{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "16c30f84", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:10:32.651638Z", "iopub.status.busy": "2025-03-25T05:10:32.651529Z", "iopub.status.idle": "2025-03-25T05:10:32.816817Z", "shell.execute_reply": "2025-03-25T05:10:32.816473Z" } }, "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", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Epilepsy/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Epilepsy/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Epilepsy/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Epilepsy/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "e7755166", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "2db494d1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:10:32.818329Z", "iopub.status.busy": "2025-03-25T05:10:32.818187Z", "iopub.status.idle": "2025-03-25T05:10:34.439784Z", "shell.execute_reply": "2025-03-25T05:10:34.439390Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking for a relevant cohort directory for Epilepsy...\n", "Available cohorts: ['TCGA_Liver_Cancer_(LIHC)', 'TCGA_Lower_Grade_Glioma_(LGG)', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Mesothelioma_(MESO)', 'TCGA_Ocular_melanomas_(UVM)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Thymoma_(THYM)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Uterine_Carcinosarcoma_(UCS)', '.DS_Store', 'CrawlData.ipynb', 'TCGA_Acute_Myeloid_Leukemia_(LAML)', 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)']\n", "Epilepsy/neurological disease-related cohorts: ['TCGA_Lower_Grade_Glioma_(LGG)', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Glioblastoma_(GBM)']\n", "Selected cohort: TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)\n", "Clinical data file: TCGA.GBMLGG.sampleMap_GBMLGG_clinicalMatrix\n", "Genetic data file: TCGA.GBMLGG.sampleMap_HiSeqV2_PANCAN.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Clinical data columns:\n", "['_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_surgery_locoregional_procedure', 'additional_surgery_metastatic_procedure', 'age_at_initial_pathologic_diagnosis', 'animal_insect_allergy_history', 'animal_insect_allergy_types', 'asthma_history', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'days_to_additional_surgery_locoregional_procedure', 'days_to_additional_surgery_metastatic_procedure', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'days_to_performance_status_assessment', 'eastern_cancer_oncology_group', 'eczema_history', 'family_history_of_cancer', 'family_history_of_primary_brain_tumor', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', 'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy', 'first_presenting_symptom', 'first_presenting_symptom_longest_duration', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'food_allergy_history', 'food_allergy_types', 'form_completion_date', 'gender', 'hay_fever_history', 'headache_history', 'histological_type', 'history_ionizing_rt_to_head', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'inherited_genetic_syndrome_found', 'inherited_genetic_syndrome_result', 'initial_pathologic_diagnosis_method', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'karnofsky_performance_score', 'laterality', 'ldh1_mutation_found', 'ldh1_mutation_test_method', 'ldh1_mutation_tested', 'longest_dimension', 'lost_follow_up', 'mental_status_changes', 'mold_or_dust_allergy_history', 'motor_movement_changes', 'neoplasm_histologic_grade', 'new_neoplasm_event_type', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'preoperative_antiseizure_meds', 'preoperative_corticosteroids', 'primary_therapy_outcome_success', 'prior_glioma', 'radiation_therapy', 'sample_type', 'sample_type_id', 'seizure_history', 'sensory_changes', 'shortest_dimension', 'supratentorial_localization', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_location', 'tumor_tissue_site', 'vial_number', 'visual_changes', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_GBMLGG_exp_HiSeqV2', '_GENOMIC_ID_TCGA_GBMLGG_PDMarrayCNV', '_GENOMIC_ID_TCGA_GBMLGG_mutation', '_GENOMIC_ID_TCGA_GBMLGG_hMethyl450', '_GENOMIC_ID_TCGA_GBMLGG_PDMarray', '_GENOMIC_ID_TCGA_GBMLGG_gistic2', '_GENOMIC_ID_TCGA_GBMLGG_PDMRNAseq', '_GENOMIC_ID_TCGA_GBMLGG_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_GBMLGG_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_GBMLGG_gistic2thd', '_GENOMIC_ID_TCGA_GBMLGG_exp_HiSeqV2_exon']\n", "\n", "Clinical data shape: (1148, 115)\n", "Genetic data shape: (20530, 702)\n" ] } ], "source": [ "import os\n", "\n", "# Check if there's a suitable cohort directory for Epilepsy\n", "print(f\"Looking for a relevant cohort directory for {trait}...