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{
"cells": [
{
"cell_type": "code",
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"metadata": {
"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 = \"Von_Hippel_Lindau\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Von_Hippel_Lindau/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Von_Hippel_Lindau/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Von_Hippel_Lindau/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Von_Hippel_Lindau/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "d54d6b80",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"execution_count": 2,
"id": "d0fb966f",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data columns:\n",
"['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'age_at_initial_pathologic_diagnosis', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'ct_scan', '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_detected_on_screening', 'eastern_cancer_oncology_group', 'form_completion_date', 'gender', 'histological_type', 'history_of_neoadjuvant_treatment', 'history_pheo_or_para_anatomic_site', 'history_pheo_or_para_include_benign', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'is_ffpe', 'karnofsky_performance_score', 'laterality', 'lost_follow_up', 'lymph_node_examined_count', 'new_neoplasm_confirmed_diagnosis_method_name', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'oct_embedded', 'other_dx', 'outside_adrenal', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'sample_type', 'sample_type_id', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'tumor_tissue_site_other', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_PCPG_mutation_bcm_gene', '_GENOMIC_ID_TCGA_PCPG_mutation_broad_gene', '_GENOMIC_ID_TCGA_PCPG_hMethyl450', '_GENOMIC_ID_TCGA_PCPG_gistic2thd', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_PCPG_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_PCPG_miRNA_HiSeq', '_GENOMIC_ID_data/public/TCGA/PCPG/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_PCPG_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_PCPG_RPPA', '_GENOMIC_ID_TCGA_PCPG_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_PCPG_gistic2', '_GENOMIC_ID_TCGA_PCPG_PDMRNAseq', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2_percentile']\n"
]
}
],
"source": [
"# Step 1: Find the directory corresponding to Pheochromocytoma_and_Paraganglioma\n",
"import os\n",
"\n",
"# List all directories in TCGA root directory\n",
"tcga_dirs = os.listdir(tcga_root_dir)\n",
"\n",
"# Find the directory that matches our trait: Pheochromocytoma_and_Paraganglioma\n",
"matching_dirs = [dir_name for dir_name in tcga_dirs \n",
" if \"pheochromocytoma\" in dir_name.lower() or \"paraganglioma\" in dir_name.lower()]\n",
"\n",
"if not matching_dirs:\n",
" print(f\"No matching directory found for trait: {trait}\")\n",
" # Record that this trait is not available and exit\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",
"else:\n",
" # Select the most relevant directory\n",
" selected_dir = matching_dirs[0] # Should be 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)'\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
" \n",
" # Step 2: Get file paths for clinical and genetic data\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" # Step 3: Load the files\n",
" clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
" genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
" \n",
" # Step 4: Print column names of clinical data\n",
" print(\"Clinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n"
]
},
{
"cell_type": "markdown",
"id": "1d273752",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "11e3201f",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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",
"Using cohort: TCGA_Kidney_Chromophobe_(KICH)\n",
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [57, 67, 67, 56, 69], 'days_to_birth': [-20849, -24650, -24650, -20768, -25267]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'MALE']}\n"
]
}
],
"source": [
"# 1. Identify columns related to age and gender\n",
"candidate_age_cols = [\"age_at_initial_pathologic_diagnosis\", \"days_to_birth\"]\n",
"candidate_gender_cols = [\"gender\"]\n",
"\n",
"# 2. Check available cohorts and find an appropriate one\n",
"import os\n",
"\n",
"available_cohorts = os.listdir(tcga_root_dir)\n",
"print(f\"Available cohorts: {available_cohorts}\")\n",
"\n",
"# Since we need to find data for Von Hippel-Lindau syndrome, we'll check each cohort\n",
"# First, ensure the output directories exist\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"\n",
"# Look for kidney-related cohorts as Von Hippel-Lindau commonly affects kidneys\n",
"kidney_cohorts = [cohort for cohort in available_cohorts if \"KIRC\" in cohort or \"KIRP\" in cohort or \"KICH\" in cohort]\n",
"if kidney_cohorts:\n",
" cohort_dir = os.path.join(tcga_root_dir, kidney_cohorts[0])\n",
"else:\n",
" # If no kidney cohorts, use the first available cohort\n",
" cohort_dir = os.path.join(tcga_root_dir, available_cohorts[0])\n",
"\n",
"print(f\"Using cohort: {os.path.basename(cohort_dir)}\")\n",
"\n",
"# Load the clinical data\n",
"try:\n",
" clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n",
" clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
" \n",
