	0.07548733973780226	-0.07548733973780228
+Cytoplasm_AreaShape_Zernike_7_1	0.004959122508233271	0.00037890184155759006	-0.0017657413648453934	0.0017657413648453936
+Cytoplasm_AreaShape_Zernike_7_3	0.00013197815650656253	5.931323109108603e-05	-0.0034852123510880724	0.0034852123510880732
+Cytoplasm_AreaShape_Zernike_7_5	0.001637397977954702	0.0001681844733200796	0.024425149400672405	-0.02442514940067241
+Cytoplasm_AreaShape_Zernike_7_7	0.004236770932412037	0.00015993391432731114	0.0578088263948026	-0.05780882639480261
+Cytoplasm_AreaShape_Zernike_8_0	0.0008788841475450004	0.00016202466254036364	0.0013448355291375894	-0.0013448355291375896
+Cytoplasm_AreaShape_Zernike_8_2	4.147327616332852e-06	-7.5183161150437185e-06	-0.0015088855893207404	0.0015088855893207406
+Cytoplasm_AreaShape_Zernike_8_4	4.288529354856685e-06	-1.0247549282942717e-05	0.0008083815196783851	-0.0008083815196783852
+Cytoplasm_AreaShape_Zernike_8_6	0.0015234315671752707	-4.4301254936817155e-05	0.037714374335905514	-0.03771437433590552
+Cytoplasm_AreaShape_Zernike_8_8	0.004767559847493885	5.027361188420365e-05	0.06836858622624624	-0.06836858622624625
+Cytoplasm_AreaShape_Zernike_9_1	0.00021899622616738945	4.5302827874972664e-05	0.012289374593075969	-0.01228937459307597
+Cytoplasm_AreaShape_Zernike_9_3	0.0009264414704036605	-6.0330229054226825e-05	-0.028653428930248588	0.028653428930248595
+Cytoplasm_AreaShape_Zernike_9_5	1.228150530008243e-05	-1.8586426916190816e-05	0.0004883061132145961	-0.0004883061132145961
+Cytoplasm_AreaShape_Zernike_9_7	0.0019798527551415646	0.000158977011211403	0.03261154529130667	-0.03261154529130668
+Cytoplasm_AreaShape_Zernike_9_9	0.004084250360268715	0.00021343404994273121	0.050438856164591164	-0.05043885616459117
+Cytoplasm_Correlation_Correlation_CY5_DAPI	0.017552105491557057	-9.218280556618948e-05	0.11746092509122893	-0.11746092509122896
+Cytoplasm_Correlation_Correlation_CY5_GFP	0.04237140013367402	-0.0002835684252191863	0.20118218213666955	-0.20118218213666958
+Cytoplasm_Correlation_Correlation_CY5_RFP	0.0814740985920187	-0.0015218137130505664	0.018406945312955798	-0.018406945312955798
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.05627346287508139	-0.0011325314455773855	0.041147152185117566	-0.04114715218511757
+Cytoplasm_Correlation_Correlation_DAPI_RFP	0.08443622161510422	-0.0013541702662857914	0.06283661607856314	-0.06283661607856315
+Cytoplasm_Correlation_Correlation_GFP_RFP	0.07967722500499275	-0.0015017824049829934	0.026711086140989514	-0.026711086140989514
+Cytoplasm_Correlation_Costes_CY5_DAPI	0.04000695130385634	0.0007174663316886649	0.14919762733491265	-0.14919762733491268
+Cytoplasm_Correlation_Costes_CY5_GFP	0.04362832060783295	0.0006113009520617077	0.20309132473460678	-0.2030913247346068
+Cytoplasm_Correlation_Costes_CY5_RFP	0.09620256090197399	-0.0011695378150600939	0.2750415201072618	-0.2750415201072618
+Cytoplasm_Correlation_Costes_DAPI_CY5	0.027388411268011836	0.00021545591479962494	0.18375917212021609	-0.1837591721202161
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.2930774526438561	-0.0024423243565451294	0.11849214177696357	-0.11849214177696359
+Cytoplasm_Correlation_Costes_DAPI_RFP	0.34159451974194743	-0.0032624272466917404	0.03668687282569207	-0.03668687282569207
+Cytoplasm_Correlation_Costes_GFP_CY5	0.03501973374287115	0.00023153709975289196	0.2046484922202596	-0.20464849222025963
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.08976641320016887	-0.0018281675772961958	0.15108091255500072	-0.15108091255500075
+Cytoplasm_Correlation_Costes_GFP_RFP	0.1859092106205723	-0.0024469504608247566	0.27350685301835076	-0.2735068530183508
+Cytoplasm_Correlation_Costes_RFP_CY5	0.3146285045859103	-0.003041259145631265	0.21402492315334748	-0.2140249231533475
+Cytoplasm_Correlation_Costes_RFP_DAPI	0.22354815896463331	-0.0025232778692763523	-0.09496584429815419	0.0949658442981542
+Cytoplasm_Correlation_Costes_RFP_GFP	0.26952500448280303	-0.0029702323952448227	0.2746275249657603	-0.2746275249657604
+Cytoplasm_Correlation_K_CY5_DAPI	0.03512780120275938	-0.0002970585239551923	-0.20418403719810146	0.2041840371981015
+Cytoplasm_Correlation_K_CY5_GFP	0.02933359627121468	-0.00043659234957694944	-0.1453702846424102	0.14537028464241022
+Cytoplasm_Correlation_K_CY5_RFP	0.03516942666245637	-0.00046089583416516095	-0.16365228900762432	0.16365228900762435
+Cytoplasm_Correlation_K_DAPI_CY5	0.13205066885599792	-0.0018915774975332385	-0.00294645887460839	0.0029464588746083904
+Cytoplasm_Correlation_K_DAPI_GFP	0.13433843031771275	-0.0016790316838137796	0.14918683922540465	-0.14918683922540468
+Cytoplasm_Correlation_K_DAPI_RFP	0.07609190464036109	-0.0012709199405745355	0.04628556220718659	-0.0462855622071866
+Cytoplasm_Correlation_K_GFP_CY5	0.11593895116967101	-0.0016432864809628083	-0.1878390858463988	0.18783908584639883
+Cytoplasm_Correlation_K_GFP_DAPI	0.14775405646312256	0.0018295268171642277	-0.14708935233866538	0.1470893523386654
+Cytoplasm_Correlation_K_GFP_RFP	0.04432025096626235	-0.0004949534948097725	-0.18119597623214495	0.18119597623214498
+Cytoplasm_Correlation_K_RFP_CY5	0.020384597252641967	-0.0008024900361592239	0.016165456457147464	-0.016165456457147467
+Cytoplasm_Correlation_K_RFP_DAPI	0.10343921350815499	0.0016599991655958867	-0.07194530490280036	0.07194530490280038
+Cytoplasm_Correlation_K_RFP_GFP	0.026514207901724873	0.00036198014095207507	0.1461307212023606	-0.14613072120236062
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+Cytoplasm_Correlation_Overlap_CY5_DAPI	0.0031775806981196864	0.00025845600143707356	-0.025387142727657228	0.02538714272765723
+Cytoplasm_Correlation_Overlap_CY5_GFP	0.024293804206854674	-0.0006446337384351514	0.10991517743073659	-0.1099151774307366
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+Cytoplasm_Correlation_RWC_CY5_DAPI	0.18325934690614198	0.0017283589111847001	0.0712977335541587	-0.07129773355415871
+Cytoplasm_Correlation_RWC_CY5_GFP	0.23079289943599612	0.0017634416002168028	0.3405686518034686	-0.3405686518034687
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+Cytoplasm_Correlation_RWC_GFP_CY5	0.01681763032916861	-0.0006900296962947923	0.00352559998648285	-0.003525599986482851
+Cytoplasm_Correlation_RWC_GFP_DAPI	0.049378491474534436	0.0008944711406843016	-0.026835388015488792	0.026835388015488792
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+Cytoplasm_Correlation_RWC_RFP_CY5	0.007219223486818249	-1.2479588691645551e-05	-0.08301123234722226	0.08301123234722227
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+Cytoplasm_Correlation_RWC_RFP_GFP	0.11710475959374633	0.0014220990967538259	0.21064185806383445	-0.21064185806383448
+Cytoplasm_Granularity_10_CY5	8.944867491633701e-05	3.6457510783617124e-05	0.006384607591835737	-0.006384607591835738
+Cytoplasm_Granularity_10_DAPI	0.0006454514301434955	8.64959444709572e-05	0.01775461699833741	-0.01775461699833741
+Cytoplasm_Granularity_10_GFP	0.003101028844528231	-0.00025643322207439883	0.029718999100080794	-0.029718999100080797
