{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "88c09c10", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:28.102100Z", "iopub.status.busy": "2025-03-25T05:09:28.101915Z", "iopub.status.idle": "2025-03-25T05:09:28.285425Z", "shell.execute_reply": "2025-03-25T05:09:28.285090Z" } }, "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 = \"Epilepsy\"\n", "cohort = \"GSE63808\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Epilepsy\"\n", "in_cohort_dir = \"../../input/GEO/Epilepsy/GSE63808\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Epilepsy/GSE63808.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Epilepsy/gene_data/GSE63808.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Epilepsy/clinical_data/GSE63808.csv\"\n", "json_path = \"../../output/preprocess/Epilepsy/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "bb9c5e30", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "a5d3c922", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:28.286801Z", "iopub.status.busy": "2025-03-25T05:09:28.286659Z", "iopub.status.idle": "2025-03-25T05:09:28.570636Z", "shell.execute_reply": "2025-03-25T05:09:28.570319Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"chronic temporal lobe epilepsy: biopsy hippocampus\"\n", "!Series_summary\t\"Analysis of biopsy hippocampal tissue of patients with pharmacoresistant temporal lobe epilepsy (TLE) undergoing neurosurgical removal of the epileptogenic focus for seizure control. Chronic TLE goes along with focal hyperexcitability. Results provide insight into molecular mechanisms that may play a role in seizure propensity\"\n", "!Series_overall_design\t\"129 human hippocampus samples\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: hippocampal formation'], 1: ['phenotype: epilepsy']}\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": "220f4b67", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "23eb002a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:28.571823Z", "iopub.status.busy": "2025-03-25T05:09:28.571710Z", "iopub.status.idle": "2025-03-25T05:09:28.577668Z", "shell.execute_reply": "2025-03-25T05:09:28.577394Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, the dataset is about human hippocampus gene expression\n", "# in epilepsy patients, which suggests gene expression data is available.\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary:\n", "# Key 1 corresponds to 'phenotype: epilepsy' which is our trait\n", "trait_row = 1\n", "# Age is not available in the sample characteristics\n", "age_row = None\n", "# Gender is not available in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (1 for epilepsy, 0 for control)\"\"\"\n", " if value is None:\n", " return None\n", " # Extract the value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " # Based on the sample characteristics, all samples have epilepsy\n", " # This is a constant feature which isn't useful for association studies\n", " if 'epilepsy' in value:\n", " return 1\n", " # For completeness, though not present in this dataset\n", " elif 'control' in value or 'normal' in value or 'healthy' in value:\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous\"\"\"\n", " # Not applicable as age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n", " # Not applicable as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Since all samples have the same trait value (all are epilepsy cases),\n", "# this is a constant feature and not useful for association studies\n", "is_trait_available = False\n", "\n", "# Validate and save cohort info\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", "# Skipping this step since trait data is not variable (constant feature)\n", "# and the required clinical_data.csv file doesn't exist in the specified path\n" ] }, { "cell_type": "markdown", "id": "ed365d91", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "c73fddcf", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:28.578779Z", "iopub.status.busy": "2025-03-25T05:09:28.578675Z", "iopub.status.idle": "2025-03-25T05:09:29.223992Z", "shell.execute_reply": "2025-03-25T05:09:29.223625Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Epilepsy/GSE63808/GSE63808_family.soft.gz\n", "Matrix file: ../../input/GEO/Epilepsy/GSE63808/GSE63808_series_matrix.txt.gz\n", "Found the matrix table marker in the file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (48803, 129)\n", "First 20 gene/probe identifiers:\n", "['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209', 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229', 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253', 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262']\n" ] } ], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for line in file:\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " break\n", " \n", " if found_marker:\n", " print(\"Found the matrix table marker in the file.