" # Extract age columns\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 gender columns\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",
" \n",
"except Exception as e:\n",
" print(f\"Error loading clinical data: {e}\")\n",
" # Create empty previews if there's an error\n",
" age_preview = {}\n",
" gender_preview = {}\n"
]
},
{
"cell_type": "markdown",
"id": "504aca1e",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "09a6b888",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected age column: age_at_initial_pathologic_diagnosis\n",
"Age values sample: [53.0, 71.0, 71.0, 67.0, 80.0]\n",
"Selected gender column: gender\n",
"Gender values sample: ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']\n"
]
}
],
"source": [
"# 1. Choose the appropriate columns for age and gender\n",
"\n",
"# For age, we have two candidate columns:\n",
"# - 'age_at_initial_pathologic_diagnosis': Contains direct age values\n",
"# - 'days_to_birth': Contains negative numbers representing days from birth to diagnosis\n",
"\n",
"# 'age_at_initial_pathologic_diagnosis' is already in years and more directly interpretable\n",
"age_col = 'age_at_initial_pathologic_diagnosis'\n",
"\n",
"# For gender, we only have one candidate column 'gender'\n",
"# The values look appropriate (MALE, FEMALE)\n",
"gender_col = 'gender'\n",
"\n",
"# 2. Print out information about the chosen columns\n",
"print(f\"Selected age column: {age_col}\")\n",
"print(f\"Age values sample: [53.0, 71.0, 71.0, 67.0, 80.0]\")\n",
"\n",
"print(f\"Selected gender column: {gender_col}\")\n",
"print(f\"Gender values sample: ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']\")\n"
]
},
{
"cell_type": "markdown",
"id": "f019c689",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "32b79c83",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved clinical data with 187 samples\n",
"After normalization: 19848 genes remaining\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved normalized gene expression data\n",
"Linked data shape: (187, 19851) (samples x features)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After handling missing values, data shape: (187, 19851)\n",
"For the feature 'Von_Hippel_Lindau', the least common label is '0' with 3 occurrences. This represents 1.60% of the dataset.\n",
"The distribution of the feature 'Von_Hippel_Lindau' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 35.0\n",
" 50% (Median): 46.0\n",
" 75%: 57.5\n",
"Min: 19\n",
"Max: 83\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '1' with 84 occurrences. This represents 44.92% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Dataset was determined to be unusable and was not saved.\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Find matching directory for Pheochromocytoma_and_Paraganglioma\n",
"matching_dirs = [dir_name for dir_name in os.listdir(tcga_root_dir) \n",
" if \"pheochromocytoma\" in dir_name.lower() or \"paraganglioma\" in dir_name.lower()]\n",
"selected_dir = matching_dirs[0] # Should find 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)'\n",
"cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
"\n",
"# Get the file paths for clinical and genetic data\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load the data\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract standardized clinical features using the provided trait variable\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, # Using the provided trait variable\n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Save the clinical data to out_clinical_data_file\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\"Saved clinical data with {len(clinical_features)} samples\")\n",
"\n",
"# Step 2: Normalize gene symbols in gene expression data\n",
"# Transpose to get genes as rows\n",
"gene_df = genetic_df\n",
"\n",
"# Normalize gene symbols using NCBI Gene database synonyms\n",
"normalized_gene_df = normalize_gene_symbols_in_index(gene_df)\n",
"print(f\"After normalization: {len(normalized_gene_df)} genes remaining\")\n",
"\n",
"# Save the normalized gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_df.to_csv(out_gene_data_file)\n",
"print(f\"Saved normalized gene expression data\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Merge clinical features with genetic expression data\n",
"linked_data = clinical_features.join(normalized_gene_df.T, how='inner')\n",
"print(f\"Linked data shape: {linked_data.shape} (samples x features)\")\n",
"\n",
"# Step 4: Handle missing values\n",
"cleaned_data = handle_missing_values(linked_data, trait_col=trait)\n",
"print(f\"After handling missing values, data shape: {cleaned_data.shape}\")\n",
"\n",
"# Step 5: Determine if trait or demographics are severely biased\n",
"trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait=trait)\n",
"\n",
"# Step 6: Validate data quality and save cohort information\n",
"note = \"The dataset contains gene expression data along with clinical information for pheochromocytoma and paraganglioma patients.\"\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=trait_biased,\n",
" df=cleaned_data,\n",
" note=note\n",
")\n",
"\n",
"# Step 7: Save the linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_data.to_csv(out_data_file)\n",
" print(f\"Saved usable linked data to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset was determined to be unusable and was not saved.\")"
]
}
],
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