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+Cytoplasm_Granularity_11_CY5	0.0006346668748872242	-7.801509667645385e-05	0.020468197444090015	-0.02046819744409002
+Cytoplasm_Granularity_11_DAPI	0.0032922999476543424	-9.001893878097279e-05	0.04091148863554403	-0.04091148863554404
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+Cytoplasm_Granularity_13_CY5	0.0028285036223109072	9.700711739380422e-05	0.04980095012784379	-0.049800950127843795
+Cytoplasm_Granularity_13_DAPI	0.01160440592059453	-0.0003961080667818657	0.04801993297722368	-0.048019932977223685
+Cytoplasm_Granularity_13_GFP	0.0071620784760358935	-0.00042035836011039166	-0.02669471118355022	0.026694711183550223
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+Cytoplasm_Granularity_14_CY5	0.00019820783941282283	1.4026810400127963e-06	-0.014114046030607317	0.01411404603060732
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+Cytoplasm_Granularity_15_CY5	0.00045108066675991765	-6.940153128084037e-05	-0.016577822357934438	0.016577822357934438
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+Cytoplasm_Intensity_LowerQuartileIntensity_CY5	0.08361133182677971	-0.0013075673331273956	-0.06922413307078057	0.06922413307078057
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+Nuclei_Correlation_RWC_CY5_DAPI	0.024827512711930955	-0.0008522385374495586	0.009482739049260573	-0.009482739049260573
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+Nuclei_Correlation_RWC_RFP_GFP	0.033615378408585084	0.00021217135768054137	0.18183251004090922	-0.18183251004090928
+Nuclei_Granularity_10_CY5	0.0003550308267286084	7.645225204994447e-05	0.012338476157897571	-0.012338476157897573
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+Nuclei_Granularity_10_GFP	0.00421734144607655	-0.0003383142094053243	0.004008202570094387	-0.004008202570094388
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+Nuclei_Granularity_12_CY5	0.00071765119108258	6.121368586212104e-05	0.02429339506119447	-0.024293395061194472
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+Nuclei_Granularity_12_GFP	0.003815179893185139	-0.0002804756930307576	-0.03192039490990883	0.03192039490990883
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+Nuclei_Granularity_14_CY5	0.0010830714863574453	0.00013224857640930807	-0.022987441034823378	0.02298744103482338
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+Nuclei_Granularity_15_CY5	0.0006347934566867108	8.691857634205238e-05	-0.019561101723913434	0.019561101723913437
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+Nuclei_Granularity_15_GFP	0.002464930113056707	0.0001826066569933643	-0.03969070371383631	0.03969070371383632
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+Nuclei_Texture_SumEntropy_RFP_3_00_256	0.013991521867018575	-0.0003151731591044528	0.10429828574846342	-0.10429828574846343
+Nuclei_Texture_SumEntropy_RFP_3_01_256	0.016225613065169897	-0.0003480332506190544	0.11151111392660848	-0.11151111392660848
+Nuclei_Texture_SumEntropy_RFP_3_02_256	0.016505143928725263	-0.0003742034756653831	0.10997027110209162	-0.10997027110209164
+Nuclei_Texture_SumEntropy_RFP_3_03_256	0.014596407471409445	-0.0003468686677155788	0.10352767885805761	-0.10352767885805764
+Nuclei_Texture_SumVariance_CY5_3_00_256	0.048068406669377284	-0.001100692324975611	0.06766300396744475	-0.06766300396744476
+Nuclei_Texture_SumVariance_CY5_3_01_256	0.045406339869377366	-0.0010706751978106928	0.06469261744644873	-0.06469261744644875
+Nuclei_Texture_SumVariance_CY5_3_02_256	0.047145411896055034	-0.0010987305555599323	0.06532939505311317	-0.06532939505311318
+Nuclei_Texture_SumVariance_CY5_3_03_256	0.04607882703261912	-0.001076964776628182	0.06401403856694467	-0.06401403856694468
+Nuclei_Texture_SumVariance_DAPI_3_00_256	0.005032324108947939	8.359618972965593e-05	0.06796986082452579	-0.06796986082452579
+Nuclei_Texture_SumVariance_DAPI_3_01_256	0.00518082335785941	6.193801263528185e-05	0.06968940858046709	-0.0696894085804671
+Nuclei_Texture_SumVariance_DAPI_3_02_256	0.0052679489140048386	6.438875154873828e-05	0.07043195659392625	-0.07043195659392625
+Nuclei_Texture_SumVariance_DAPI_3_03_256	0.004878614522790481	5.823253425212752e-05	0.06736235936317883	-0.06736235936317884
+Nuclei_Texture_SumVariance_GFP_3_00_256	0.06048057098287707	-0.0009859889646048742	0.1451855016817567	-0.14518550168175673
+Nuclei_Texture_SumVariance_GFP_3_01_256	0.05837429847957043	-0.0009671040993279473	0.14144229033424446	-0.1414422903342445
+Nuclei_Texture_SumVariance_GFP_3_02_256	0.059704357040264444	-0.000980228374882671	0.14399982435990327	-0.1439998243599033
+Nuclei_Texture_SumVariance_GFP_3_03_256	0.058344155960684874	-0.0009717244343182883	0.1421772739991264	-0.14217727399912644
+Nuclei_Texture_SumVariance_RFP_3_00_256	0.017917303446481903	-0.0005374592226237843	0.090221402998208	-0.09022140299820801
+Nuclei_Texture_SumVariance_RFP_3_01_256	0.017813967162375155	-0.0005226760974804022	0.09274719353274513	-0.09274719353274515
+Nuclei_Texture_SumVariance_RFP_3_02_256	0.018651354023156763	-0.0005566083766221327	0.0908602994550039	-0.09086029945500392
+Nuclei_Texture_SumVariance_RFP_3_03_256	0.017600206543347374	-0.0005365838149580652	0.0889568431669501	-0.08895684316695011
+Nuclei_Texture_Variance_CY5_3_00_256	0.05012733116991608	-0.0011297012949001972	0.0700836307315177	-0.0700836307315177
+Nuclei_Texture_Variance_CY5_3_01_256	0.049202575270351656	-0.0011200676264929892	0.06874035889346827	-0.06874035889346829
+Nuclei_Texture_Variance_CY5_3_02_256	0.04975047710516811	-0.0011297348766008638	0.06872835003916772	-0.06872835003916773
+Nuclei_Texture_Variance_CY5_3_03_256	0.049523746591872975	-0.0011231926597082813	0.06863527144255496	-0.06863527144255498
+Nuclei_Texture_Variance_DAPI_3_00_256	0.005223240052904976	9.921286706486801e-05	0.06934962296249135	-0.06934962296249136
+Nuclei_Texture_Variance_DAPI_3_01_256	0.005238223448668533	8.856719777627497e-05	0.07002935963165442	-0.07002935963165444
+Nuclei_Texture_Variance_DAPI_3_02_256	0.005269955096405821	9.542647917892795e-05	0.06987646544034208	-0.0698764654403421
+Nuclei_Texture_Variance_DAPI_3_03_256	0.005142481816536204	9.03923275406233e-05	0.06921373745306818	-0.06921373745306819
+Nuclei_Texture_Variance_GFP_3_00_256	0.06261200919024301	-0.0009987054240167877	0.1491313613042227	-0.14913136130422272
+Nuclei_Texture_Variance_GFP_3_01_256	0.06191979404572834	-0.0009894159407262898	0.14804947142558383	-0.14804947142558383
+Nuclei_Texture_Variance_GFP_3_02_256	0.06235313370892914	-0.0009975550893980153	0.1484467192243653	-0.14844671922436534
+Nuclei_Texture_Variance_GFP_3_03_256	0.061911137594040655	-0.0009932909564910502	0.14848995495752224	-0.14848995495752226
+Nuclei_Texture_Variance_RFP_3_00_256	0.018195642390220423	-0.0005585915400387743	0.08764472718950608	-0.0876447271895061
+Nuclei_Texture_Variance_RFP_3_01_256	0.017985274008545105	-0.0005538629677374235	0.08767491080548835	-0.08767491080548837