\")\n", " else:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " \n", " # Try to extract gene data from the matrix file\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " is_gene_available = False\n", " \n", " # Try to diagnose the file format\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if i < 10: # Print first 10 lines to diagnose\n", " print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", "\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n" ] }, { "cell_type": "markdown", "id": "fed50090", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9317d6cd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:29.225253Z", "iopub.status.busy": "2025-03-25T05:09:29.225148Z", "iopub.status.idle": "2025-03-25T05:09:29.226965Z", "shell.execute_reply": "2025-03-25T05:09:29.226700Z" } }, "outputs": [], "source": [ "# Analyze the gene identifiers from the output\n", "# The identifiers starting with \"ILMN_\" are Illumina microarray probe IDs\n", "# These are not human gene symbols and need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "3c1d2f4a", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "2af1eef2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:29.227982Z", "iopub.status.busy": "2025-03-25T05:09:29.227882Z", "iopub.status.idle": "2025-03-25T05:09:40.800523Z", "shell.execute_reply": "2025-03-25T05:09:40.800204Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'nuID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE', 'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband', 'Definition', 'Ontology_Component', 'Ontology_Process', 'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC']\n", "{'ID': ['ILMN_1725881', 'ILMN_1910180', 'ILMN_1804174', 'ILMN_1796063', 'ILMN_1811966'], 'nuID': ['rp13_p1x6D80lNLk3c', 'NEX0oqCV8.er4HVfU4', 'KyqQynMZxJcruyylEU', 'xXl7eXuF7sbPEp.KFI', '9ckqJrioiaej9_ajeQ'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['RefSeq', 'Unigene', 'RefSeq', 'RefSeq', 'RefSeq'], 'Search_Key': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756'], 'Transcript': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756'], 'ILMN_Gene': ['LOC23117', 'HS.575038', 'FCGR2B', 'TRIM44', 'LOC653895'], 'Source_Reference_ID': ['XM_933824.1', 'Hs.575038', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'RefSeq_ID': ['XM_933824.1', nan, 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'Unigene_ID': [nan, 'Hs.575038', nan, nan, nan], 'Entrez_Gene_ID': [23117.0, nan, 2213.0, 54765.0, 653895.0], 'GI': [89040007.0, 10437021.0, 88952550.0, 29029528.0, 89033487.0], 'Accession': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'Symbol': ['LOC23117', nan, 'FCGR2B', 'TRIM44', 'LOC653895'], 'Protein_Product': ['XP_938917.1', nan, 'XP_943944.1', 'NP_060053.2', 'XP_941472.1'], 'Array_Address_Id': [1710221.0, 5900364.0, 2480717.0, 1300239.0, 4480719.0], 'Probe_Type': ['I', 'S', 'I', 'S', 'S'], 'Probe_Start': [122.0, 1409.0, 1643.0, 2901.0, 25.0], 'SEQUENCE': ['GGCTCCTCTTTGGGCTCCTACTGGAATTTATCAGCCATCAGTGCATCTCT', 'ACACCTTCAGGAGGGAAGCCCTTATTTCTGGGTTGAACTCCCCTTCCATG', 'TAGGGGCAATAGGCTATACGCTACAGCCTAGGTGTGTAGTAGGCCACACC', 'CCTGCCTGTCTGCCTGTGACCTGTGTACGTATTACAGGCTTTAGGACCAG', 'CTAGCAGGGAGCGGTGAGGGAGAGCGGCTGGATTTCTTGCGGGATCTGCA'], 'Chromosome': ['16', nan, nan, '11', nan], 'Probe_Chr_Orientation': ['-', nan, nan, '+', nan], 'Probe_Coordinates': ['21766363-21766363:21769901-21769949', nan, nan, '35786070-35786119', nan], 'Cytoband': ['16p12.2a', nan, '1q23.3b', '11p13a', '10q11.23b'], 'Definition': ['PREDICTED: Homo sapiens KIAA0220-like protein, transcript variant 11 (LOC23117), mRNA.', 'Homo sapiens cDNA: FLJ21027 fis, clone CAE07110', 'PREDICTED: Homo sapiens Fc fragment of IgG, low affinity IIb, receptor (CD32) (FCGR2B), mRNA.', 'Homo sapiens tripartite motif-containing 44 (TRIM44), mRNA.', 'PREDICTED: Homo sapiens similar to protein geranylgeranyltransferase type I, beta subunit (LOC653895), mRNA.'], 'Ontology_Component': [nan, nan, nan, 'intracellular [goid 5622] [evidence IEA]', nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, 'zinc ion binding [goid 8270] [evidence IEA]; metal ion binding [goid 46872] [evidence IEA]', nan], 'Synonyms': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan], 'Obsolete_Probe_Id': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan], 'GB_ACC': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1']}\n", "\n", "Sample of Description column (first 5 rows):\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Based on the preview, 'ID' appears to be the probe ID and 'Description' contains gene names\n", "# Display more samples from the Description column to better understand the format\n", "print(\"\\nSample of Description column (first 5 rows):\")\n", "if 'Description' in gene_annotation.columns:\n", " for i in range(min(5, len(gene_annotation))):\n", " print(f\"Row {i}: {gene_annotation['Description'].iloc[i]}\")\n" ] }, { "cell_type": "markdown", "id": "912e4e61", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "3fe64fea", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:40.801984Z", "iopub.status.busy": "2025-03-25T05:09:40.801864Z", "iopub.status.idle": "2025-03-25T05:09:43.081043Z", "shell.execute_reply": "2025-03-25T05:09:43.080544Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (36157, 2)\n", "Preview of gene_mapping dataframe:\n", "{'ID': ['ILMN_1725881', 'ILMN_1804174', 'ILMN_1796063', 'ILMN_1811966', 'ILMN_1668162'], 'Gene': ['LOC23117', 'FCGR2B', 'TRIM44', 'LOC653895', 'DGAT2L3']}\n", "\n", "Applying gene mapping to convert probes to gene symbols...\n", "After mapping: gene_data shape: (19120, 129)\n", "First few genes in the expression data:\n", "['A1BG', 'A1CF', 'A26A1', 'A26B1', 'A26C1B', 'A26C3', 'A2BP1', 'A2M', 'A2ML1', 'A3GALT2']\n", "Number of unique genes: 19120\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to: ../../output/preprocess/Epilepsy/gene_data/GSE63808.csv\n" ] } ], "source": [ "# 1. Identify the columns needed for mapping\n", "# 'ID' column in gene_annotation contains the probe identifiers (ILMN_*)\n", "# 'Symbol' column contains the gene symbols we need to map to\n", "prob_col = 'ID'\n", "gene_col = 'Symbol'\n", "\n", "# 2. Get the gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n", "print(\"Preview of gene_mapping dataframe:\")\n", "print(preview_df(gene_mapping, n=5))\n", "\n", "# 3. Convert probe-level measurements to gene expression data\n", "print(\"\\nApplying gene mapping to convert probes to gene symbols...