From 076f62df3474855112d528a3e7165317dc8e8fc9 Mon Sep 17 00:00:00 2001 From: jenna-tomkinson Date: Mon, 18 Mar 2024 13:04:17 -0600 Subject: [PATCH 1/3] add linear model results from concat plates --- .../linear_model/0.linear_model_coeff.ipynb | 9 +- .../0.linear_model_concat_coeff.ipynb | 676 +++++ ...tures_concat_plate5_plate3_plate3prime.tsv | 2272 +++++++++++++++++ .../scripts/0.linear_model_coeff.py | 7 +- .../scripts/0.linear_model_concat_coeff.py | 155 ++ 5 files changed, 3110 insertions(+), 9 deletions(-) create mode 100644 4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb create mode 100644 4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_concat_plate5_plate3_plate3prime.tsv create mode 100644 4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py 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 index eda85c3..7a57c22 100644 --- a/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb +++ b/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb @@ -21,12 +21,11 @@ "outputs": [], "source": [ "import pathlib\n", - "import pandas as pd\n", - "\n", - "from sklearn.linear_model import LinearRegression\n", "\n", + "import pandas as pd\n", "from pycytominer import feature_select\n", - "from pycytominer.cyto_utils import infer_cp_features\n" + "from pycytominer.cyto_utils import infer_cp_features\n", + "from sklearn.linear_model import LinearRegression" ] }, { @@ -347,7 +346,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up the dummy matrix between null and WT cell types\n" + "## Set up the dummy matrix between Null and WT cell types\n" ] }, { diff --git a/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb b/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb new file mode 100644 index 0000000..9d06ea2 --- /dev/null +++ b/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb @@ -0,0 +1,676 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perform linear model per CellProfiler feature on concatenated normalized data from plates 5, 3 prime, and 3\n", + "\n", + "We will include 4 co-variates:\n", + "\n", + "1. Cell count per well contribution\n", + "2. Plate contribution -> need variability across plates for cell count per well \n", + "3. Genotype contribution (WT versus Null) -> could be imbalance \n", + "4. Interaction term between genotype and cell count -> remove first, Natalie think this might be too correlated\n", + "\n", + "Assumes Gaussian for LM -> need to check for that LM\n", + "\n", + "If co-variates are too highly correlated, then coefficients will \"explode\"\n", + "\n", + "Can Python show if there was convergence or not\n", + "\n", + "QC plot -> density and qq (do for one first for qq), and scatterplot with each of the covariantes \n", + "\n", + "Might need multiple test correction which uses p-value -> Bonferonni and Benjamini - Hockberg\n", + "\n", + "Under null hypothesis the p-values are evenly distributed but if there is a significant value of p-value that is low than reject null" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import pandas as pd\n", + "from pycytominer import feature_select\n", + "from pycytominer.cyto_utils import infer_cp_features\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We are testing 2271 CellProfiler features\n", + "(22585, 2289)\n" + ] + }, + { + "data": { + "text/html": [ + "
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0Cytoplasm_AreaShape_Area0.076596-0.001731-0.0364350.0364350.350194-0.068791-0.281403
1Cytoplasm_AreaShape_BoundingBoxArea0.076316-0.001628-0.0523300.0523300.320520-0.074655-0.245865
2Cytoplasm_AreaShape_BoundingBoxMaximum_X0.000805-0.000147-0.0148590.0148590.034820-0.014360-0.020460
3Cytoplasm_AreaShape_BoundingBoxMaximum_Y0.002924-0.000333-0.0037570.0037570.075917-0.016994-0.058923