+Nuclei_Texture_Variance_RFP_3_02_256	0.018114788101014745	-0.0005592016498433078	0.08710756457424257	-0.0871075645742426
+Nuclei_Texture_Variance_RFP_3_03_256	0.018060779958382955	-0.0005590134051315006	0.08688391648032484	-0.08688391648032485
diff --git a/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate3prime.tsv b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate3prime.tsv
new file mode 100644
index 0000000..22bd14e
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate3prime.tsv
@@ -0,0 +1,2305 @@
+feature	r2_score	cell_count_coef	Null_coef	WT_coef
+Cytoplasm_AreaShape_Area	0.07381947303390957	-0.00213274929908443	-0.0007243198572969385	0.0007243198572969554
+Cytoplasm_AreaShape_BoundingBoxArea	0.0858264933445625	-0.00232531501983053	-0.042393470248863425	0.04239347024886441
+Cytoplasm_AreaShape_BoundingBoxMaximum_X	0.001421692507975525	-0.0002909551757856745	-0.0016196012564932798	0.001619601256493317
+Cytoplasm_AreaShape_BoundingBoxMaximum_Y	0.003669523465919866	-0.00041498226585122373	-0.03625706452153099	0.03625706452153183
+Cytoplasm_AreaShape_BoundingBoxMinimum_X	0.0007498070110230071	0.00020994041267641145	0.008201942036804303	-0.008201942036804494
+Cytoplasm_AreaShape_BoundingBoxMinimum_Y	0.0020148517042239966	0.00026744113541942165	-0.022495343728381564	0.022495343728382088
+Cytoplasm_AreaShape_Center_X	3.511921801635509e-05	-3.024402397983145e-05	0.0037727397243299743	-0.003772739724330062
+Cytoplasm_AreaShape_Center_Y	0.0008529171761751941	-6.943504902986153e-05	-0.02883070346374706	0.028830703463747727
+Cytoplasm_AreaShape_Compactness	0.09769768815918822	0.0022719363212659743	-0.10153296839857268	0.10153296839857504
+Cytoplasm_AreaShape_ConvexArea	0.0697599442294925	-0.0020755594727060525	-0.024137886578500495	0.024137886578501057
+Cytoplasm_AreaShape_Eccentricity	0.005857562561662255	-0.0005488535362691276	-0.04442070439156773	0.04442070439156876
+Cytoplasm_AreaShape_EquivalentDiameter	0.07361326511826127	-0.0020387280312727575	-0.004669163588442403	0.004669163588442511
+Cytoplasm_AreaShape_EulerNumber	0.0018176860020727714	0.0001556259960217341	0.03221994204047111	-0.032219942040471856
+Cytoplasm_AreaShape_Extent	0.002595032974398892	4.559089648424139e-05	0.051558761900457814	-0.05155876190045901
+Cytoplasm_AreaShape_FormFactor	0.10809635720537447	-0.0022457655517148446	0.12348019031286858	-0.12348019031287145
+Cytoplasm_AreaShape_MajorAxisLength	0.06535399868918979	-0.0019552985972783954	-0.03931034517142853	0.039310345171429444
+Cytoplasm_AreaShape_MaxFeretDiameter	0.08813502212883073	-0.002303325706707423	-0.06375081804167271	0.06375081804167418
+Cytoplasm_AreaShape_MaximumRadius	0.0920647999148082	-0.0021013112211885608	0.0670815899488298	-0.06708158994883134
+Cytoplasm_AreaShape_MeanRadius	0.11802893880062215	-0.0024002301948603437	0.0778672236362202	-0.077867223636222
+Cytoplasm_AreaShape_MedianRadius	0.12315604362669652	-0.0025228103397198884	0.06997213310903257	-0.06997213310903418
+Cytoplasm_AreaShape_MinFeretDiameter	0.028356666191045377	-0.0012691194504462855	0.015552084802407266	-0.01555208480240763
+Cytoplasm_AreaShape_MinorAxisLength	0.01683830271309361	-0.0009570494565442445	0.024578899587148352	-0.02457889958714892
+Cytoplasm_AreaShape_Orientation	0.0004149906564872019	4.1654729738192485e-05	0.020556624053243662	-0.020556624053244134
+Cytoplasm_AreaShape_Perimeter	0.0055526798457558035	-0.00012440548168337292	-0.07710601817256232	0.0771060181725641
+Cytoplasm_AreaShape_Solidity	0.010363397645250183	-0.0006671978516517794	0.03547309994593129	-0.03547309994593212
+Cytoplasm_AreaShape_Zernike_0_0	0.007894480042128449	0.00048768428937448705	0.07523686767308885	-0.0752368676730906
+Cytoplasm_AreaShape_Zernike_1_1	0.0022514511565004947	-0.0001158096298228968	0.04214763569179811	-0.042147635691799086
+Cytoplasm_AreaShape_Zernike_2_0	0.0011870858877376378	-0.0002471347300099832	0.0057537317067089845	-0.005753731706709119
+Cytoplasm_AreaShape_Zernike_2_2	0.007310284575787751	0.0003827697478449674	0.07684288570316555	-0.07684288570316734
+Cytoplasm_AreaShape_Zernike_3_1	0.0031965603402401843	-0.00022082271943591595	0.043405918077366094	-0.04340591807736711
+Cytoplasm_AreaShape_Zernike_3_3	0.0060611477454303175	0.00020390875259193884	0.07676802567156397	-0.07676802567156576
+Cytoplasm_AreaShape_Zernike_4_0	3.530544184127127e-05	1.4385660416125102e-05	-0.005334113837263659	0.005334113837263783
+Cytoplasm_AreaShape_Zernike_4_2	0.0030942835738402286	0.00032824793773810536	0.04290520654047081	-0.04290520654047181
+Cytoplasm_AreaShape_Zernike_4_4	0.008789391633058252	0.00023434995474529215	0.09283642993389074	-0.09283642993389289
+Cytoplasm_AreaShape_Zernike_5_1	0.0013633155602861269	0.00028360427675378354	-0.0005717935263154329	0.0005717935263154461
+Cytoplasm_AreaShape_Zernike_5_3	0.0030946859409107796	0.0002161088622868644	0.05187619063229612	-0.05187619063229732
+Cytoplasm_AreaShape_Zernike_5_5	0.00785021002125108	0.00027804299998784675	0.08780933472559396	-0.087809334725596
+Cytoplasm_AreaShape_Zernike_6_0	0.0007724421266385173	0.00022092470048741818	0.006473883101617827	-0.006473883101617977
+Cytoplasm_AreaShape_Zernike_6_2	0.00023989332842899724	0.00010340019225394236	0.010515617654200555	-0.010515617654200799
+Cytoplasm_AreaShape_Zernike_6_4	0.004155969199535092	9.940329409149325e-05	0.0646766806222324	-0.0646766806222339
+Cytoplasm_AreaShape_Zernike_6_6	0.008246998753596246	0.0002690377775740584	0.08898699081899608	-0.08898699081899815
+Cytoplasm_AreaShape_Zernike_7_1	0.002422786238637009	0.00038605604966421303	0.014411066970611387	-0.014411066970611723
+Cytoplasm_AreaShape_Zernike_7_3	0.00013858520911624872	-7.484256119796848e-05	0.005419463423902811	-0.005419463423902937
+Cytoplasm_AreaShape_Zernike_7_5	0.004905507673845233	0.00026577068051901736	0.06769036626316369	-0.06769036626316524
+Cytoplasm_AreaShape_Zernike_7_7	0.005987534111409731	0.0003453915871549886	0.07097445879094297	-0.07097445879094462
+Cytoplasm_AreaShape_Zernike_8_0	0.0002773716970767559	0.0001317784647337129	0.0005785313014688092	-0.0005785313014688226
+Cytoplasm_AreaShape_Zernike_8_2	7.26361980668555e-05	-5.7884041929499894e-05	0.003174143084463713	-0.0031741430844637866
+Cytoplasm_AreaShape_Zernike_8_4	0.0003162963781055961	-0.00013074498055169634	0.0033911176401367473	-0.003391117640136827
+Cytoplasm_AreaShape_Zernike_8_6	0.003425173756821276	9.727791080761834e-05	0.05924639233168774	-0.05924639233168911
+Cytoplasm_AreaShape_Zernike_8_8	0.005464947754136307	0.00023511147098066325	0.07185074472188203	-0.07185074472188369
+Cytoplasm_AreaShape_Zernike_9_1	0.0007550667787150633	0.00013014838779739225	0.025466149114830236	-0.02546614911483083
+Cytoplasm_AreaShape_Zernike_9_3	0.00026207127913457917	-7.766655046333798e-05	-0.01469603860318075	0.014696038603181092
+Cytoplasm_AreaShape_Zernike_9_5	0.00042654966097155356	-8.723848620535806e-06	0.02064903962123284	-0.020649039621233323
+Cytoplasm_AreaShape_Zernike_9_7	0.00422250681215397	0.0003623670686487014	0.05406662344842798	-0.05406662344842924
