{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ed9d7816", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:30.035172Z", "iopub.status.busy": "2025-03-25T08:35:30.034990Z", "iopub.status.idle": "2025-03-25T08:35:30.200170Z", "shell.execute_reply": "2025-03-25T08:35:30.199722Z" } }, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n", "\n", "# Path Configuration\n", "from tools.preprocess import *\n", "\n", "# Processing context\n", "trait = \"Cystic_Fibrosis\"\n", "cohort = \"GSE142610\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n", "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE142610\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE142610.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE142610.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE142610.csv\"\n", "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "ea7b74fe", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "35243b84", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:30.201627Z", "iopub.status.busy": "2025-03-25T08:35:30.201481Z", "iopub.status.idle": "2025-03-25T08:35:30.309681Z", "shell.execute_reply": "2025-03-25T08:35:30.309284Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Integrative genomic meta-analysis reveals novel molecular insights into cystic fibrosis and ΔF508-CFTR rescue\"\n", "!Series_summary\t\"Cystic fibrosis (CF), caused by mutations to CFTR, leads to severe and progressive lung disease. The most common mutant, ΔF508-CFTR, undergoes proteasomal degradation, depleting its anion channel function. “Proteostasis” pathways, i.e. those relevant to protein processing and trafficking, are altered in cells with ΔF508-CFTR and can be modulated to partially rescue protein function. However, many details regarding proteostasis modulation, and its relevance to CF and ΔF508-CFTR rescue, remain poorly understood. To shed light on this, we re-analyzed public datasets characterizing transcription in CF vs. non-CF epithelia from human and pig airways, and also profiled established temperature, genetic, and chemical interventions that rescue ΔF508-CFTR. Meta-analysis yielded a core disease signature and two core rescue signatures. To interpret these, we compiled proteostasis pathways and an original “CFTR Gene Set Library”. The disease signature revealed differential regulation of mTORC1 signaling, endocytosis, and proteasomal degradation. Overlaying functional genomics data identified candidate mediators of low-temperature rescue, while multiple rescue strategies converged on activation of unfolded protein response pathways. Remarkably, however, C18, an analog of the CFTR corrector compound Lumacaftor, induced minimal transcriptional perturbation despite its rescue activity. This work elucidates the involvement of proteostasis in both disease and rescue perturbations while highlighting that not all CFTR rescue interventions act on transcription.\"\n", "!Series_overall_design\t\"Polarized air-liquid interface cultures of CFBE cells were treated to either knockdown of SIN3A, SYVN1 or NEED8, overexpression of miR-138, treated with corrector compound 18 (C18), or cultured at temperatures associated with improving ΔF508-CFTR trafficking.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tag: Cell line: CFBE'], 1: ['treatment: DMSO for 24h', 'temperature: 40°C incubation for 24h followed by 27°C incubation for 24h', 'treatment: NEDD8 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', 'treatment: Scrambled DsiRNA', 'temperature: 27°C incubation for 24h', 'treatment: SIN3A DsiRNA', 'temperature: 37°C incubation for 24h', 'treatment: SYVN1 DsiRNA', 'treatment: 6µM Corrector Compound C18 treatment for 24h', 'treatment: No treatment', 'treatment: miR-138 mimic', 'treatment: SYVN1 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', 