{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "2a4e4239", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:55:11.098262Z", "iopub.status.busy": "2025-03-25T04:55:11.098087Z", "iopub.status.idle": "2025-03-25T04:55:11.267024Z", "shell.execute_reply": "2025-03-25T04:55:11.266567Z" } }, "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 = \"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" ] }, { "cell_type": "code", "execution_count": 2, "id": "d0fb966f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:55:11.268283Z", "iopub.status.busy": "2025-03-25T04:55:11.268131Z", "iopub.status.idle": "2025-03-25T04:55:11.775564Z", "shell.execute_reply": "2025-03-25T04:55:11.774994Z" } }, "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", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:55:11.777179Z", "iopub.status.busy": "2025-03-25T04:55:11.777048Z", "iopub.status.idle": "2025-03-25T04:55:11.795167Z", "shell.execute_reply": "2025-03-25T04:55:11.794740Z" } }, "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": { "iopub.execute_input": "2025-03-25T04:55:11.796658Z", "iopub.status.busy": "2025-03-25T04:55:11.796542Z", "iopub.status.idle": "2025-03-25T04:55:11.799276Z", "shell.execute_reply": "2025-03-25T04:55:11.798881Z" } }, "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": { "iopub.execute_input": "2025-03-25T04:55:11.800684Z", "iopub.status.busy": "2025-03-25T04:55:11.800573Z", "iopub.status.idle": "2025-03-25T04:55:21.232499Z", "shell.execute_reply": "2025-03-25T04:55:21.231849Z" } }, "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.\")" ] } ], "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 }