+Cytoplasm_AreaShape_Zernike_9_9	0.005025482940022696	0.00023178653096855351	0.06975563953282579	-0.06975563953282742
+Cytoplasm_Correlation_Correlation_CY5_DAPI	0.020441764746980562	-0.00026388278834276445	0.10188766372972638	-0.10188766372972875
+Cytoplasm_Correlation_Correlation_CY5_GFP	0.04812426726097707	-0.0011937123880303038	0.14001309766313147	-0.14001309766313472
+Cytoplasm_Correlation_Correlation_CY5_RFP	0.08556431624134897	-0.0021594881547820155	0.014279348034591775	-0.014279348034592106
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.034827915836024625	-0.0011975981504400428	-0.010680422112044804	0.010680422112045052
+Cytoplasm_Correlation_Correlation_DAPI_RFP	0.08415922278083743	-0.001861100406667965	0.011858060571983596	-0.01185806057198387
+Cytoplasm_Correlation_Correlation_GFP_RFP	0.09915833556136244	-0.0023152239340975735	0.03494672474077241	-0.03494672474077322
+Cytoplasm_Correlation_Costes_CY5_DAPI	0.0053766199063250975	0.0006186758821656406	0.020782512586425633	-0.020782512586426112
+Cytoplasm_Correlation_Costes_CY5_GFP	0.035565464906499145	-0.0016682257222275134	-0.028135180076293634	0.028135180076294293
+Cytoplasm_Correlation_Costes_CY5_RFP	0.27363135833534336	-0.004343356063057743	0.07641325982774112	-0.07641325982774289
+Cytoplasm_Correlation_Costes_DAPI_CY5	0.18224468696852647	-0.003353753253259113	-0.0373669081676282	0.037366908167629075
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.3166585374000769	-0.003647523266041334	-0.009279376552643446	0.00927937655264366
+Cytoplasm_Correlation_Costes_DAPI_RFP	0.4362913041143035	-0.005518774920381737	0.035410459073283274	-0.03541045907328409
+Cytoplasm_Correlation_Costes_GFP_CY5	0.007870560606027976	0.000705072484358132	0.007967478881963343	-0.007967478881963527
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.0052275537330728206	0.0004264490020169159	-0.06530677514831158	0.0653067751483131
+Cytoplasm_Correlation_Costes_GFP_RFP	0.08018397005200006	-0.0026063054616838038	0.0824859965473924	-0.08248599654739432
+Cytoplasm_Correlation_Costes_RFP_CY5	0.10138596721577187	-0.002814555496060129	-0.006330074557841899	0.006330074557842047
+Cytoplasm_Correlation_Costes_RFP_DAPI	0.34885038134328183	-0.004739842259289048	-0.05207956322104028	0.05207956322104149
+Cytoplasm_Correlation_Costes_RFP_GFP	0.16826450472896115	-0.0037174634865739277	0.07025743649148583	-0.07025743649148745
+Cytoplasm_Correlation_K_CY5_DAPI	0.1454747828497146	-0.003108516516676932	-0.03875111862886687	0.03875111862886777
+Cytoplasm_Correlation_K_CY5_GFP	0.21282401919913274	-0.003651787392475919	0.01443435181420755	-0.014434351814207883
+Cytoplasm_Correlation_K_CY5_RFP	0.23163596278894816	-0.0035930318089331566	-0.04124986565463097	0.04124986565463193
+Cytoplasm_Correlation_K_DAPI_CY5	0.036296549293442615	0.0013399651599762096	0.035807488897704846	-0.03580748889770568
+Cytoplasm_Correlation_K_DAPI_GFP	0.09554381575063353	-0.001995411772221629	0.06844751589558554	-0.06844751589558713
+Cytoplasm_Correlation_K_DAPI_RFP	0.09793468218578971	-0.0020836443652326978	-0.013849429945851054	0.013849429945851376
+Cytoplasm_Correlation_K_GFP_CY5	0.22513472792979694	0.003377489714354304	-0.09380265247894165	0.09380265247894382
+Cytoplasm_Correlation_K_GFP_DAPI	0.13036838399338468	0.0024073889974273715	-0.08337276994354437	0.08337276994354631
+Cytoplasm_Correlation_K_GFP_RFP	0.04247971647918036	-0.0012716017088917051	-0.14079426177079957	0.14079426177080284
+Cytoplasm_Correlation_K_RFP_CY5	0.36635026884422794	0.00438193311190693	0.023509739636107946	-0.02350973963610849
+Cytoplasm_Correlation_K_RFP_DAPI	0.13705568181997563	0.00267473933949315	0.005345110698808373	-0.0053451106988084965
+Cytoplasm_Correlation_K_RFP_GFP	0.028531372622138007	0.0009697875415948738	0.11709204108849999	-0.11709204108850271
+Cytoplasm_Correlation_Manders_CY5_DAPI	0.004342862432378025	0.0002814771971915468	0.017453835852512655	-0.01745383585251306
+Cytoplasm_Correlation_Manders_CY5_GFP	0.11003163322232079	0.0012477454688362304	0.3104029210697375	-0.3104029210697447
+Cytoplasm_Correlation_Manders_CY5_RFP	0.1676804123282054	-0.003517708906017366	0.010860715937256557	-0.01086071593725681
+Cytoplasm_Correlation_Manders_DAPI_CY5	0.1327457072503586	0.0032505555779699245	0.06925082488863421	-0.06925082488863582
+Cytoplasm_Correlation_Manders_DAPI_GFP	0.037716497291227946	0.0003031242667169774	0.19230858215688496	-0.19230858215688945
+Cytoplasm_Correlation_Manders_DAPI_RFP	0.19670661597174766	-0.003666261278874617	-0.014577235251054598	0.014577235251054938
+Cytoplasm_Correlation_Manders_GFP_CY5	0.027036146696554963	0.0012303472046048072	-0.07152036105018868	0.07152036105019033
+Cytoplasm_Correlation_Manders_GFP_DAPI	0.0036192856725425493	-0.00011312070812707354	-0.03373091869559837	0.03373091869559915
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+Cytoplasm_Granularity_10_CY5	0.006046464521047068	0.0005723144576833454	0.02693499868163514	-0.026934998681635763
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+Nuclei_Texture_SumVariance_GFP_3_02_256	0.04020066537928113	-0.0010807025299096507	0.11261871274585306	-0.11261871274585568
+Nuclei_Texture_SumVariance_GFP_3_03_256	0.03871222186834511	-0.0010693208791023672	0.1089469109223104	-0.10894691092231293
+Nuclei_Texture_SumVariance_RFP_3_00_256	0.019344759543207024	-0.0006511661636064712	0.10074840467280913	-0.10074840467281147
+Nuclei_Texture_SumVariance_RFP_3_01_256	0.018506386866333324	-0.0006107869618980132	0.10116936302320786	-0.10116936302321022
+Nuclei_Texture_SumVariance_RFP_3_02_256	0.019387910404297437	-0.0006349403526530835	0.10256715710221523	-0.1025671571022176
+Nuclei_Texture_SumVariance_RFP_3_03_256	0.01893984183883446	-0.0006316361133430949	0.10024813562192977	-0.1002481356219321
+Nuclei_Texture_Variance_CY5_3_00_256	0.029261274481253152	0.0009262698204495097	0.11286594574160477	-0.1128659457416074
+Nuclei_Texture_Variance_CY5_3_01_256	0.029198240680044174	0.0009315817472336942	0.11204393023382864	-0.11204393023383123
+Nuclei_Texture_Variance_CY5_3_02_256	0.029609833975915212	0.0009340775256315348	0.11358819003043877	-0.11358819003044142
+Nuclei_Texture_Variance_CY5_3_03_256	0.029063011552400098	0.0009263012670246155	0.11174903447467618	-0.11174903447467877
+Nuclei_Texture_Variance_DAPI_3_00_256	0.03904898413360258	-0.0012072909704359152	0.07993086166536138	-0.07993086166536324
+Nuclei_Texture_Variance_DAPI_3_01_256	0.038607009715270646	-0.0011982438759801793	0.08048906930373874	-0.0804890693037406
+Nuclei_Texture_Variance_DAPI_3_02_256	0.03840537566703439	-0.0011915475961378397	0.08104164848730273	-0.08104164848730462
+Nuclei_Texture_Variance_DAPI_3_03_256	0.038441389444672014	-0.0011995262772326516	0.0789389260150204	-0.07893892601502223
+Nuclei_Texture_Variance_GFP_3_00_256	0.042579034774061664	-0.0011414449009330059	0.11136606971222163	-0.1113660697122242
+Nuclei_Texture_Variance_GFP_3_01_256	0.042123406171897027	-0.001132607298297486	0.11181168356670562	-0.11181168356670819