\")\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"After mapping: gene_data shape: {gene_data.shape}\")\n", "print(\"First few genes in the expression data:\")\n", "print(gene_data.index[:10].tolist())\n", "\n", "# Check the number of unique genes after mapping\n", "print(f\"Number of unique genes: {len(gene_data)}\")\n", "\n", "# Save the processed gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to: {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "258d7812", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "df826f3d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:43.082478Z", "iopub.status.busy": "2025-03-25T05:09:43.082347Z", "iopub.status.idle": "2025-03-25T05:09:50.739091Z", "shell.execute_reply": "2025-03-25T05:09:50.738692Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (19120, 129)\n", "Gene data shape after normalization: (18326, 129)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Epilepsy/gene_data/GSE63808.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Extracted clinical data shape: (1, 129)\n", "Preview of clinical data (first 5 samples):\n", " GSM1565578 GSM1565579 GSM1565580 GSM1565581 GSM1565582\n", "Epilepsy 1.0 1.0 1.0 1.0 1.0\n", "Clinical data saved to ../../output/preprocess/Epilepsy/clinical_data/GSE63808.csv\n", "Gene data columns (first 5): ['GSM1565578', 'GSM1565579', 'GSM1565580', 'GSM1565581', 'GSM1565582']\n", "Clinical data columns (first 5): ['GSM1565578', 'GSM1565579', 'GSM1565580', 'GSM1565581', 'GSM1565582']\n", "Found 129 common samples between gene and clinical data\n", "Initial linked data shape: (129, 18327)\n", "Preview of linked data (first 5 rows, first 5 columns):\n", " Epilepsy A1BG A1CF A2M A2ML1\n", "GSM1565578 1.0 171.960000 264.260000 431.243333 90.660000\n", "GSM1565579 1.0 170.456667 261.040000 326.450000 101.096667\n", "GSM1565580 1.0 175.460000 260.106667 331.516667 106.350000\n", "GSM1565581 1.0 176.813333 254.776667 665.113333 89.360000\n", "GSM1565582 1.0 178.753333 254.280000 326.263333 95.166667\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (129, 18327)\n", "Quartiles for 'Epilepsy':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'Epilepsy' in this dataset is severely biased.\n", "\n", "Data not usable for the trait study - not saving final linked data.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Make sure the directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Use the gene_data variable from the previous step (don't try to load it from file)\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " # Use the normalized data for further processing\n", " gene_data = normalized_gene_data\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load clinical data - respecting the analysis from Step 2\n", "# From Step 2, we determined:\n", "# trait_row = None # No Epilepsy data available\n", "# age_row = None\n", "# gender_row = None\n", "is_trait_available = trait_row is not None\n", "\n", "# Skip clinical feature extraction when trait_row is None\n", "if is_trait_available:\n", " try:\n", " # Load the clinical data from file\n", " soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", " \n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender,\n", " age_row=age_row,\n", " convert_age=convert_age\n", " )\n", " \n", " print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n", " print(\"Preview of clinical data (first 5 samples):\")\n", " print(clinical_features.iloc[:, :5])\n", " \n", " # Save the properly extracted clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error extracting clinical data: {e}\")\n", " is_trait_available = False\n", "else:\n", " print(\"No trait data (Epilepsy) available in this dataset based on previous analysis.\")\n", "\n", "# 3. Link clinical and genetic data if both are available\n", "if is_trait_available and is_gene_available:\n", " try:\n", " # Debug the column names to ensure they match\n", " print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n", " print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n", " \n", " # Check for common sample IDs\n", " common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n", " print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n", " \n", " if len(common_samples) > 0:\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Debug the trait values before handling missing values\n", " print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate the data quality and save cohort info\n", " note = \"Dataset contains gene expression data from GBM cell cultures, but no epilepsy phenotype 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=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", " )\n", " \n", " # Save the linked data if it's usable\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 not usable for the trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " else:\n", " print(\"No common samples found between gene expression and clinical data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No common samples between gene expression and clinical data.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error linking or processing data: {e}\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Assume biased if there's an error\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=f\"Error in data processing: {str(e)}\"\n", " )\n", "else:\n", " # Create an empty DataFrame for metadata purposes\n", " empty_df = pd.DataFrame()\n", " \n", " # We can't proceed with linking if either trait or gene data is missing\n", " print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Data is unusable if we're missing components\n", " df=empty_df, # Empty dataframe for metadata\n", " note=\"Missing essential data components for linking: dataset contains gene expression data from GBM cell cultures, but no epilepsy phenotype information.\"\n", " )" ] } ], "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 }