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{
"cells": [
{
"cell_type": "code",
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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 = \"Arrhythmia\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Arrhythmia/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Arrhythmia/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Arrhythmia/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Arrhythmia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "eaeb3d1f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"metadata": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking for a relevant cohort directory for Arrhythmia...\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",
"Cardiac-related cohorts: []\n",
"No direct cardiac cohorts found. Looking for possible related cohorts...\n",
"Possible related cohorts: ['TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Thymoma_(THYM)']\n",
"Selected cohort: TCGA_Lung_Adenocarcinoma_(LUAD)\n",
"Clinical data file: TCGA.LUAD.sampleMap_LUAD_clinicalMatrix\n",
"Genetic data file: TCGA.LUAD.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['ABSOLUTE_Ploidy', 'ABSOLUTE_Purity', 'AKT1', 'ALK_translocation', 'BRAF', 'CBL', 'CTNNB1', 'Canonical_mut_in_KRAS_EGFR_ALK', 'Cnncl_mt_n_KRAS_EGFR_ALK_RET_ROS1_BRAF_ERBB2_HRAS_NRAS_AKT1_MAP2', 'EGFR', 'ERBB2', 'ERBB4', 'Estimated_allele_fraction_of_a_clonal_varnt_prsnt_t_1_cpy_pr_cll', 'Expression_Subtype', 'HRAS', 'KRAS', 'MAP2K1', 'MET', 'NRAS', 'PIK3CA', 'PTPN11', 'Pathology', 'Pathology_Updated', 'RET_translocation', 'ROS1_translocation', 'STK11', 'WGS_as_of_20120731_0_no_1_yes', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_LUAD', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_LUAD', '_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', 'anatomic_neoplasm_subdivision', 'anatomic_neoplasm_subdivision_other', '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_after_initial_treatment', 'disease_code', 'dlco_predictive_percent', 'eastern_cancer_oncology_group', 'egfr_mutation_performed', 'egfr_mutation_result', 'eml4_alk_translocation_method', 'eml4_alk_translocation_performed', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'karnofsky_performance_score', 'kras_gene_analysis_performed', 'kras_mutation_found', 'kras_mutation_result', 'location_in_lung_parenchyma', 'longest_dimension', 'lost_follow_up', 'new_neoplasm_event_type', 'new_tumor_event_after_initial_treatment', 'number_pack_years_smoked', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'post_bronchodilator_fev1_fvc_percent', 'post_bronchodilator_fev1_percent', 'pre_bronchodilator_fev1_fvc_percent', 'pre_bronchodilator_fev1_percent', 'primary_therapy_outcome_success', 'progression_determined_by', 'project_code', 'pulmonary_function_test_performed', 'radiation_therapy', 'residual_tumor', 'sample_type', 'sample_type_id', 'shortest_dimension', 'stopped_smoking_year', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'tobacco_smoking_history_indicator', 'tumor_tissue_site', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', 'year_of_tobacco_smoking_onset', '_GENOMIC_ID_TCGA_LUAD_mutation', '_GENOMIC_ID_TCGA_LUAD_mutation_curated_broad_gene', '_GENOMIC_ID_TCGA_LUAD_PDMarray', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2', '_GENOMIC_ID_TCGA_LUAD_G4502A_07_3', '_GENOMIC_ID_TCGA_LUAD_hMethyl27', '_GENOMIC_ID_data/public/TCGA/LUAD/miRNA_GA_gene', '_GENOMIC_ID_TCGA_LUAD_gistic2', '_GENOMIC_ID_TCGA_LUAD_hMethyl450', '_GENOMIC_ID_TCGA_LUAD_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_LUAD_gistic2thd', '_GENOMIC_ID_TCGA_LUAD_PDMarrayCNV', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_LUAD_miRNA_HiSeq', '_GENOMIC_ID_TCGA_LUAD_RPPA_RBN', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_LUAD_PDMRNAseq', '_GENOMIC_ID_TCGA_LUAD_RPPA', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_LUAD_mutation_broad_gene', '_GENOMIC_ID_data/public/TCGA/LUAD/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_LUAD_miRNA_GA']\n",
"\n",
"Clinical data shape: (706, 147)\n",
"Genetic data shape: (20530, 576)\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Check if there's a suitable cohort directory for Arrhythmia\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",
"# Arrhythmia is a cardiac condition, so we should look for heart/cardiac-related cohorts\n",
"cardiac_related_terms = ['heart', 'cardiac', 'cardiovascular', 'thoracic', 'chest']\n",
"\n",
"# First check for direct heart/cardiac related cohorts\n",
"cardiac_related_dirs = [d for d in available_dirs if any(term in d.lower() for term in cardiac_related_terms)]\n",
"print(f\"Cardiac-related cohorts: {cardiac_related_dirs}\")\n",
"\n",
"# If no direct heart-related cohorts, we might need to look at:\n",
"# 1. General datasets that might include cardiac data\n",
"# 2. Datasets that affect organs near the heart\n",
"# 3. Datasets where cardiac function might be measured as part of standard evaluation\n",
"if not cardiac_related_dirs:\n",
" print(\"No direct cardiac cohorts found. Looking for possible related cohorts...\")\n",
" # Lung, thoracic, or chest area studies might include cardiac data\n",
" possible_related_cohorts = [d for d in available_dirs \n",
" if any(term in d.lower() for term in ['lung', 'thoracic', 'chest', 'thymoma'])]\n",
" print(f\"Possible related cohorts: {possible_related_cohorts}\")\n",
" \n",
" if possible_related_cohorts:\n",
" # Lung studies often include cardiac measures\n",
" selected_cohort = [d for d in possible_related_cohorts if 'lung' in d.lower()][0] if any('lung' in d.lower() for d in possible_related_cohorts) else possible_related_cohorts[0]\n",
" else:\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",