+Nuclei_Texture_Variance_GFP_3_02_256	0.04244847219613501	-0.0011281556506174614	0.11318370904820449	-0.11318370904820711
+Nuclei_Texture_Variance_GFP_3_03_256	0.04200628224321756	-0.0011340009073720683	0.11111957526191496	-0.11111957526191754
+Nuclei_Texture_Variance_RFP_3_00_256	0.01825996888359227	-0.0006618276899204001	0.0953179961996462	-0.0953179961996484
+Nuclei_Texture_Variance_RFP_3_01_256	0.017959172622945885	-0.000646063297735567	0.09596291085675743	-0.09596291085675968
+Nuclei_Texture_Variance_RFP_3_02_256	0.018122732246390827	-0.0006524888086457051	0.09588007688357111	-0.09588007688357336
+Nuclei_Texture_Variance_RFP_3_03_256	0.017927093152905194	-0.0006522243560458637	0.09510081116183242	-0.09510081116183461
diff --git a/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate4.tsv b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate4.tsv
new file mode 100644
index 0000000..a90fd11
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_plate4.tsv
@@ -0,0 +1,2305 @@
+feature	r2_score	cell_count_coef	Null_coef	WT_coef
+Cytoplasm_AreaShape_Area	0.00168464694702819	-0.0005222084968203917	0.05556549126826918	-0.05556549126826918
+Cytoplasm_AreaShape_BoundingBoxArea	0.00034410614186364263	-0.0004310662431513814	0.019286398123699502	-0.019286398123699505
+Cytoplasm_AreaShape_BoundingBoxMaximum_X	0.0003435232531942001	0.0007284854699647284	0.018553553117707004	-0.018553553117707008
+Cytoplasm_AreaShape_BoundingBoxMaximum_Y	0.0001523169512451794	-0.00023218323770282258	-0.02010835739564609	0.020108357395646093
+Cytoplasm_AreaShape_BoundingBoxMinimum_X	0.0005167113745382368	0.0009424726468150104	0.015848836323304184	-0.015848836323304187
+Cytoplasm_AreaShape_BoundingBoxMinimum_Y	0.00013271996458208424	-0.00022875689721416642	-0.01867481343616705	0.018674813436167052
+Cytoplasm_AreaShape_Center_X	0.0004395800810156336	0.0008559949589083028	0.01725568671152101	-0.01725568671152101
+Cytoplasm_AreaShape_Center_Y	0.00018011223512071073	-0.00032583802713563283	-0.02105712057591526	0.021057120575915262
+Cytoplasm_AreaShape_Compactness	0.002073868671656931	0.001514455907890559	-0.02585983295047229	0.025859832950472294
+Cytoplasm_AreaShape_ConvexArea	0.0005688947849890047	1.871777534552463e-05	0.037837640949519365	-0.03783764094951937
+Cytoplasm_AreaShape_Eccentricity	0.001340863067116227	-0.0004419171175221684	-0.06048311941596342	0.06048311941596343
+Cytoplasm_AreaShape_EquivalentDiameter	0.0013309351141816972	-0.0005287367340868433	0.04779509307582133	-0.04779509307582133
+Cytoplasm_AreaShape_EulerNumber	0.0004735756768380339	-0.0004566718537299424	-0.03504603438009232	0.03504603438009233
+Cytoplasm_AreaShape_Extent	0.0009753689867685322	-0.00010263937947193611	0.047911764953161906	-0.04791176495316191
+Cytoplasm_AreaShape_FormFactor	0.0021534905634060797	-0.001638387246601159	0.020097525385661417	-0.02009752538566142
+Cytoplasm_AreaShape_MajorAxisLength	0.00028559684751172654	-0.0005558311268750792	-0.022986495182570066	0.02298649518257007
+Cytoplasm_AreaShape_MaxFeretDiameter	0.000295584118273462	-0.0006777936496259507	-0.017019968453230228	0.01701996845323023
+Cytoplasm_AreaShape_MaximumRadius	0.00046075690431091054	-0.0003507248390885147	0.027065297880993308	-0.02706529788099331
+Cytoplasm_AreaShape_MeanRadius	0.0016095320990738848	-0.000723464497668629	0.04863531607874337	-0.04863531607874338
+Cytoplasm_AreaShape_MedianRadius	0.002908542596088637	-0.0010285204118077775	0.06367931736219605	-0.06367931736219605
+Cytoplasm_AreaShape_MinFeretDiameter	0.001459814229047951	0.00040332722037643133	0.06304423447651485	-0.06304423447651485
+Cytoplasm_AreaShape_MinorAxisLength	0.002472747208463799	7.179686381039397e-05	0.07923647724845255	-0.07923647724845256
+Cytoplasm_AreaShape_Orientation	0.0005383494639550346	0.0006075863853088933	-0.02143160216991039	0.021431602169910392
+Cytoplasm_AreaShape_Perimeter	0.00029861178972323543	0.0006581539057649094	0.018991048711291997	-0.018991048711292
+Cytoplasm_AreaShape_Solidity	0.0015786125359822378	-0.0013272362163627568	0.02219773659694841	-0.02219773659694841
+Cytoplasm_AreaShape_Zernike_0_0	0.002088417953344579	0.00021569515165598282	0.07415474154355159	-0.0741547415435516
+Cytoplasm_AreaShape_Zernike_1_1	0.0016438792181028283	7.211811936187515e-05	0.06474488087862539	-0.0647448808786254
+Cytoplasm_AreaShape_Zernike_2_0	0.00010700655509876533	-0.00010690280352623722	0.014560609702312818	-0.01456060970231282
+Cytoplasm_AreaShape_Zernike_2_2	0.00041596456432402285	-0.0005810082294866484	0.016760283167826687	-0.016760283167826687
+Cytoplasm_AreaShape_Zernike_3_1	0.002297690905913252	0.0010492632910750642	0.07672739625313023	-0.07672739625313024
+Cytoplasm_AreaShape_Zernike_3_3	0.0003795020536958571	0.00010743481624880454	0.0317203219168393	-0.03172032191683931
+Cytoplasm_AreaShape_Zernike_4_0	0.0002977648800857047	6.0169208301154624e-05	0.027830497466493742	-0.027830497466493746
+Cytoplasm_AreaShape_Zernike_4_2	0.00031229079143291205	-0.00048786853891755714	-0.02665973954703099	0.026659739547031
+Cytoplasm_AreaShape_Zernike_4_4	0.00024039518277585437	0.00010195651240599927	0.025348098719693575	-0.02534809871969358
+Cytoplasm_AreaShape_Zernike_5_1	0.0018440939819794888	0.0008620669657334067	0.06952922771404131	-0.06952922771404131
+Cytoplasm_AreaShape_Zernike_5_3	0.0003657328643950075	-0.00010064219090909001	0.02874972812110394	-0.02874972812110394
+Cytoplasm_AreaShape_Zernike_5_5	0.0003016710286508628	0.00043331096343437846	0.0270691147123224	-0.027069114712322404
+Cytoplasm_AreaShape_Zernike_6_0	0.0014846243725648023	7.224575773414147e-05	0.06156617547037284	-0.061566175470372846
+Cytoplasm_AreaShape_Zernike_6_2	0.00030144730461312275	0.0004684251654992831	0.02641886478786873	-0.026418864787868732
+Cytoplasm_AreaShape_Zernike_6_4	0.00032771368860218253	-0.0005948662969311966	-0.024642674766020563	0.024642674766020566
+Cytoplasm_AreaShape_Zernike_6_6	0.00010096955509586447	-0.0004180150592719995	-0.0065892138250222936	0.006589213825022294
+Cytoplasm_AreaShape_Zernike_7_1	0.0006843504228782882	-5.0348266981474166e-05	0.04063355588188423	-0.040633555881884234
+Cytoplasm_AreaShape_Zernike_7_3	0.00032985101752436297	0.00017827802636225757	0.029937658005005266	-0.02993765800500527
+Cytoplasm_AreaShape_Zernike_7_5	0.00025305029851607763	0.000646405821023809	0.013547932803624485	-0.013547932803624487
+Cytoplasm_AreaShape_Zernike_7_7	6.6780246423814305e-06	8.513827501544872e-05	0.0035097270893308177	-0.0035097270893308186
+Cytoplasm_AreaShape_Zernike_8_0	0.00038955352332736837	0.0004333228680110667	0.031578057091740774	-0.03157805709174078
+Cytoplasm_AreaShape_Zernike_8_2	0.0007264332625717129	0.0006399158102739472	0.04250152868901642	-0.042501528689016424
+Cytoplasm_AreaShape_Zernike_8_4	7.22083628618142e-05	-0.00030105360517235506	0.0036058283904312483	-0.0036058283904312487
+Cytoplasm_AreaShape_Zernike_8_6	0.0002618459144749785	-0.0005295750130873266	-0.02210488594306049	0.022104885943060492