\")\n", "\n", "# Check available cohorts\n", "available_dirs = os.listdir(tcga_root_dir)\n", "print(f\"Available cohorts: {available_dirs}\")\n", "\n", "# Epilepsy-related keywords (looking for neurological/brain conditions that could be related to epilepsy)\n", "epilepsy_keywords = ['epilepsy', 'seizure', 'neurological', 'brain', 'glioma', 'gbm', 'lgg']\n", "\n", "# Look for epilepsy/neurological disease-related directories\n", "epilepsy_related_dirs = []\n", "for d in available_dirs:\n", " if any(keyword in d.lower() for keyword in epilepsy_keywords):\n", " epilepsy_related_dirs.append(d)\n", "\n", "print(f\"Epilepsy/neurological disease-related cohorts: {epilepsy_related_dirs}\")\n", "\n", "if not epilepsy_related_dirs:\n", " print(f\"No suitable cohort found for {trait}.\")\n", " # Mark the task as completed by recording the unavailability\n", " validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=False,\n", " is_trait_available=False\n", " )\n", " # Exit the script early since no suitable cohort was found\n", " selected_cohort = None\n", "else:\n", " # For epilepsy, the lower grade glioma and glioblastoma combined dataset might be most relevant\n", " # as these brain tumors are often associated with seizures\n", " if 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)' in epilepsy_related_dirs:\n", " selected_cohort = 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)'\n", " else:\n", " selected_cohort = epilepsy_related_dirs[0] # Use the first match if the preferred one isn't available\n", "\n", "if selected_cohort:\n", " print(f\"Selected cohort: {selected_cohort}\")\n", " \n", " # Get the full path to the selected cohort directory\n", " cohort_dir = os.path.join(tcga_root_dir, selected_cohort)\n", " \n", " # Get the clinical and genetic data file paths\n", " clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", " \n", " print(f\"Clinical data file: {os.path.basename(clinical_file_path)}\")\n", " print(f\"Genetic data file: {os.path.basename(genetic_file_path)}\")\n", " \n", " # Load the clinical and genetic data\n", " clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\n", " genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\\t')\n", " \n", " # Print the column names of the clinical data\n", " print(\"\\nClinical data columns:\")\n", " print(clinical_df.columns.tolist())\n", " \n", " # Basic info about the datasets\n", " print(f\"\\nClinical data shape: {clinical_df.shape}\")\n", " print(f\"Genetic data shape: {genetic_df.shape}\")\n" ] }, { "cell_type": "markdown", "id": "199e7ebd", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "df0f42a1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:10:34.441126Z", "iopub.status.busy": "2025-03-25T05:10:34.441012Z", "iopub.status.idle": "2025-03-25T05:10:34.455950Z", "shell.execute_reply": "2025-03-25T05:10:34.455645Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age columns preview:\n", "{'age_at_initial_pathologic_diagnosis': [44.0, 50.0, 59.0, 56.0, 40.0], 'days_to_birth': [-16179.0, -18341.0, -21617.0, -20516.0, -14806.0], 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust': [nan, nan, nan, nan, nan], 'first_diagnosis_age_of_animal_insect_allergy': [nan, nan, nan, nan, nan], 'first_diagnosis_age_of_food_allergy': [nan, nan, nan, nan, nan]}\n", "\n", "Gender columns preview:\n", "{'gender': ['FEMALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']}\n" ] } ], "source": [ "# 1. Identify candidate columns for age and gender\n", "candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', \n", " 'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy']\n", "candidate_gender_cols = ['gender']\n", "\n", "# 2. Get the clinical data file path\n", "cohort_dir = os.path.join(tcga_root_dir, 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)')\n", "clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n", "\n", "# Load the clinical data\n", "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n", "\n", "# Extract candidate columns and preview\n", "age_preview = {}\n", "for col in candidate_age_cols:\n", " if col in clinical_df.columns:\n", " age_preview[col] = clinical_df[col].head(5).tolist()\n", "\n", "gender_preview = {}\n", "for col in candidate_gender_cols:\n", " if col in clinical_df.columns:\n", " gender_preview[col] = clinical_df[col].head(5).tolist()\n", "\n", "print(\"Age columns preview:\")\n", "print(age_preview)\n", "print(\"\\nGender columns preview:\")\n", "print(gender_preview)\n" ] }, { "cell_type": "markdown", "id": "c2137589", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "05ac3b28", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:10:34.457235Z", "iopub.status.busy": "2025-03-25T05:10:34.457121Z", "iopub.status.idle": "2025-03-25T05:10:34.460372Z", "shell.execute_reply": "2025-03-25T05:10:34.460076Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected age column: age_at_initial_pathologic_diagnosis\n", "Age column preview: [44.0, 50.0, 59.0, 56.0, 40.0]\n", "Selected gender column: gender\n", "Gender column preview: ['FEMALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']\n" ] } ], "source": [ "# Analyze age columns\n", "age_columns = {\n", " 'age_at_initial_pathologic_diagnosis': [44.0, 50.0, 59.0, 56.0, 40.0], \n", " 'days_to_birth': [-16179.0, -18341.0, -21617.0, -20516.0, -14806.0], \n", " 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')], \n", " 'first_diagnosis_age_of_animal_insect_allergy': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')], \n", " 'first_diagnosis_age_of_food_allergy': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')]\n", "}\n", "\n", "# Select age column - choose between age_at_initial_pathologic_diagnosis and days_to_birth\n", "# age_at_initial_pathologic_diagnosis is more directly usable than days_to_birth (which is negative)\n", "age_col = 'age_at_initial_pathologic_diagnosis'\n", "\n", "# Analyze gender columns\n", "gender_columns = {'gender': ['FEMALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']}\n", "\n", "# Select gender column - only one candidate is available\n", "gender_col = 'gender'\n", "\n", "# Print the selected columns and their values\n", "print(f\"Selected age column: {age_col}\")\n", "print(f\"Age column preview: {age_columns[age_col]}\")\n", "print(f\"Selected gender column: {gender_col}\")\n", "print(f\"Gender column preview: {gender_columns[gender_col]}\")\n" ] }, { "cell_type": "markdown", "id": "e5c27a96", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "f6ad9274", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:10:34.461455Z", "iopub.status.busy": "2025-03-25T05:10:34.461349Z", "iopub.status.idle": "2025-03-25T05:11:46.863514Z", "shell.execute_reply": "2025-03-25T05:11:46.863122Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical features (first 5 rows):\n", " Epilepsy Age Gender\n", "sampleID \n", "TCGA-02-0001-01 1 44.0 0.0\n", "TCGA-02-0003-01 1 50.0 1.0\n", "TCGA-02-0004-01 1 59.0 1.0\n", "TCGA-02-0006-01 1 56.0 0.0\n", "TCGA-02-0007-01 1 40.0 0.0\n", "\n", "Processing gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Original gene data shape: (20530, 702)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Attempting to normalize gene symbols...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (19848, 702)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to: ../../output/preprocess/Epilepsy/gene_data/TCGA.csv\n", "\n", "Linking clinical and genetic data...\n", "Clinical data shape: (1148, 3)\n", "Genetic data shape: (19848, 702)\n", "Number of common samples: 702\n", "\n", "Linked data shape: (702, 19851)\n", "Linked data preview (first 5 rows, first few columns):\n", " Epilepsy Age Gender A1BG A1BG-AS1\n", "TCGA-FG-A4MU-01 1 58.0 1.0 4.236714 -1.467213\n", "TCGA-HT-7478-01 1 36.0 1.0 2.646014 -2.607613\n", "TCGA-DU-A6S2-01 1 37.0 0.0 2.751714 -2.326013\n", "TCGA-QH-A6CZ-01 1 38.0 1.0 1.255714 -2.867213\n", "TCGA-DU-7292-01 1 69.0 1.0 3.046014 -1.780413\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Data shape after handling missing values: (702, 19851)\n", "\n", "Checking for bias in features:\n", "For the feature 'Epilepsy', the least common label is '0' with 5 occurrences. This represents 0.71% of the dataset.\n", "The distribution of the feature 'Epilepsy' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 34.0\n", " 50% (Median): 46.0\n", " 75%: 59.0\n", "Min: 14.0\n", "Max: 89.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 297 occurrences. This represents 42.31% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "\n", "Performing final validation...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to: ../../output/preprocess/Epilepsy/TCGA.csv\n", "Clinical data saved to: ../../output/preprocess/Epilepsy/clinical_data/TCGA.csv\n" ] } ], "source": [ "# 1. Extract and standardize clinical features\n", "# Use tcga_select_clinical_features which will automatically create the trait variable and add age/gender if provided\n", "# Use the correct cohort identified in Step 1\n", "cohort_dir = os.path.join(tcga_root_dir, 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)')\n", "clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", "\n", "# Load the clinical data if not already loaded\n", "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n", "\n", "linked_clinical_df = tcga_select_clinical_features(\n", " clinical_df, \n", " trait=trait, \n", " age_col=age_col, \n", " gender_col=gender_col\n", ")\n", "\n", "# Print preview of clinical features\n", "print(\"Clinical features (first 5 rows):\")\n", "print(linked_clinical_df.head())\n", "\n", "# 2. Process gene expression data\n", "print(\"\\nProcessing gene expression data...