'temperature: 40°C incubation for 24h', 'treatment: NEDD8 DsiRNA']}\n" ] } ], "source": [ "from tools.preprocess import *\n", "# 1. Identify the paths to the SOFT file and the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Read the matrix file to obtain background information and sample characteristics data\n", "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n", "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", "\n", "# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n", "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", "\n", "# 4. Explicitly print out all the background information and the sample characteristics dictionary\n", "print(\"Background Information:\")\n", "print(background_info)\n", "print(\"Sample Characteristics Dictionary:\")\n", "print(sample_characteristics_dict)\n" ] }, { "cell_type": "markdown", "id": "07ce1988", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "3bad36fc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:30.311112Z", "iopub.status.busy": "2025-03-25T08:35:30.310991Z", "iopub.status.idle": "2025-03-25T08:35:30.322221Z", "shell.execute_reply": "2025-03-25T08:35:30.321831Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview: {'Sample_1': [1.0], 'Sample_2': [nan], 'Sample_3': [0.0], 'Sample_4': [1.0], 'Sample_5': [nan], 'Sample_6': [0.0], 'Sample_7': [nan], 'Sample_8': [0.0], 'Sample_9': [0.0], 'Sample_10': [1.0], 'Sample_11': [0.0], 'Sample_12': [0.0], 'Sample_13': [nan], 'Sample_14': [0.0]}\n", "Clinical data saved to ../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE142610.csv\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import numpy as np\n", "import re\n", "from typing import Callable, Optional, Dict, Any, Union\n", "import json\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this appears to be a gene expression dataset \n", "# analyzing transcriptional changes in CF, not just miRNA or methylation\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For trait: Based on the characteristics dictionary, we see \"treatment\" at index 1\n", "# which can be used to determine CF vs non-CF status\n", "trait_row = 1 # The row with treatment information\n", "\n", "# There is no age data available in the sample characteristics\n", "age_row = None\n", "\n", "# There is no gender data available in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert the treatment value to a binary trait (CF status).\n", " 1 = CF-like condition (DMSO, scrambled, no treatment as controls)\n", " 0 = Rescue interventions (treatments aimed at rescuing ΔF508-CFTR)\n", " \n", " Args:\n", " value: The treatment value from the dataset\n", " \n", " Returns:\n", " 1 for CF-like (control) conditions, 0 for rescue interventions, None for unknown\n", " \"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Control conditions (CF-like)\n", " if any(x in value.lower() for x in ['dmso', 'scrambled dsirna', 'no treatment']):\n", " return 1\n", " # Rescue interventions\n", " elif any(x in value.lower() for x in ['temperature: 27', 'sin3a', 'syvn1', 'nedd8', 'mir-138', 'c18', 'corrector']):\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Placeholder function since age data is not available.\"\"\"\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Placeholder function since gender data is not available.