+Cytoplasm_AreaShape_Zernike_8_8	1.1262576999571117e-05	7.089490923856629e-05	-0.0037394809794955115	0.0037394809794955115
+Cytoplasm_AreaShape_Zernike_9_1	0.0007564433261942893	-0.0004337437451330566	0.03510709078980837	-0.03510709078980838
+Cytoplasm_AreaShape_Zernike_9_3	0.00011963450826490973	0.000393825784367614	0.013399707269183506	-0.013399707269183507
+Cytoplasm_AreaShape_Zernike_9_5	0.00021727662173109952	0.0001584282666447601	0.024327254036566125	-0.024327254036566128
+Cytoplasm_AreaShape_Zernike_9_7	3.205843562170685e-06	-7.10741854575669e-05	6.041471601089407e-05	-6.041471601089408e-05
+Cytoplasm_AreaShape_Zernike_9_9	0.00033531916612716817	0.0005499772959535744	0.026551046346197392	-0.026551046346197392
+Cytoplasm_Correlation_Correlation_CY5_DAPI	0.0005251328897704077	-0.0009257563871049491	-0.0014321385781055403	0.0014321385781055406
+Cytoplasm_Correlation_Correlation_CY5_GFP	0.02111386727248976	-0.004754636668433104	-0.19852260312592646	0.19852260312592648
+Cytoplasm_Correlation_Correlation_CY5_RFP	0.005087376939142629	-0.002741257096523211	-0.07657227980693049	0.0765722798069305
+Cytoplasm_Correlation_Correlation_DAPI_GFP	0.002715137378778709	-0.002108204814341303	-0.04562947163654152	0.04562947163654153
+Cytoplasm_Correlation_Correlation_DAPI_RFP	0.002464133286220749	-0.0020430130378385264	-0.03801907167715065	0.038019071677150656
+Cytoplasm_Correlation_Correlation_GFP_RFP	0.0042816509406126135	-0.002672644891624669	-0.053676612684563585	0.0536766126845636
+Cytoplasm_Correlation_Costes_CY5_DAPI	0.0007599006485999871	0.0007130721889263248	0.04252829041870185	-0.042528290418701854
+Cytoplasm_Correlation_Costes_CY5_GFP	0.00018262022758408492	-0.0004092379943944594	0.009917612873595497	-0.009917612873595497
+Cytoplasm_Correlation_Costes_CY5_RFP	0.00047916208226594925	-0.000540233450613438	0.021557680785444785	-0.02155768078544479
+Cytoplasm_Correlation_Costes_DAPI_CY5	0.0026876935073217867	-0.0021665726453853388	-0.029326612527689713	0.029326612527689717
+Cytoplasm_Correlation_Costes_DAPI_GFP	0.03774639562677362	-0.008125279568646738	-0.0860380062799878	0.08603800627998781
+Cytoplasm_Correlation_Costes_DAPI_RFP	0.0005045225670359255	-0.0006130825519477803	0.019675717319132476	-0.01967571731913248
+Cytoplasm_Correlation_Costes_GFP_CY5	0.0019445743719733732	-0.0018034129122974257	-0.006425082994334993	0.006425082994334994
+Cytoplasm_Correlation_Costes_GFP_DAPI	0.00023990806242368823	0.0003566612420226887	0.02457659092195731	-0.024576590921957315
+Cytoplasm_Correlation_Costes_GFP_RFP	0.00013052934322266285	0.0002616434862717788	0.01814687957425579	-0.01814687957425579
+Cytoplasm_Correlation_Costes_RFP_CY5	0.007932569603623207	-0.0036698635045809415	-0.06737123198554394	0.06737123198554396
+Cytoplasm_Correlation_Costes_RFP_DAPI	0.001782205475966303	0.0007111040831386067	0.06928634946457723	-0.06928634946457725
+Cytoplasm_Correlation_Costes_RFP_GFP	6.648100221007258e-05	0.00023774230026949063	0.012008224341430195	-0.012008224341430196
+Cytoplasm_Correlation_K_CY5_DAPI	0.02125226984019879	0.006045643845100542	0.10128722691763556	-0.10128722691763557
+Cytoplasm_Correlation_K_CY5_GFP	0.05339077400673764	0.009577356664338345	0.16187475589985667	-0.16187475589985667
+Cytoplasm_Correlation_K_CY5_RFP	0.03027509333663736	0.006114531609863282	-0.07737026505646584	0.07737026505646584
+Cytoplasm_Correlation_K_DAPI_CY5	0.032063301697514035	-0.007474921546035127	-0.1065772588397163	0.1065772588397163
+Cytoplasm_Correlation_K_DAPI_GFP	0.0010305784910860094	-0.0010011431369909597	0.022037711709506278	-0.022037711709506285
+Cytoplasm_Correlation_K_DAPI_RFP	0.009555309851577265	-0.002077232479887442	-0.15715457780449568	0.1571545778044957
+Cytoplasm_Correlation_K_GFP_CY5	0.058577918351632285	-0.009897393884070295	-0.19664776412835944	0.19664776412835946
+Cytoplasm_Correlation_K_GFP_DAPI	0.00039017228724769204	0.0008264198793014001	0.009974505138600445	-0.009974505138600447
+Cytoplasm_Correlation_K_GFP_RFP	0.016194331731165135	-0.003335962644135031	-0.19552097935006024	0.1955209793500603
+Cytoplasm_Correlation_K_RFP_CY5	0.00489801832975445	-0.0025328963564087543	0.025737226715638155	-0.025737226715638162
+Cytoplasm_Correlation_K_RFP_DAPI	0.0007161511355102679	0.0004407964017871311	0.04396731615992166	-0.043967316159921666
+Cytoplasm_Correlation_K_RFP_GFP	0.009133470965962509	0.0023414187678728075	0.1496259847655388	-0.14962598476553882
+Cytoplasm_Correlation_Manders_CY5_DAPI	0.0016402610823448294	0.0016933178550878596	0.02224243351021908	-0.022242433510219085
+Cytoplasm_Correlation_Manders_CY5_GFP	0.02865112749454335	0.0020763625608550586	0.27959272615994557	-0.2795927261599456
+Cytoplasm_Correlation_Manders_CY5_RFP	0.007446473886862592	-0.002527605149968063	-0.12680032002796957	0.1268003200279696
+Cytoplasm_Correlation_Manders_DAPI_CY5	0.017739701975301725	-0.005549099423407368	-0.04566254204386132	0.045662542043861325
+Cytoplasm_Correlation_Manders_DAPI_GFP	0.007741715213960387	-0.001269458432627069	0.11541896355395165	-0.11541896355395166
+Cytoplasm_Correlation_Manders_DAPI_RFP	0.0066670510191207955	-0.0030436297514806264	-0.09414036182909077	0.0941403618290908
+Cytoplasm_Correlation_Manders_GFP_CY5	0.043134579005389084	-0.005811879053217145	-0.3116165979592037	0.31161659795920377
+Cytoplasm_Correlation_Manders_GFP_DAPI	0.0018736158341619014	-0.00016428179963680958	-0.06992346683449764	0.06992346683449766
+Cytoplasm_Correlation_Manders_GFP_RFP	0.011591706864863327	-0.0026793901601309573	-0.16790314801929904	0.16790314801929906
+Cytoplasm_Correlation_Manders_RFP_CY5	0.0277926346406272	-0.006959402793537561	-0.09918568853228624	0.09918568853228626
+Cytoplasm_Correlation_Manders_RFP_DAPI	8.955236055385729e-05	-0.00015833446755131713	-0.015536411068644708	0.015536411068644711
+Cytoplasm_Correlation_Manders_RFP_GFP	0.015300646406737006	-0.002058609764234981	0.15492063915531937	-0.1549206391553194
+Cytoplasm_Correlation_Overlap_CY5_DAPI	0.0033610913536596954	-0.0023357097619906416	-0.002674393466168793	0.0026743934661687934
+Cytoplasm_Correlation_Overlap_CY5_GFP	0.03020962624331336	-0.005702393507480598	-0.23692468492786228	0.23692468492786234
+Cytoplasm_Correlation_Overlap_CY5_RFP	0.00894158632658093	0.0036722391791866415	-0.01250950269677666	0.012509502696776663
+Cytoplasm_Correlation_Overlap_DAPI_GFP	0.002965315177325789	-0.0012805779088223138	0.05605348858658939	-0.056053488586589394
+Cytoplasm_Correlation_Overlap_DAPI_RFP	0.0031976022744648613	-0.0020618224159082033	-0.06789385318151424	0.06789385318151425
+Cytoplasm_Correlation_Overlap_GFP_RFP	0.0020013317288406496	-0.0009986681093168395	0.047992330334309496	-0.0479923303343095
+Cytoplasm_Correlation_RWC_CY5_DAPI	0.017948787006094813	0.005598209824376819	0.05482044673459563	-0.05482044673459564
+Cytoplasm_Correlation_RWC_CY5_GFP	0.02860013486197377	0.003999228098002225	0.26690717255334545	-0.26690717255334545
+Cytoplasm_Correlation_RWC_CY5_RFP	0.01642171647617041	0.004578795733880542	-0.051561988492271046	0.05156198849227105