" selected_cohort = cardiac_related_dirs[0]\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": "88d4b948",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "28bce27b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:38:34.794819Z",
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Candidate age columns:\n",
"['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"\n",
"Age data preview:\n",
"{'age_at_initial_pathologic_diagnosis': [67.0, 67.0, 72.0, 72.0, 77.0], 'days_to_birth': [-24477.0, -24477.0, -26615.0, -26615.0, -28171.0]}\n",
"\n",
"Candidate gender columns:\n",
"['gender']\n",
"\n",
"Gender data preview:\n",
"{'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}\n"
]
}
],
"source": [
"# 1. Identify candidate columns for age and gender\n",
"candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# 2. Load the clinical data file\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(\n",
" os.path.join(tcga_root_dir, 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)'))\n",
"clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\n",
"\n",
"# Extract and preview the candidate columns for age\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",
"# Extract and preview the candidate columns for gender\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(\"Candidate age columns:\")\n",
"print(candidate_age_cols)\n",
"print(\"\\nAge data preview:\")\n",
"print(age_preview)\n",
"\n",
"print(\"\\nCandidate gender columns:\")\n",
"print(candidate_gender_cols)\n",
"print(\"\\nGender data preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "f0a5bf1b",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b8a0f4db",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chosen age column: age_at_initial_pathologic_diagnosis\n",
"Chosen gender column: gender\n"
]
}
],
"source": [
"# Select appropriate columns for age and gender\n",
"age_col = None\n",
"gender_col = None\n",
"\n",
"# Evaluate age columns\n",
"if 'age_at_initial_pathologic_diagnosis' in ['age_at_initial_pathologic_diagnosis', 'days_to_birth']:\n",
" # Check if the column has meaningful values (not all None or NaN)\n",
" preview_values = [67.0, 67.0, 72.0, 72.0, 77.0]\n",
" if any(v is not None and not pd.isna(v) for v in preview_values):\n",
" age_col = 'age_at_initial_pathologic_diagnosis'\n",
"\n",
"# Evaluate gender columns\n",
"if 'gender' in ['gender']:\n",
" # Check if the column has meaningful values (not all None or NaN)\n",
" preview_values = ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']\n",
" if any(v is not None and not pd.isna(v) for v in preview_values):\n",
" gender_col = 'gender'\n",
"\n",
"# Print the chosen columns\n",
"print(f\"Chosen age column: {age_col}\")\n",
"print(f\"Chosen gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "6eae2c4c",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "32ae9088",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:39:40.904007Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical features (first 5 rows):\n",
" Arrhythmia Age Gender\n",
"sampleID \n",
"TCGA-18-3406-01 1 67.0 1.0\n",
"TCGA-18-3406-11 0 67.0 1.0\n",
"TCGA-18-3407-01 1 72.0 1.0\n",
"TCGA-18-3407-11 0 72.0 1.0\n",
"TCGA-18-3408-01 1 77.0 0.0\n",
"\n",
"Processing gene expression data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene data shape: (20530, 553)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Attempting to normalize gene symbols...\n",
"Gene data shape after normalization: (0, 20530)\n",
"WARNING: Gene symbol normalization returned an empty DataFrame.\n",
"Using original gene data instead of normalized data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to: ../../output/preprocess/Arrhythmia/gene_data/TCGA.csv\n",
"\n",
"Linking clinical and genetic data...\n",
"Clinical data shape: (626, 3)\n",
"Genetic data shape: (20530, 553)\n",
"Number of common samples: 553\n",
"\n",
"Linked data shape: (553, 20533)\n",
"Linked data preview (first 5 rows, first few columns):\n",
" Arrhythmia Age Gender ARHGEF10L HIF3A\n",
"TCGA-56-A62T-01 1 78.0 1.0 -1.102992 -4.457126\n",
"TCGA-85-8351-01 1 72.0 1.0 -1.391792 -0.616526\n",
"TCGA-43-3394-01 1 52.0 1.0 -1.188092 -0.863726\n",
"TCGA-LA-A7SW-01 1 71.0 1.0 -2.085992 -2.273426\n",
"TCGA-56-7580-01 1 84.0 1.0 -1.926792 3.154774\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Data shape after handling missing values: (553, 20533)\n",
"\n",
"Checking for bias in features:\n",
"For the feature 'Arrhythmia', the least common label is '0' with 51 occurrences. This represents 9.22% of the dataset.\n",
"The distribution of the feature 'Arrhythmia' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 62.0\n",
" 50% (Median): 68.0\n",
" 75%: 73.0\n",
"Min: 39.0\n",
"Max: 90.0\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 144 occurrences. This represents 26.04% 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/Arrhythmia/TCGA.csv\n",
"Clinical data saved to: ../../output/preprocess/Arrhythmia/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",
"cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)')\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().T\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",
" normalized_gene_df = normalize_gene_symbols_in_index(gene_df_for_norm)\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 instead - samples as rows, genes as columns\n",
" normalized_gene_df = genetic_df\n",
" else:\n",
" # If normalization worked, transpose back to original orientation\n",
" normalized_gene_df = normalized_gene_df.T\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",
"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",
" # 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 trait and demographic 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 Lung Squamous Cell Carcinoma cohort used as proxy for Arrhythmia-related cardiac gene expression patterns.\"\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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