\"\"\"\n", " return None\n", "\n", "# 3. Save Metadata - Initial Filtering\n", "# Trait data is available if trait_row is not None\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Only if trait data is available\n", "if trait_row is not None:\n", " try:\n", " # Create a DataFrame in the expected format for geo_select_clinical_features\n", " # Samples as columns, features as rows\n", " sample_characteristics = {\n", " 0: ['tag: Cell line: CFBE'],\n", " 1: ['treatment: DMSO for 24h', \n", " 'temperature: 40°C incubation for 24h followed by 27°C incubation for 24h', \n", " 'treatment: NEDD8 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', \n", " 'treatment: Scrambled DsiRNA', \n", " 'temperature: 27°C incubation for 24h', \n", " 'treatment: SIN3A DsiRNA', \n", " 'temperature: 37°C incubation for 24h', \n", " 'treatment: SYVN1 DsiRNA', \n", " 'treatment: 6µM Corrector Compound C18 treatment for 24h', \n", " 'treatment: No treatment', \n", " 'treatment: miR-138 mimic', \n", " 'treatment: SYVN1 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', \n", " 'temperature: 40°C incubation for 24h', \n", " 'treatment: NEDD8 DsiRNA']\n", " }\n", " \n", " # Create a list of sample IDs\n", " unique_treatments = sample_characteristics[1]\n", " sample_ids = [f\"Sample_{i+1}\" for i in range(len(unique_treatments))]\n", " \n", " # Create the clinical data DataFrame with samples as columns\n", " clinical_data = pd.DataFrame(index=range(max(sample_characteristics.keys())+1))\n", " \n", " for i, sample_id in enumerate(sample_ids):\n", " clinical_data[sample_id] = None\n", " \n", " # Assign cell line info to all samples\n", " clinical_data.at[0, sample_id] = sample_characteristics[0][0]\n", " \n", " # Assign treatment info\n", " clinical_data.at[1, sample_id] = unique_treatments[i]\n", " \n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical Data Preview:\", preview)\n", " \n", " # Save the processed clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error processing clinical data: {str(e)}\")\n", " print(\"Clinical data processing skipped.\")\n" ] }, { "cell_type": "markdown", "id": "590f5c7d", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "92e3e0fa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:30.323480Z", "iopub.status.busy": "2025-03-25T08:35:30.323373Z", "iopub.status.idle": "2025-03-25T08:35:30.492362Z", "shell.execute_reply": "2025-03-25T08:35:30.491824Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 66\n", "Header line: \"ID_REF\"\t\"GSM4232834\"\t\"GSM4232835\"\t\"GSM4232836\"\t\"GSM4232837\"\t\"GSM4232838\"\t\"GSM4232839\"\t\"GSM4232840\"\t\"GSM4232841\"\t\"GSM4232842\"\t\"GSM4232843\"\t\"GSM4232844\"\t\"GSM4232845\"\t\"GSM4232846\"\t\"GSM4232847\"\t\"GSM4232848\"\t\"GSM4232849\"\t\"GSM4232850\"\t\"GSM4232851\"\t\"GSM4232852\"\t\"GSM4232853\"\t\"GSM4232854\"\t\"GSM4232855\"\t\"GSM4232856\"\t\"GSM4232857\"\t\"GSM4232858\"\t\"GSM4232859\"\t\"GSM4232860\"\t\"GSM4232861\"\t\"GSM4232862\"\t\"GSM4232863\"\t\"GSM4232864\"\t\"GSM4232865\"\t\"GSM4232866\"\t\"GSM4232867\"\t\"GSM4232868\"\t\"GSM4232869\"\t\"GSM4232870\"\t\"GSM4232871\"\t\"GSM4232872\"\t\"GSM4232873\"\t\"GSM4232874\"\t\"GSM4232875\"\t\"GSM4232876\"\t\"GSM4232877\"\t\"GSM4232878\"\t\"GSM4232879\"\t\"GSM4232880\"\t\"GSM4232881\"\t\"GSM4232882\"\t\"GSM4232883\"\t\"GSM4232884\"\t\"GSM4232885\"\t\"GSM4232886\"\t\"GSM4232887\"\t\"GSM4232888\"\t\"GSM4232889\"\t\"GSM4232890\"\t\"GSM4232891\"\t\"GSM4232892\"\t\"GSM4232893\"\n", "First data line: \"7A5\"\t7.00047\t7.4364\t7.2259\t6.95089\t7.01398\t6.94179\t6.35476\t6.39446\t7.04405\t6.67603\t6.38158\t6.87048\t6.78098\t6.94703\t7.00125\t7.0633\t6.01448\t7.10264\t6.87251\t7.03624\t7.04809\t6.72825\t7.0007\t6.90422\t6.90433\t7.23055\t7.52354\t6.29845\t6.93591\t6.45731\t6.93591\t6.44016\t7.30199\t6.90369\t6.44151\t6.8296\t6.27562\t6.85061\t7.22973\t6.96944\t6.52329\t6.62954\t6.69973\t6.95149\t6.17045\t6.70617\t6.7019\t6.9133\t6.78328\t6.98717\t7.05936\t6.44223\t7.03674\t7.01894\t7.03133\t7.28102\t6.84521\t7.02275\t6.80499\t7.24612\n", "Index(['7A5', 'A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1',\n", " 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS', 'AACSL', 'AADAC',\n", " 'AADACL1', 'AADACL2', 'AADACL3', 'AADACL4'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. First, let's examine the structure