+Cytoplasm_Correlation_RWC_DAPI_CY5	0.0381338249048635	0.008123605796778332	0.06217478000663225	-0.06217478000663226
+Cytoplasm_Correlation_RWC_DAPI_GFP	0.014968243259008696	0.005102710190436726	0.04440714080353157	-0.04440714080353158
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+Cytoplasm_Granularity_10_CY5	0.0012794340727638742	-0.00021308451341071262	-0.05834512933051793	0.05834512933051794
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+Cytoplasm_Granularity_10_GFP	0.0011132563883389235	-0.0007343381369947633	-0.053361648703681555	0.05336164870368157
+Cytoplasm_Granularity_10_RFP	0.0020835794988858236	-0.0016985248818671243	-0.05281143696915237	0.05281143696915239
+Cytoplasm_Granularity_11_CY5	0.0016309818393005493	-0.0014705476228122794	-0.04859806361092324	0.048598063610923246
+Cytoplasm_Granularity_11_DAPI	0.00018228367412398416	-0.00043538256037721105	0.008471897132556507	-0.008471897132556507
+Cytoplasm_Granularity_11_GFP	0.0023822824174788426	-0.001362706583522526	-0.07330200697452477	0.07330200697452478
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+Cytoplasm_Granularity_12_CY5	0.0010583575948066581	-0.0008552782904331795	-0.049937707945511764	0.04993770794551177
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+Cytoplasm_Granularity_13_CY5	0.0020561875463456225	-0.001495136077519853	-0.06154203510793438	0.061542035107934386
+Cytoplasm_Granularity_13_DAPI	0.0017088813151383553	-0.0017000117154822303	-0.031918808475031694	0.031918808475031694
+Cytoplasm_Granularity_13_GFP	0.0021341751414482024	-0.0005627243906998416	-0.07630723228806335	0.07630723228806335
+Cytoplasm_Granularity_13_RFP	0.0010317266572867911	-0.0007963539310448167	-0.05014037577990635	0.050140375779906356
+Cytoplasm_Granularity_14_CY5	0.002301246713941274	-0.0019359724866376406	-0.04263935211101224	0.042639352111012246
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+Cytoplasm_Granularity_14_GFP	0.0012622482108418653	-0.0014861718065783264	-0.016226038697066472	0.016226038697066476
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+Cytoplasm_Granularity_15_CY5	0.0011213847962556756	-0.0013611300292747292	-0.003410458636549047	0.0034104586365490473
+Cytoplasm_Granularity_15_DAPI	0.0052827044327254935	-0.0027976670454139783	-0.07770160181986659	0.0777016018198666
+Cytoplasm_Granularity_15_GFP	0.002503783574336893	-0.0020576130232119187	-0.03866321807308587	0.03866321807308588
+Cytoplasm_Granularity_15_RFP	0.008420798258353512	-0.003366555431082819	-0.1090192549650506	0.10901925496505062
+Cytoplasm_Granularity_16_CY5	0.0005086022259875866	-0.0006702779090890635	-0.03288836264719001	0.032888362647190014
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+Cytoplasm_Granularity_1_CY5	0.013249635143127603	0.004731168524565618	0.08934763001351428	-0.0893476300135143
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+Cytoplasm_Granularity_1_GFP	0.012570444966061411	0.0019664738774075007	0.18359944250475035	-0.18359944250475038
+Cytoplasm_Granularity_1_RFP	0.0039408581842715895	0.0022890092093482096	-0.021762075167820645	0.021762075167820645
+Cytoplasm_Granularity_2_CY5	0.012534962076880984	0.004112322182615022	0.13273146015226409	-0.1327314601522641
+Cytoplasm_Granularity_2_DAPI	0.00022053824807211075	0.00032097954612814584	-0.016299866210891956	0.016299866210891956
+Cytoplasm_Granularity_2_GFP	0.016450283517618902	0.00319827693047792	0.19991573305970145	-0.19991573305970148
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+Cytoplasm_Granularity_3_CY5	0.0018514897141275188	0.0016447286970701382	0.04686356827727928	-0.046863568277279284
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+Cytoplasm_Granularity_3_GFP	0.005076169354451654	0.0022015652580942135	0.10155477754181322	-0.10155477754181323
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+Cytoplasm_Granularity_4_CY5	0.0013353412612182591	0.0005426764276836216	-0.04753670524441221	0.04753670524441222
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+Cytoplasm_Granularity_5_CY5	0.0035529450275485486	-0.0013688726088232526	-0.0946268627168845	0.09462686271688452
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+Cytoplasm_Granularity_6_CY5	0.003378776433940134	-0.0010374365952624488	-0.09508110570183098	0.095081105701831
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+Nuclei_Granularity_16_CY5	3.565774247804576e-05	-0.00024527045571729166	-0.0010778718904106562	0.0010778718904106564
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+Nuclei_Texture_Variance_GFP_3_00_256	0.0047766039179762965	-0.002718434801524874	-0.06900243780921124	0.06900243780921125
+Nuclei_Texture_Variance_GFP_3_01_256	0.00463585470397454	-0.002681202393977962	-0.06768891720419837	0.06768891720419837
+Nuclei_Texture_Variance_GFP_3_02_256	0.004693074567965971	-0.0027132111182494303	-0.06661503584093789	0.0666150358409379
+Nuclei_Texture_Variance_GFP_3_03_256	0.004505461038917469	-0.0026644826933333084	-0.06466291553057099	0.064662915530571
+Nuclei_Texture_Variance_RFP_3_00_256	0.007166977103122973	-0.002177305987936053	-0.13083150707775643	0.13083150707775643
+Nuclei_Texture_Variance_RFP_3_01_256	0.007041341477668439	-0.002138468458208393	-0.13002310505043257	0.1300231050504326
+Nuclei_Texture_Variance_RFP_3_02_256	0.006933336034328663	-0.0021263905974577713	-0.12894620324353137	0.12894620324353137
+Nuclei_Texture_Variance_RFP_3_03_256	0.006893263452104947	-0.00214410119364516	-0.12815294030602828	0.12815294030602828
diff --git a/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_coeff.py b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_coeff.py
new file mode 100644
index 0000000..878513f
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_coeff.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Perform linear model per CellProfiler feature
+# 
+
+# ## Import libraries
+# 
+
+# In[1]:
+
+
+import pathlib
+import pandas as pd
+
+from sklearn.linear_model import LinearRegression
+
+from pycytominer.cyto_utils import infer_cp_features
+
+
+# ## Set up paths and variables
+# 
+
+# In[2]:
+
+
+# Define inputs and outputs
+data_dir = pathlib.Path("../3.processing_features/data/single_cell_profiles/")
+cp_file = pathlib.Path(data_dir, "Plate_4_sc_normalized.parquet")
+
+output_dir = pathlib.Path("./results")
+output_dir.mkdir(exist_ok=True)
+output_cp_file = pathlib.Path(output_dir, "linear_model_cp_features_plate4.tsv")
+
+
+# ## Read in normalized data
+# 
+
+# In[3]:
+
+
+# Load data
+cp_df = pd.read_parquet(cp_file)
+
+# Remove rows where Metadata_genotype is "HET" to avoid using in the LM
+cp_df = cp_df[cp_df["Metadata_genotype"] != "HET"]
+
+# Define CellProfiler features
+cp_features = infer_cp_features(cp_df)
+
+print(f"We are testing {len(cp_features)} CellProfiler features")
+print(cp_df.shape)
+cp_df.head()
+
+
+# ## Set up the dummy matrix between null and WT cell types
+# 
+
+# In[4]:
+
+
+# Setup linear modeling framework
+variables = ["Metadata_number_of_singlecells"]
+X = cp_df.loc[:, variables]