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" + ], + "text/plain": [ + " feature r2_score cell_count_coef \\\n", + "0 Cytoplasm_AreaShape_Area 0.076596 -0.001731 \n", + "1 Cytoplasm_AreaShape_BoundingBoxArea 0.076316 -0.001628 \n", + "2 Cytoplasm_AreaShape_BoundingBoxMaximum_X 0.000805 -0.000147 \n", + "3 Cytoplasm_AreaShape_BoundingBoxMaximum_Y 0.002924 -0.000333 \n", + "4 Cytoplasm_AreaShape_BoundingBoxMinimum_X 0.000846 0.000185 \n", + "\n", + " Null_coef WT_coef Plate_3_coef Plate_3_prime_coef Plate_5_coef \n", + "0 -0.036435 0.036435 0.350194 -0.068791 -0.281403 \n", + "1 -0.052330 0.052330 0.320520 -0.074655 -0.245865 \n", + "2 -0.014859 0.014859 0.034820 -0.014360 -0.020460 \n", + "3 -0.003757 0.003757 0.075917 -0.016994 -0.058923 \n", + "4 -0.003690 0.003690 -0.033054 0.001485 0.031569 " + ] + }, + "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 = concat_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=[\n", + " \"feature\",\n", + " \"r2_score\",\n", + " \"cell_count_coef\",\n", + " \"Null_coef\",\n", + " \"WT_coef\",\n", + " \"Plate_3_coef\",\n", + " \"Plate_3_prime_coef\",\n", + " \"Plate_5_coef\",\n", + " ],\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()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python_analysis_nf1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_concat_plate5_plate3_plate3prime.tsv b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_concat_plate5_plate3_plate3prime.tsv new file mode 100644 index 0000000..0f2214a --- /dev/null +++ b/4.analyze_data/notebooks/linear_model/results/linear_model_cp_features_concat_plate5_plate3_plate3prime.tsv @@ -0,0 +1,2272 @@ +feature r2_score cell_count_coef Null_coef WT_coef Plate_3_coef 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+Nuclei_Texture_Variance_RFP_3_03_256 0.011596049658670449 -0.0006005381746591122 0.049074397458514024 -0.04907439745851415 0.10089240790463573 -0.05715817108374903 -0.043734236820886264 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 index bda6df6..7fb1437 100644 --- 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 @@ -11,12 +11,11 @@ import pathlib -import pandas as pd - -from sklearn.linear_model import LinearRegression +import pandas as pd from pycytominer import feature_select from pycytominer.cyto_utils import infer_cp_features +from sklearn.linear_model import LinearRegression # ## Set up paths and variables @@ -62,7 +61,7 @@ cp_df.head() -# ## Set up the dummy matrix between null and WT cell types +# ## Set up the dummy matrix between Null and WT cell types # # In[4]: diff --git a/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py new file mode 100644 index 0000000..ebf7bfe --- /dev/null +++ b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Perform linear model per CellProfiler feature on concatenated normalized data from plates 5, 3 prime, and 3 +# +# We will include 4 co-variates: +# +# 1. Cell count per well contribution +# 2. Plate contribution -> need variability across plates for cell count per well +# 3. Genotype contribution (WT versus Null) -> could be imbalance +# 4. Interaction term between genotype and cell count -> remove first, Natalie think this might be too correlated +# +# Assumes Gaussian for LM -> need to check for that LM +# +# If co-variates are too highly correlated, then coefficients will "explode" +# +# Can Python show if there was convergence or not +# +# QC plot -> density and qq (do for one first for qq), and scatterplot with each of the covariantes +# +# Might need multiple test correction which uses p-value -> Bonferonni and Benjamini - Hockberg +# +# Under null hypothesis the p-values are evenly distributed but if there is a significant value of p-value that is low than reject null + +# In[1]: + + +import pathlib + +import pandas as pd +from pycytominer import feature_select +from pycytominer.cyto_utils import infer_cp_features +from sklearn.linear_model import LinearRegression + + +# In[2]: + + +# Define inputs and outputs +data_dir = pathlib.Path("../../../../nf1_cellpainting_data/3.processing_features/data/single_cell_profiles/") +cp_files = [ + pathlib.Path(data_dir, f"Plate_{plate}_sc_normalized.parquet") + for plate in ["5", "3", "3_prime"] +] + +# Output directory for LM coeffs +output_dir = pathlib.Path("./results") +output_dir.mkdir(exist_ok=True) + +# Name of output file with LM coeffs +output_cp_file = pathlib.Path( + output_dir, "linear_model_cp_features_concat_plate5_plate3_plate3prime.tsv" +) + +# Filter and load the specified files +metadata_mapping = {"Plate_3_prime": "Plate_3_prime"} + +# Filter and load the specified files +df_list = [] + +for plate in cp_files: + cp_file = pathlib.Path(plate) + + if cp_file.exists(): + # Load data + df = pd.read_parquet(cp_file) + + # Update Metadata_Plate only for Plate_3_prime + if plate.stem.replace("_sc_normalized", "") == "Plate_3_prime": + df["Metadata_Plate"] = "Plate_3_prime" + + # Remove rows with 'HET' in 'Metadata_genotype' + df = df[df["Metadata_genotype"] != "HET"] + + df_list.append(df) + +# Concatenate the DataFrames +concat_df = pd.concat(df_list, ignore_index=True) + +# Make sure there are no NaNs +concat_df = feature_select(concat_df, operation="drop_na_columns", na_cutoff=0) + +# Define CellProfiler features +cp_features = infer_cp_features(concat_df) + +print(f"We are testing {len(cp_features)} CellProfiler features") +print(concat_df.shape) +concat_df.head() + + +# ## Set up dummy framework for WT versus Null and plate to plate comparison + +# In[3]: + + +# Setup linear modeling framework +variables = ["Metadata_number_of_singlecells"] +X = concat_df.loc[:, variables] + +# Add dummy matrix of categorical genotypes +genotype_x = pd.get_dummies(data=concat_df.Metadata_genotype) + +# Add dummy matrix of categorical plates +plate_x = pd.get_dummies(data=concat_df.Metadata_Plate) + +X = pd.concat([X, genotype_x, plate_x], axis=1) + +print(X.shape) +X.head() + + +# 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 = concat_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", + "Plate_3_coef", + "Plate_3_prime_coef", + "Plate_5_coef", + ], +) + +# Output file +lm_results.to_csv(output_cp_file, sep="\t", index=False) + +print(lm_results.shape) +lm_results.head() + From 46efbd724fe7894382e8cdf550ed1e8e138dae3f Mon Sep 17 00:00:00 2001 From: jenna-tomkinson Date: Mon, 18 Mar 2024 13:23:29 -0600 Subject: [PATCH 2/3] update documentation --- .../0.linear_model_concat_coeff.ipynb | 19 +++---------------- .../scripts/0.linear_model_concat_coeff.py | 19 +++---------------- 2 files changed, 6 insertions(+), 32 deletions(-) diff --git a/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb b/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb index 9d06ea2..eb3192a 100644 --- a/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb +++ b/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb @@ -6,24 +6,11 @@ "source": [ "# Perform linear model per CellProfiler feature on concatenated normalized data from plates 5, 3 prime, and 3\n", "\n", - "We will include 4 co-variates:\n", + "We will include 3 co-variates:\n", "\n", "1. Cell count per well contribution\n", - "2. Plate contribution -> need variability across plates for cell count per well \n", - "3. Genotype contribution (WT versus Null) -> could be imbalance \n", - "4. Interaction term between genotype and cell count -> remove first, Natalie think this might be too correlated\n", - "\n", - "Assumes Gaussian for LM -> need to check for that LM\n", - "\n", - "If co-variates are too highly correlated, then coefficients will \"explode\"\n", - "\n", - "Can Python show if there was convergence or not\n", - "\n", - "QC plot -> density and qq (do for one first for qq), and scatterplot with each of the covariantes \n", - "\n", - "Might need multiple test correction which uses p-value -> Bonferonni and Benjamini - Hockberg\n", - "\n", - "Under null hypothesis the p-values are evenly distributed but if there is a significant value of p-value that is low than reject null" + "2. Plate contribution\n", + "3. Genotype contribution (WT versus Null)" ] }, { diff --git a/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py index ebf7bfe..b460bdb 100644 --- a/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py +++ b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py @@ -3,24 +3,11 @@ # # Perform linear model per CellProfiler feature on concatenated