of the matrix file to understand its format\n", "import gzip\n", "\n", "# Peek at the first few lines of the file to understand its structure\n", "with gzip.open(matrix_file, 'rt') as file:\n", " # Read first 100 lines to find the header structure\n", " for i, line in enumerate(file):\n", " if '!series_matrix_table_begin' in line:\n", " print(f\"Found data marker at line {i}\")\n", " # Read the next line which should be the header\n", " header_line = next(file)\n", " print(f\"Header line: {header_line.strip()}\")\n", " # And the first data line\n", " first_data_line = next(file)\n", " print(f\"First data line: {first_data_line.strip()}\")\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Matrix table marker not found in first 100 lines\")\n", " break\n", "\n", "# 3. Now try to get the genetic data with better error handling\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(gene_data.index[:20])\n", "except KeyError as e:\n", " print(f\"KeyError: {e}\")\n", " \n", " # Alternative approach: manually extract the data\n", " print(\"\\nTrying alternative approach to read the gene data:\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Find the start of the data\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read the headers and data\n", " import pandas as pd\n", " df = pd.read_csv(file, sep='\\t', index_col=0)\n", " print(f\"Column names: {df.columns[:5]}\")\n", " print(f\"First 20 row IDs: {df.index[:20]}\")\n", " gene_data = df\n" ] }, { "cell_type": "markdown", "id": "d6ed32f3", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "2bc0731c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:30.493873Z", "iopub.status.busy": "2025-03-25T08:35:30.493748Z", "iopub.status.idle": "2025-03-25T08:35:30.495952Z", "shell.execute_reply": "2025-03-25T08:35:30.495587Z" } }, "outputs": [], "source": [ "# Reviewing the gene identifiers\n", "# Looking at the identifiers from the output, we can see entries like:\n", "# '7A5', 'A1BG', 'A1CF', 'A2BP1', etc.\n", "\n", "# These appear to be standard human gene symbols. \n", "# For example:\n", "# - A1BG is Alpha-1-B Glycoprotein\n", "# - A2M is Alpha-2-Macroglobulin\n", "# - AAAS is Achalasia, Adrenocortical Insufficiency, Alacrimia syndrome gene\n", "\n", "# While some identifiers might be less common (like 7A5), the majority appear to be\n", "# standard HGNC gene symbols, so no mapping should be required\n", "\n", "requires_gene_mapping = False\n" ] }, { "cell_type": "markdown", "id": "5aa4c749", "metadata": {}, "source": [ "### Step 5: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 6, "id": "f274558c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:30.497231Z", "iopub.status.busy": "2025-03-25T08:35:30.497121Z", "iopub.status.idle": "2025-03-25T08:35:42.410116Z", "shell.execute_reply": "2025-03-25T08:35:42.409470Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (20747, 60)\n", "First few genes with their expression values after normalization:\n", " GSM4232834 GSM4232835 GSM4232836 GSM4232837 GSM4232838 \\\n", "ID \n", "A1BG 4.79408 4.77433 5.09248 5.12294 5.22396 \n", "A1BG-AS1 4.41521 4.10095 4.19279 4.15799 4.01244 \n", "A1CF 4.47919 4.49296 4.96132 4.61623 4.62902 \n", "A2M 4.18512 3.43994 4.08894 3.50579 3.90165 \n", "A2ML1 4.53153 3.44832 4.08500 2.97268 4.07312 \n", "\n", " GSM4232839 GSM4232840 GSM4232841 GSM4232842 GSM4232843 ... \\\n", "ID ... \n", "A1BG 4.83021 5.07336 4.71037 5.22138 5.04408 ... \n", "A1BG-AS1 4.37280 4.83188 4.62063 4.36214 3.76720 ... \n", "A1CF 4.61928 4.64433 4.49737 4.74431 4.53624 ... \n", "A2M 3.68211 3.59082 3.72203 3.68729 3.36298 ... \n", "A2ML1 2.88517 3.25851 4.20093 4.47530 3.98375 ... \n", "\n", " GSM4232884 GSM4232885 GSM4232886 GSM4232887 GSM4232888 \\\n", "ID \n", "A1BG 4.76957 4.83349 4.89837 4.95678 5.55280 \n", "A1BG-AS1 4.16814 4.50411 4.44726 