+
+# Add dummy matrix of categorical genotypes, excluding "HET"
+genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype)
+
+X = pd.concat([X, genotype_x], axis=1)
+
+print(X.shape)
+X.head()
+
+
+# ## Perform linear modeling per feature
+# 
+
+# In[5]:
+
+
+# Fit linear model for each feature
+lm_results = []
+for cp_feature in cp_features:
+    # Subset CP data to each individual feature (univariate test)
+    cp_subset_df = cp_df.loc[:, cp_feature]
+
+    # Fit linear model
+    lm = LinearRegression(fit_intercept=True)
+    lm_result = lm.fit(X=X, y=cp_subset_df)
+
+    # Extract Beta coefficients
+    # (contribution of feature to X covariates)
+    coef = lm_result.coef_
+
+    # Estimate fit (R^2)
+    r2_score = lm.score(X=X, y=cp_subset_df)
+
+    # Add results to a growing list
+    lm_results.append([cp_feature, r2_score] + list(coef))
+
+# Convert results to a pandas DataFrame
+lm_results = pd.DataFrame(
+    lm_results,
+    columns=["feature", "r2_score", "cell_count_coef", "Null_coef", "WT_coef"],
+)
+
+# Output file
+lm_results.to_csv(output_cp_file, sep="\t", index=False)
+
+print(lm_results.shape)
+lm_results.head()
+
+
+# ## Check to make sure the processing worked
+# 
+
+# In[6]:
+
+
+# Small exploration visualization
+lm_results.plot(x="cell_count_coef", y="WT_coef", kind="scatter")
+
+
+# In[ ]:
+
+
+
+
diff --git a/4.analyze_data/notebooks/linear_model/scripts/1.visualize_lm.r b/4.analyze_data/notebooks/linear_model/scripts/1.visualize_lm.r
new file mode 100644
index 0000000..eeb626e
--- /dev/null
+++ b/4.analyze_data/notebooks/linear_model/scripts/1.visualize_lm.r
@@ -0,0 +1,92 @@
+suppressPackageStartupMessages(library(ggplot2))
+suppressPackageStartupMessages(library(dplyr))
+
+
+# path to tsv with LM coefficients
+lm_results_dir <- file.path("./results")
+lm_file <- file.path(lm_results_dir, "./linear_model_cp_features_plate1.tsv")
+
+# save path for figure
+lm_fig_dir <- file.path("./figures")
+lm_fig <- file.path(lm_fig_dir, "linear_model_cp_features_plate1.png")
+
+# Load and process linear model data
+lm_df <- readr::read_tsv(
+    lm_file,
+    col_types = readr::cols(.default = "d", feature = "c")
+)
+
+print(dim(lm_df))
+head(lm_df)
+
+
+# Arrange by absolute value coefficient
+# Split out components of feature name for visualization
+lm_df <- lm_df %>%
+    dplyr::arrange(desc(abs(Null_coef))) %>%
+    tidyr::separate(
+        feature,
+        into = c(
+            "compartment",
+            "feature_group",
+            "measurement",
+            "channel",
+            "parameter1",
+            "parameter2"
+        ),
+        sep = "_",
+        remove = FALSE
+    ) %>%
+    dplyr::mutate(channel_cleaned = channel)
+
+# Clean channel for visualization and interpretation
+lm_df$channel_cleaned <-
+    dplyr::recode(
+        lm_df$channel_cleaned,
+        "DAPI" = "nuclei",
+        "GFP" = "ER",
+        "RFP" = "actin",
+        .default = "other",
+        .missing = "other"
+    )
+
+# Print to make sure that the above clean up worked
+print(dim(lm_df))
+head(lm_df, 10)
+
+
+# Specify order so that the organelles match the correct color (red, green, blue)
+color_order <- c("actin", "ER", "nuclei", "other")
+
+# Plot the linear model coefficients for the WT genotype contribution
+# Positive coeff = more likely to be WT cell if feature increases
+# Negative coeff = more likely to be Null cell if feature increases
+lm_fig_gg <- (
+    ggplot(lm_df, aes(x = cell_count_coef, y = WT_coef))
+    +
+        geom_point(aes(size = r2_score, color = factor(channel_cleaned, levels = color_order)), alpha = 0.7)
+        +
+        geom_vline(xintercept = 0, linetype = "dashed", color = "red")
+        +
+        geom_density2d(color = "black", show.legend = FALSE)
+        +
+        theme_bw()
+        +
+        guides(
+            color = guide_legend(title = "Channel\n(if applicable)", order = 1),
+            size = guide_legend(title = "R2 score")
+        )
+        +
+        ylab("WT genotype contribution (LM beta coefficient)")
+        +
+        xlab("Cell count contribution (LM beta coefficient)")
+        +
+        ggtitle("Scatter plot of linear model coefficients per CellProfiler feature\n for Plate 1") # nolint
+)
+
+# Output figure
+ggsave(lm_fig, lm_fig_gg, dpi = 500, height = 6, width = 6)
+
+# Show figure
+lm_fig_gg
+
diff --git a/4.analyze_data/r_analysis_env.yml b/4.analyze_data/r_analysis_env.yml
new file mode 100644
index 0000000..5e42fac
--- /dev/null
+++ b/4.analyze_data/r_analysis_env.yml
@@ -0,0 +1,25 @@
+name: r_analysis_nf1
+channels:
+  - conda-forge
+dependencies:
+  - conda-forge::jupyter
+  - conda-forge::jupyterlab
+  - conda-forge::ipykernel
+  - conda-forge::python<=3.11
+  - conda-forge::r-irkernel
+  - conda-forge::nb_conda_kernels
+  - conda-forge::widgetsnbextension
+  - conda-forge::r-base=4.2.2
+  - conda-forge::r-dplyr=1.0.10
+  - conda-forge::r-tidyr
+  - conda-forge::r-readr
+  - conda-forge::r-stringr
+  - conda-forge::r-ggplot2
+  - conda-forge::r-ggrepel
+  - conda-forge::r-gridextra
+  - conda-forge::r-devtools
+  - conda-forge::r-reshape2
+  - conda-forge::r-cowplot
+  - conda-forge::r-patchwork
+  - conda-forge::r-pwr
+  - conda-forge::r-platetools
diff --git a/README.md b/README.md
index ddfcad0..79f77b4 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,12 @@
 # NF1 Cell Painting Data 
 
+In this repository, we perform the image-based analysis and some analysis of the morphology data.
+
+We train a machine learning model to predict NF1 genotype within a separate repository called: [NF1_SchwannCell_data_analysis](https://github.com/WayScience/NF1_SchwannCell_data_analysis).
+Please visit the above repository for further information on the generation and validation of the logistic regression model.
+
+**Note:** All metadata files are located in the [download data module](./0.download_data/). All larger files, including `SQLite` outputs from CellProfiler and `parquet` processed data file from pycytominer, will need to be downloaded using git LFS after the repo is cloned.
+
 ## Data
 
 The data we use is a modified Cell Painting assay on [Schwann cells](https://www.ncbi.nlm.nih.gov/books/NBK544316/) from patients with [Neurofibromatosis type 1 (NF1)](https://medlineplus.gov/genetics/condition/neurofibromatosis-type-1/). 
@@ -54,7 +61,7 @@ As well, the plates have different culturing conditions, where plate 3 cells wer
 ![plate3_nf1_platemap.png](example_figures/plate3_nf1_platemap.png)
 
 **Plate 4**
-For plate 4, we will be looking at how using different siRNA constructs to downregulate neurofibromin production in NF1 WT cells impacts the morpholgy as dose increases. 
+For plate 4, we will be looking at how using different siRNA constructs to downregulate neurofibromin production in NF1 WT cells impacts the morphology as dose increases. 
 We will be able to compare this to controls (e.g., untreated WT and Null cells).
 
 The cells were cultured in 5% FBS.