normalized data from plates 5, 3 prime, and 3 # -# We will include 4 co-variates: +# We will include 3 co-variates: # # 1. Cell count per well contribution -# 2. Plate contribution -> need variability across plates for cell count per well -# 3. Genotype contribution (WT versus Null) -> could be imbalance -# 4. Interaction term between genotype and cell count -> remove first, Natalie think this might be too correlated -# -# Assumes Gaussian for LM -> need to check for that LM -# -# If co-variates are too highly correlated, then coefficients will "explode" -# -# Can Python show if there was convergence or not -# -# QC plot -> density and qq (do for one first for qq), and scatterplot with each of the covariantes -# -# Might need multiple test correction which uses p-value -> Bonferonni and Benjamini - Hockberg -# -# Under null hypothesis the p-values are evenly distributed but if there is a significant value of p-value that is low than reject null +# 2. Plate contribution +# 3. Genotype contribution (WT versus Null) # In[1]: From d4d936281f8fb7fe565212e9f3c2d275f590bea0 Mon Sep 17 00:00:00 2001 From: jenna-tomkinson Date: Thu, 21 Mar 2024 12:46:17 -0600 Subject: [PATCH 3/3] update code and documentation --- .../linear_model/0.linear_model_coeff.ipynb | 4 +- .../0.linear_model_concat_coeff.ipynb | 67 ++++++++++--------- .../scripts/0.linear_model_coeff.py | 4 +- .../scripts/0.linear_model_concat_coeff.py | 26 ++++--- 4 files changed, 50 insertions(+), 51 deletions(-) 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 index 7a57c22..6c1e7f7 100644 --- a/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb +++ b/4.analyze_data/notebooks/linear_model/0.linear_model_coeff.ipynb @@ -346,7 +346,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up the dummy matrix between Null and WT cell types\n" + "## Set up the binary matrix between Null and WT cell types\n" ] }, { @@ -441,7 +441,7 @@ "variables = [\"Metadata_number_of_singlecells\"]\n", "X = cp_df.loc[:, variables]\n", "\n", - "# Add dummy matrix of categorical genotypes\n", + "# Add binary matrix of categorical genotypes\n", "genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype)\n", "\n", "X = pd.concat([X, genotype_x], axis=1)\n", diff --git a/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb b/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb index eb3192a..d8eecf5 100644 --- a/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb +++ b/4.analyze_data/notebooks/linear_model/0.linear_model_concat_coeff.ipynb @@ -291,7 +291,7 @@ ], "source": [ "# Define inputs and outputs\n", - "data_dir = pathlib.Path(\"../../../../nf1_cellpainting_data/3.processing_features/data/single_cell_profiles/\")\n", + "data_dir = pathlib.Path(\"../../../3.processing_features/data/single_cell_profiles/\")\n", "cp_files = [\n", " pathlib.Path(data_dir, f\"Plate_{plate}_sc_normalized.parquet\")\n", " for plate in [\"5\", \"3\", \"3_prime\"]\n", @@ -307,25 +307,19 @@ ")\n", "\n", "# Filter and load the specified files\n", - "metadata_mapping = {\"Plate_3_prime\": \"Plate_3_prime\"}\n", - "\n", - "# Filter and load the specified files\n", "df_list = []\n", "\n", "for plate in cp_files:\n", " cp_file = pathlib.Path(plate)\n", "\n", " if cp_file.exists():\n", - " # Load data\n", - " df = pd.read_parquet(cp_file)\n", + " # Load the parquet file into a DataFrame without HET rows\n", + " df = pd.read_parquet(cp_file, filters=[('Metadata_genotype', '!=', 'HET')])\n", "\n", - " # Update Metadata_Plate only for Plate_3_prime\n", + " # Update Metadata_Plate only for Plate_3_prime since during CellProfiler analysis, the metadata only caught Plate_3\n", " if plate.stem.replace(\"_sc_normalized\", \"\") == \"Plate_3_prime\":\n", " df[\"Metadata_Plate\"] = \"Plate_3_prime\"\n", "\n", - " # Remove rows with 'HET' in 'Metadata_genotype'\n", - " df = df[df[\"Metadata_genotype\"] != \"HET\"]\n", - "\n", " df_list.append(df)\n", "\n", "# Concatenate the DataFrames\n", @@ -346,7 +340,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up dummy framework for WT