{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7967b317", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:47.626723Z", "iopub.status.busy": "2025-03-25T04:08:47.626403Z", "iopub.status.idle": "2025-03-25T04:08:47.819495Z", "shell.execute_reply": "2025-03-25T04:08:47.819026Z" } }, "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 = \"Testicular_Cancer\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Testicular_Cancer/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Testicular_Cancer/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Testicular_Cancer/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Testicular_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "1f526fe5", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "be082dd1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:47.821038Z", "iopub.status.busy": "2025-03-25T04:08:47.820868Z", "iopub.status.idle": "2025-03-25T04:08:48.254690Z", "shell.execute_reply": "2025-03-25T04:08:48.254029Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available TCGA directories: ['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", "Found relevant directory for Testicular_Cancer: TCGA_Testicular_Cancer_(TGCT)\n", "Clinical data file: ../../input/TCGA/TCGA_Testicular_Cancer_(TGCT)/TCGA.TGCT.sampleMap_TGCT_clinicalMatrix\n", "Genetic data file: ../../input/TCGA/TCGA_Testicular_Cancer_(TGCT)/TCGA.TGCT.sampleMap_HiSeqV2_PANCAN.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "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', 'bilateral_diagnosis_timing_type', 'clinical_M', 'clinical_N', 'clinical_T', 'clinical_stage', 'days_to_bilateral_tumor_dx', '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', 'days_to_post_orchi_serum_test', 'days_to_pre_orchi_serum_test', 'eastern_cancer_oncology_group', 'family_history_other_cancer', 'family_history_testicular_cancer', 'family_member_relationship_type', 'first_treatment_success', 'form_completion_date', 'gender', 'histological_percentage', 'histological_type', 'history_fertility', 'history_hypospadias', 'history_of_neoadjuvant_treatment', 'history_of_undescended_testis', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'igcccg_stage', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_pathologic_diagnosis_method', 'initial_weight', 'intratubular_germ_cell_neoplasm', 'is_ffpe', 'karnofsky_performance_score', 'laterality', 'level_of_non_descent', 'lost_follow_up', 'lymphovascular_invasion_present', 'molecular_test_result', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_death_reason', 'patient_id', 'person_neoplasm_cancer_status', 'post_orchi_afp', 'post_orchi_hcg', 'post_orchi_ldh', 'post_orchi_lh', 'post_orchi_lymph_node_dissection', 'post_orchi_testosterone', 'postoperative_rx_tx', 'postoperative_tx', 'pre_orchi_afp', 'pre_orchi_hcg', 'pre_orchi_ldh', 'pre_orchi_lh', 'pre_orchi_testosterone', 'primary_therapy_outcome_success', 'radiation_therapy', 'relation_testicular_cancer', 'relative_family_cancer_hx_text', 'sample_type', 'sample_type_id', 'serum_markers', 'source_of_patient_death_reason', 'synchronous_tumor_histology_pct', 'synchronous_tumor_histology_type', 'system_version', 'testis_tumor_macroextent', 'testis_tumor_macroextent_other', 'testis_tumor_microextent', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'undescended_testis_corrected', 'undescended_testis_corrected_age', 'undescended_testis_method_left', 'undescended_testis_method_right', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_TGCT_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_TGCT_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_TGCT_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_TGCT_exp_HiSeqV2', '_GENOMIC_ID_TCGA_TGCT_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_TGCT_hMethyl450', '_GENOMIC_ID_TCGA_TGCT_gistic2', '_GENOMIC_ID_data/public/TCGA/TGCT/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_TGCT_gistic2thd', '_GENOMIC_ID_TCGA_TGCT_mutation_bcm_gene', '_GENOMIC_ID_TCGA_TGCT_miRNA_HiSeq', '_GENOMIC_ID_TCGA_TGCT_mutation_broad_gene', '_GENOMIC_ID_TCGA_TGCT_PDMRNAseq', '_GENOMIC_ID_TCGA_TGCT_RPPA', '_GENOMIC_ID_TCGA_TGCT_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_TGCT_mutation_bcgsc_gene']\n" ] } ], "source": [ "# Step 1: Review subdirectories to find one related to Testicular Cancer\n", "import os\n", "\n", "# List all directories in TCGA root directory\n", "tcga_dirs = os.listdir(tcga_root_dir)\n", "print(f\"Available TCGA directories: {tcga_dirs}\")\n", "\n", "# Find the directory related to Testicular Cancer\n", "testicular_cancer_dir = None\n", "for dir_name in tcga_dirs:\n", " if \"testicular\" in dir_name.lower():\n", " testicular_cancer_dir = dir_name\n", " break\n", "\n", "if testicular_cancer_dir:\n", " print(f\"Found relevant directory for {trait}: {testicular_cancer_dir}\")\n", " \n", " # Get the full path to the directory\n", " cohort_dir = os.path.join(tcga_root_dir, testicular_cancer_dir)\n", " \n", " # Step 2: Find clinical and genetic data files\n", " clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", " \n", " print(f\"Clinical data file: {clinical_file_path}\")\n", " print(f\"Genetic data file: {genetic_file_path}\")\n", " \n", " # Step 3: Load the data files\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", " # Step 4: Print column names of clinical data\n", " print(\"\\nClinical data columns:\")\n", " print(clinical_df.columns.tolist())\n", " \n", " # Check if both datasets are available\n", " is_gene_available = not genetic_df.empty\n", " is_trait_available = not clinical_df.empty\n", " \n", " # Initial validation\n", " validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", " )\n", "else:\n", " print(f\"No directory specifically matches the trait: {trait}\")\n", " \n", " # Since the trait is not directly represented, we should record this fact\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", " print(f\"Task marked as completed. {trait} is not directly represented in the TCGA dataset.