\")\n", "# Load genetic data from the same cohort directory\n", "genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n", "\n", "# Check gene data shape\n", "print(f\"Original gene data shape: {genetic_df.shape}\")\n", "\n", "# Save a version of the gene data before normalization (as a backup)\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "genetic_df.to_csv(out_gene_data_file.replace('.csv', '_original.csv'))\n", "\n", "# We need to transpose genetic data so genes are rows and samples are columns for normalization\n", "gene_df_for_norm = genetic_df.copy() # Keep original orientation for now\n", "\n", "# Try to normalize gene symbols - adding debug output to understand what's happening\n", "print(\"Attempting to normalize gene symbols...\")\n", "try:\n", " # First check if we need to transpose based on the data format\n", " # In TCGA data, typically genes are rows and samples are columns\n", " if gene_df_for_norm.shape[0] > gene_df_for_norm.shape[1]:\n", " # More rows than columns, likely genes are rows already\n", " normalized_gene_df = normalize_gene_symbols_in_index(gene_df_for_norm)\n", " else:\n", " # Need to transpose first\n", " normalized_gene_df = normalize_gene_symbols_in_index(gene_df_for_norm.T)\n", " \n", " print(f\"Gene data shape after normalization: {normalized_gene_df.shape}\")\n", " \n", " # Check if normalization returned empty DataFrame\n", " if normalized_gene_df.shape[0] == 0:\n", " print(\"WARNING: Gene symbol normalization returned an empty DataFrame.\")\n", " print(\"Using original gene data instead of normalized data.\")\n", " # Use original data\n", " normalized_gene_df = genetic_df\n", " \n", "except Exception as e:\n", " print(f\"Error during gene symbol normalization: {e}\")\n", " print(\"Using original gene data instead.\")\n", " normalized_gene_df = genetic_df\n", "\n", "# Save gene data\n", "normalized_gene_df.to_csv(out_gene_data_file)\n", "print(f\"Gene data saved to: {out_gene_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "# TCGA data uses the same sample IDs in both datasets\n", "print(\"\\nLinking clinical and genetic data...\")\n", "print(f\"Clinical data shape: {linked_clinical_df.shape}\")\n", "print(f\"Genetic data shape: {normalized_gene_df.shape}\")\n", "\n", "# Find common samples between clinical and genetic data\n", "# In TCGA, samples are typically columns in the gene data and index in the clinical data\n", "common_samples = set(linked_clinical_df.index).intersection(set(normalized_gene_df.columns))\n", "print(f\"Number of common samples: {len(common_samples)}\")\n", "\n", "if len(common_samples) == 0:\n", " print(\"ERROR: No common samples found between clinical and genetic data.\")\n", " # Try the alternative orientation\n", " common_samples = set(linked_clinical_df.index).intersection(set(normalized_gene_df.index))\n", " print(f\"Checking alternative orientation: {len(common_samples)} common samples found.\")\n", " \n", " if len(common_samples) == 0:\n", " # Use is_final=False mode which doesn't require df and is_biased\n", " validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True\n", " )\n", " print(\"The dataset was determined to be unusable for this trait due to no common samples. No data files were saved.\")\n", "else:\n", " # Filter clinical data to only include common samples\n", " linked_clinical_df = linked_clinical_df.loc[list(common_samples)]\n", " \n", " # Create linked data by merging\n", " linked_data = pd.concat([linked_clinical_df, normalized_gene_df[list(common_samples)].T], axis=1)\n", " \n", " print(f\"\\nLinked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first few columns):\")\n", " display_cols = [trait, 'Age', 'Gender'] + list(linked_data.columns[3:5])\n", " print(linked_data[display_cols].head())\n", " \n", " # 4. Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"\\nData shape after handling missing values: {linked_data.shape}\")\n", " \n", " # 5. Check for bias in features\n", " print(\"\\nChecking for bias in features:\")\n", " is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # 6. Validate and save cohort info\n", " print(\"\\nPerforming final validation...\")\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=len(linked_data.columns) > 3, # More than just trait/age/gender columns\n", " is_trait_available=trait in linked_data.columns,\n", " is_biased=is_trait_biased,\n", " df=linked_data,\n", " note=\"Data from TCGA lower-grade glioma and glioblastoma cohort used for epilepsy analysis.\"\n", " )\n", " \n", " # 7. Save linked data if 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", " \n", " # Also save clinical data separately\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_columns = [col for col in linked_data.columns if col in [trait, 'Age', 'Gender']]\n", " linked_data[clinical_columns].to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n", " else:\n", " print(\"The dataset was determined to be unusable for this trait. No data files were 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 }