versus Null and plate to plate comparison" + "## Set up binary framework for WT versus Null and plate to plate comparison" ] }, { @@ -459,10 +453,10 @@ "variables = [\"Metadata_number_of_singlecells\"]\n", "X = concat_df.loc[:, variables]\n", "\n", - "# Add dummy matrix of categorical genotypes\n", + "# Add binary matrix of categorical genotypes\n", "genotype_x = pd.get_dummies(data=concat_df.Metadata_genotype)\n", "\n", - "# Add dummy matrix of categorical plates\n", + "# Add binary matrix of categorical plates\n", "plate_x = pd.get_dummies(data=concat_df.Metadata_Plate)\n", "\n", "X = pd.concat([X, genotype_x, plate_x], axis=1)\n", @@ -471,6 +465,33 @@ "X.head()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "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 = concat_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))" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -596,26 +617,6 @@ } ], "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 = concat_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", 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 index 7fb1437..eb1c968 100644 --- 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 @@ -61,7 +61,7 @@ cp_df.head() -# ## Set up the dummy matrix between Null and WT cell types +# ## Set up the binary matrix between Null and WT cell types # # In[4]: @@ -71,7 +71,7 @@ variables = ["Metadata_number_of_singlecells"] X = cp_df.loc[:, variables] -# Add dummy matrix of categorical genotypes +# Add binary matrix of categorical genotypes genotype_x = pd.get_dummies(data=cp_df.Metadata_genotype) X = pd.concat([X, genotype_x], axis=1) diff --git a/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py index b460bdb..426374a 100644 --- a/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py +++ b/4.analyze_data/notebooks/linear_model/scripts/0.linear_model_concat_coeff.py @@ -24,7 +24,7 @@ # Define inputs and outputs -data_dir = pathlib.Path("../../../../nf1_cellpainting_data/3.processing_features/data/single_cell_profiles/") +data_dir = pathlib.Path("../../../3.processing_features/data/single_cell_profiles/") cp_files = [ pathlib.Path(data_dir, f"Plate_{plate}_sc_normalized.parquet") for plate in ["5", "3", "3_prime"] @@ -39,9 +39,6 @@ output_dir, "linear_model_cp_features_concat_plate5_plate3_plate3prime.tsv" ) -# Filter and load the specified files -metadata_mapping = {"Plate_3_prime": "Plate_3_prime"} - # Filter and load the specified files df_list = [] @@ -49,16 +46,13 @@ cp_file = pathlib.Path(plate) if cp_file.exists(): - # Load data - df = pd.read_parquet(cp_file) + # Load the parquet file into a DataFrame without HET rows + df = pd.read_parquet(cp_file, filters=[('Metadata_genotype', '!=', 'HET')]) - # Update Metadata_Plate only for Plate_3_prime + # Update Metadata_Plate only for Plate_3_prime since during CellProfiler analysis, the metadata only caught Plate_3 if plate.stem.replace("_sc_normalized", "") == "Plate_3_prime": df["Metadata_Plate"] = "Plate_3_prime" - # Remove rows with 'HET' in 'Metadata_genotype' - df = df[df["Metadata_genotype"] != "HET"] - df_list.append(df) # Concatenate the DataFrames @@ -75,7 +69,7 @@ concat_df.head() -# ## Set up dummy framework for WT versus Null and plate to plate comparison +# ## Set up binary framework for WT versus Null and plate to plate comparison # In[3]: @@ -84,10 +78,10 @@ variables = ["Metadata_number_of_singlecells"] X = concat_df.loc[:, variables] -# Add dummy matrix of categorical genotypes +# Add binary matrix of categorical genotypes genotype_x = pd.get_dummies(data=concat_df.Metadata_genotype) -# Add dummy matrix of categorical plates +# Add binary matrix of categorical plates plate_x = pd.get_dummies(data=concat_df.Metadata_Plate) X = pd.concat([X, genotype_x, plate_x], axis=1) @@ -96,7 +90,7 @@ X.head() -# In[5]: +# In[4]: # Fit linear model for each feature @@ -119,6 +113,10 @@ # Add results to a growing list lm_results.append([cp_feature, r2_score] + list(coef)) + +# In[5]: + + # Convert results to a pandas DataFrame lm_results = pd.DataFrame( lm_results,