\")\n" ] }, { "cell_type": "markdown", "id": "ea61cc0d", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "c135cb25", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:48.256171Z", "iopub.status.busy": "2025-03-25T04:08:48.256023Z", "iopub.status.idle": "2025-03-25T04:08:48.264880Z", "shell.execute_reply": "2025-03-25T04:08:48.264408Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age columns preview:\n", "{'age_at_initial_pathologic_diagnosis': [31.0, 38.0, 28.0, 30.0, 28.0], 'days_to_birth': [-11325.0, -13964.0, -10511.0, -10983.0, -10281.0]}\n", "Gender columns preview:\n", "{'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'MALE']}\n" ] } ], "source": [ "# Define 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", "# Read the clinical data\n", "clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Testicular_Cancer_(TGCT)'))\n", "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n", "\n", "# Preview age columns\n", "if candidate_age_cols:\n", " age_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_age_cols}\n", " print(\"Age columns preview:\")\n", " print(age_preview)\n", "\n", "# Preview gender columns\n", "if candidate_gender_cols:\n", " gender_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_gender_cols}\n", " print(\"Gender columns preview:\")\n", " print(gender_preview)\n" ] }, { "cell_type": "markdown", "id": "b5c316d7", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "c8cc2c79", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:48.266069Z", "iopub.status.busy": "2025-03-25T04:08:48.265945Z", "iopub.status.idle": "2025-03-25T04:08:48.269434Z", "shell.execute_reply": "2025-03-25T04:08:48.268962Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected age column: age_at_initial_pathologic_diagnosis\n", "Age values preview: [31.0, 38.0, 28.0, 30.0, 28.0]\n", "Selected gender column: gender\n", "Gender values preview: ['MALE', 'MALE', 'MALE', 'MALE', 'MALE']\n" ] } ], "source": [ "# Step: Select Demographic Features\n", "\n", "# Evaluate age columns\n", "age_columns = {'age_at_initial_pathologic_diagnosis': [31.0, 38.0, 28.0, 30.0, 28.0], 'days_to_birth': [-11325.0, -13964.0, -10511.0, -10983.0, -10281.0]}\n", "\n", "# Evaluate gender columns\n", "gender_columns = {'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'MALE']}\n", "\n", "# Select age column - prefer age_at_initial_pathologic_diagnosis since it's in years which is more standard\n", "age_col = 'age_at_initial_pathologic_diagnosis' if age_columns else None\n", "\n", "# Select gender column - only one option\n", "gender_col = 'gender' if gender_columns else None\n", "\n", "# Print chosen columns\n", "print(f\"Selected age column: {age_col}\")\n", "print(f\"Age values preview: {age_columns.get(age_col, [])}\")\n", "print(f\"Selected gender column: {gender_col}\")\n", "print(f\"Gender values preview: {gender_columns.get(gender_col, [])}\")\n" ] }, { "cell_type": "markdown", "id": "da111c06", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "be9ee855", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:48.270588Z", "iopub.status.busy": "2025-03-25T04:08:48.270477Z", "iopub.status.idle": "2025-03-25T04:08:57.550845Z", "shell.execute_reply": "2025-03-25T04:08:57.550208Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved clinical data with 156 samples\n", "After normalization: 19848 genes remaining\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved normalized gene expression data\n", "Linked data shape: (156, 19851) (samples x features)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After handling missing values, data shape: (156, 19851)\n", "Quartiles for 'Testicular_Cancer':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1\n", "Max: 1\n", "The distribution of the feature 'Testicular_Cancer' in this dataset is severely biased.\n", "\n", "Quartiles for 'Age':\n", " 25%: 26.0\n", " 50% (Median): 31.8705035971223\n", " 75%: 36.0\n", "Min: 14.0\n", "Max: 67.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 156 occurrences. This represents 100.00% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is severely biased.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset was determined to be unusable and was not saved.\n" ] } ], "source": [ "# Step 1: Extract and standardize clinical features\n", "# Use the Testicular Cancer directory identified in Step 1\n", "selected_dir = \"TCGA_Testicular_Cancer_(TGCT)\"\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 testicular cancer patients from TCGA.\"\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 }