3.90727 4.08097 \n", "A1CF 4.68982 4.46157 4.54973 4.70105 4.50531 \n", "A2M 3.98965 4.56486 3.47137 3.84234 4.02411 \n", "A2ML1 3.29743 4.00144 3.68519 4.54602 3.94150 \n", "\n", " GSM4232889 GSM4232890 GSM4232891 GSM4232892 GSM4232893 \n", "ID \n", "A1BG 5.31089 4.85788 5.19227 5.00836 4.98561 \n", "A1BG-AS1 4.40857 4.42194 4.27793 3.90266 4.25806 \n", "A1CF 4.34295 4.66214 4.30193 4.12278 4.21548 \n", "A2M 4.41546 3.47600 3.81681 4.10732 3.47600 \n", "A2ML1 3.31699 4.02810 3.84733 3.15712 3.85833 \n", "\n", "[5 rows x 60 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE142610.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found treatment information for 60 samples\n", "Clinical data created with 60 samples\n", "Cystic_Fibrosis\n", "0 52\n", "1 8\n", "Name: count, dtype: int64\n", "Linked data shape: (60, 20748)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (60, 20748)\n", "For the feature 'Cystic_Fibrosis', the least common label is '1' with 8 occurrences. This represents 13.33% of the dataset.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Cystic_Fibrosis/GSE142610.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "print(\"First few genes with their expression values after normalization:\")\n", "print(normalized_gene_data.head())\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Properly extract original clinical data to match GSM IDs\n", "# First, let's get the proper mapping between GSM IDs and treatment conditions\n", "# Extract the relevant lines from the SOFT file for sample information\n", "with gzip.open(soft_file, 'rt') as f:\n", " sample_info_lines = []\n", " current_gsm = None\n", " for line in f:\n", " line = line.strip()\n", " if line.startswith(\"^SAMPLE = \"):\n", " current_gsm = line.split(\" = \")[1]\n", " elif line.startswith(\"!Sample_title\") and current_gsm:\n", " title = line.split(\" = \")[1].strip('\"')\n", " sample_info_lines.append((current_gsm, title))\n", "\n", "# Create a mapping of GSM IDs to treatment conditions\n", "gsm_to_treatment = {}\n", "for gsm, title in sample_info_lines:\n", " gsm_to_treatment[gsm] = title\n", "\n", "print(f\"Found treatment information for {len(gsm_to_treatment)} samples\")\n", "\n", "# Create clinical data with real GSM IDs\n", "clinical_data = pd.DataFrame(index=normalized_gene_data.columns)\n", "\n", "# Assign trait values based on treatment descriptions\n", "# 1 = CF-like condition (control)\n", "# 0 = Rescue intervention\n", "clinical_data[trait] = clinical_data.index.map(lambda gsm: 1 if any(x in gsm_to_treatment.get(gsm, \"\").lower() \n", " for x in ['dmso', 'scrambled', 'control', 'untreated']) \n", " else 0 if gsm in gsm_to_treatment else None)\n", "\n", "print(f\"Clinical data created with {len(clinical_data)} samples\")\n", "print(clinical_data[trait].value_counts())\n", "\n", "# Link the clinical and genetic data\n", "linked_data = clinical_data.join(normalized_gene_data.T)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 3. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 4. Determine whether the trait is biased\n", "trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n", "if trait_type == \"binary\":\n", " is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n", "else:\n", " is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n", "\n", "# 5. Conduct final quality validation and save cohort information\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=is_trait_biased, \n", " df=linked_data,\n", " note=f\"Dataset contains gene expression data comparing rescue interventions with control conditions in CFBE cells.\"\n", ")\n", "\n", "# 6. If the linked data is usable, save it\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Data was determined to be unusable and was not saved\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }