{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e9f6bd2a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:53.239986Z", "iopub.status.busy": "2025-03-25T06:22:53.239805Z", "iopub.status.idle": "2025-03-25T06:22:53.403128Z", "shell.execute_reply": "2025-03-25T06:22:53.402780Z" } }, "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 = \"Adrenocortical_Cancer\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "388af33e", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "15623685", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:53.404546Z", "iopub.status.busy": "2025-03-25T06:22:53.404405Z", "iopub.status.idle": "2025-03-25T06:22:53.615064Z", "shell.execute_reply": "2025-03-25T06:22:53.614686Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available TCGA subdirectories: ['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", "Selected directory: TCGA_Adrenocortical_Cancer_(ACC)\n", "Clinical data file: ../../input/TCGA/TCGA_Adrenocortical_Cancer_(ACC)/TCGA.ACC.sampleMap_ACC_clinicalMatrix\n", "Genetic data file: ../../input/TCGA/TCGA_Adrenocortical_Cancer_(ACC)/TCGA.ACC.sampleMap_HiSeqV2_PANCAN.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Clinical data columns:\n", "['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'atypical_mitotic_figures', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'clinical_M', 'ct_scan_findings', 'cytoplasm_presence_less_than_equal_25_percent', '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', 'diffuse_architecture', 'distant_metastasis_anatomic_site', 'excess_adrenal_hormone_diagnosis_method_type', 'excess_adrenal_hormone_history_type', 'form_completion_date', 'gender', 'germline_testing_performed', 'histologic_disease_progression_present_indicator', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'invasion_of_tumor_capsule', 'is_ffpe', 'laterality', 'lost_follow_up', 'lymph_node_examined_count', 'metastatic_neoplasm_confirmed_diagnosis_method_name', 'metastatic_neoplasm_confirmed_diagnosis_method_text', 'mitoses_count', 'mitotane_therapy', 'mitotane_therapy_adjuvant_setting', 'mitotane_therapy_for_macroscopic_residual_disease', 'mitotic_rate', 'necrosis', '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_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'nuclear_grade_III_IV', 'number_of_lymphnodes_positive_by_he', 'oct_embedded', 'other_dx', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'post_surgical_procedure_assessment_thyroid_gland_carcinoma_stats', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'residual_tumor', 'ret', 'sample_type', 'sample_type_id', 'sinusoid_invasion', 'therapeutic_mitotane_levels_achieved', 'therapeutic_mitotane_lvl_macroscopic_residual', 'therapeutic_mitotane_lvl_progression', 'therapeutic_mitotane_lvl_recurrence', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weiss_score', 'weiss_venous_invasion', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_ACC_mutation_curated_bcm_gene', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2_percentile', '_GENOMIC_ID_data/public/TCGA/ACC/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_ACC_miRNA_HiSeq', '_GENOMIC_ID_TCGA_ACC_RPPA', '_GENOMIC_ID_TCGA_ACC_hMethyl450', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2', '_GENOMIC_ID_TCGA_ACC_gistic2thd', '_GENOMIC_ID_TCGA_ACC_PDMRNAseq', '_GENOMIC_ID_TCGA_ACC_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_ACC_gistic2', '_GENOMIC_ID_TCGA_ACC_mutation_bcm_gene', '_GENOMIC_ID_TCGA_ACC_mutation_curated_broad_gene']\n", "\n", "Clinical data shape: (92, 104)\n", "Genetic data shape: (20530, 79)\n" ] } ], "source": [ "import os\n", "\n", "# Step 1: Look for directories related to Adrenocortical Cancer\n", "tcga_subdirs = os.listdir(tcga_root_dir)\n", "print(f\"Available TCGA subdirectories: {tcga_subdirs}\")\n", "\n", "# Look for directory related to Adrenocortical Cancer\n", "target_dir = None\n", "for subdir in tcga_subdirs:\n", " # Look for exact match or synonymous terms\n", " if trait.lower() in subdir.lower() or \"ACC\" in subdir:\n", " target_dir = subdir\n", " break\n", "\n", "if target_dir is None:\n", " print(f\"No suitable directory found for {trait}.\")\n", " # Mark the task as completed by creating a JSON record indicating data is not available\n", " validate_and_save_cohort_info(is_final=False, cohort=\"TCGA\", info_path=json_path, \n", " is_gene_available=False, is_trait_available=False)\n", " exit() # Exit the program\n", "\n", "# Step 2: Get file paths for the selected directory\n", "cohort_dir = os.path.join(tcga_root_dir, target_dir)\n", "clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", "\n", "print(f\"Selected directory: {target_dir}\")\n", "print(f\"Clinical data file: {clinical_file_path}\")\n", "print(f\"Genetic data file: {genetic_file_path}\")\n", "\n", "# Step 3: Load 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", "# Step 4: Print column names of clinical data\n", "print(\"\\nClinical data columns:\")\n", "print(clinical_df.columns.tolist())\n", "\n", "# Additional basic information\n", "print(f\"\\nClinical data shape: {clinical_df.shape}\")\n", "print(f\"Genetic data shape: {genetic_df.shape}\")\n" ] }, { "cell_type": "markdown", "id": "75c1e26b", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "e108da5e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:53.616239Z", "iopub.status.busy": "2025-03-25T06:22:53.616122Z", "iopub.status.idle": "2025-03-25T06:22:53.622919Z", "shell.execute_reply": "2025-03-25T06:22:53.622626Z" } }, "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': [58, 44, 23, 23, 30], 'days_to_birth': [-21496, -16090, -8624, -8451, -11171]}\n", "\n", "Candidate gender columns:\n", "['gender']\n", "\n", "Gender data preview:\n", "{'gender': ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'MALE']}\n" ] } ], "source": [ "# 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", "# Get the clinical data file path\n", "cohort_dir = os.path.join(tcga_root_dir, \"TCGA_Adrenocortical_Cancer_(ACC)\")\n", "clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n", "\n", "# Load clinical data\n", "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n", "\n", "# Extract and preview 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 and preview 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(\"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": "0b65dc43", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "5d1a5947", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:53.623928Z", "iopub.status.busy": "2025-03-25T06:22:53.623821Z", "iopub.status.idle": "2025-03-25T06:22:53.626166Z", "shell.execute_reply": "2025-03-25T06:22:53.625887Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected age column: age_at_initial_pathologic_diagnosis\n", "Selected gender column: gender\n" ] } ], "source": [ "# Selecting appropriate age column\n", "age_col = None\n", "if 'age_at_initial_pathologic_diagnosis' in ['age_at_initial_pathologic_diagnosis', 'days_to_birth']:\n", " # Choosing age_at_initial_pathologic_diagnosis as it directly provides the age in years\n", " age_col = 'age_at_initial_pathologic_diagnosis'\n", "\n", "# Selecting appropriate gender column\n", "gender_col = None\n", "if 'gender' in ['gender']:\n", " # The 'gender' column seems to have appropriate values (MALE/FEMALE)\n", " gender_col = 'gender'\n", "\n", "# Printing the chosen columns\n", "print(f\"Selected age column: {age_col}\")\n", "print(f\"Selected gender column: {gender_col}\")\n" ] }, { "cell_type": "markdown", "id": "ca831a02", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "bd0635b4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:53.627137Z", "iopub.status.busy": "2025-03-25T06:22:53.627037Z", "iopub.status.idle": "2025-03-25T06:23:00.586244Z", "shell.execute_reply": "2025-03-25T06:23:00.585911Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data saved to ../../output/preprocess/Adrenocortical_Cancer/clinical_data/TCGA.csv\n", "Clinical data shape: (92, 3)\n", " Adrenocortical_Cancer Age Gender\n", "sampleID \n", "TCGA-OR-A5J1-01 1 58 1\n", "TCGA-OR-A5J2-01 1 44 0\n", "TCGA-OR-A5J3-01 1 23 0\n", "TCGA-OR-A5J4-01 1 23 0\n", "TCGA-OR-A5J5-01 1 30 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Adrenocortical_Cancer/gene_data/TCGA.csv\n", "Normalized gene data shape: (19848, 79)\n", "Linked data shape: (79, 19851)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After handling missing values - linked data shape: (79, 19851)\n", "Quartiles for 'Adrenocortical_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 'Adrenocortical_Cancer' in this dataset is severely biased.\n", "\n", "Quartiles for 'Age':\n", " 25%: 35.0\n", " 50% (Median): 49.0\n", " 75%: 59.5\n", "Min: 14\n", "Max: 77\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 31 occurrences. This represents 39.24% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "After removing biased features - linked data shape: (79, 19851)\n", "Linked data not saved due to quality concerns\n" ] } ], "source": [ "# Step 1: Extract and standardize the clinical features\n", "# Get file paths\n", "cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Adrenocortical_Cancer_(ACC)')\n", "clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", "\n", "# Load 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", "# Create standardized clinical features dataframe with trait, age, and gender\n", "# The trait for Adrenocortical Cancer is based on tumor/normal classification\n", "clinical_features = tcga_select_clinical_features(\n", " clinical_df, \n", " trait=trait, # Using predefined trait variable\n", " age_col=age_col, \n", " gender_col=gender_col\n", ")\n", "\n", "# Save clinical data\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\"Clinical data saved to {out_clinical_data_file}\")\n", "print(f\"Clinical data shape: {clinical_features.shape}\")\n", "print(clinical_features.head())\n", "\n", "# Step 2: Normalize gene symbols in gene expression data\n", "# Transpose the genetic data to have genes as rows\n", "genetic_data = genetic_df.copy()\n", "\n", "# Normalize gene symbols using the NCBI Gene database synonyms\n", "normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)\n", "\n", "# Save normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "\n", "# Step 3: Link clinical and genetic data\n", "# Transpose genetic data to get samples as rows, genes as columns\n", "genetic_data_transposed = normalized_gene_data.T\n", "\n", "# Ensure clinical and genetic data have the same samples (index values)\n", "common_samples = clinical_features.index.intersection(genetic_data_transposed.index)\n", "clinical_subset = clinical_features.loc[common_samples]\n", "genetic_subset = genetic_data_transposed.loc[common_samples]\n", "\n", "# Combine clinical and genetic data\n", "linked_data = pd.concat([clinical_subset, genetic_subset], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# Step 4: Handle missing values\n", "linked_data = handle_missing_values(linked_data, trait_col=trait)\n", "print(f\"After handling missing values - linked data shape: {linked_data.shape}\")\n", "\n", "# Step 5: Determine biased features\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)\n", "print(f\"After removing biased features - linked data shape: {linked_data.shape}\")\n", "\n", "# Step 6: Validate data quality and save cohort info\n", "# First check if we have both gene and trait data\n", "is_gene_available = linked_data.shape[1] > 3 # More than just trait, Age, Gender\n", "is_trait_available = trait in linked_data.columns\n", "\n", "# Take notes of special findings\n", "notes = f\"TCGA Adrenocortical Cancer dataset processed. Used tumor/normal classification as the trait.\"\n", "\n", "# Validate the data quality\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=notes\n", ")\n", "\n", "# Step 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", "else:\n", " print(\"Linked data not saved due to quality concerns\")" ] } ], "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 }