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{
"cells": [
{
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"execution": {
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"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"
]
},
{
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"id": "2db494d1",
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{
"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": {
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"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": {
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"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": {
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"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.\")"
]
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