{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "95eebd67", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:54:14.839405Z", "iopub.status.busy": "2025-03-25T06:54:14.839184Z", "iopub.status.idle": "2025-03-25T06:54:15.002805Z", "shell.execute_reply": "2025-03-25T06:54:15.002474Z" } }, "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 = \"Autoinflammatory_Disorders\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Autoinflammatory_Disorders/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "56dd88d4", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "0525c11d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:54:15.004261Z", "iopub.status.busy": "2025-03-25T06:54:15.004126Z", "iopub.status.idle": "2025-03-25T06:54:15.161538Z", "shell.execute_reply": "2025-03-25T06:54:15.161158Z" } }, "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", "Potential matches for Autoinflammatory_Disorders: ['TCGA_Large_Bcell_Lymphoma_(DLBC)']\n", "\n", "Selected directory: TCGA_Large_Bcell_Lymphoma_(DLBC)\n", "Clinical data file: TCGA.DLBC.sampleMap_DLBC_clinicalMatrix\n", "Genetic data file: TCGA.DLBC.sampleMap_HiSeqV2_PANCAN.gz\n", "\n", "Clinical data columns:\n", "['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'age_at_initial_pathologic_diagnosis', 'b_lymphocyte_genotyping_method', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'bone_marrow_biopsy_done', 'bone_marrow_involvement', 'bone_marrow_sample_histology', 'clinical_stage', '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', 'eastern_cancer_oncology_group', 'ebv_positive_malignant_cells_percent', 'ebv_status_malignant_cells_method', 'epstein_barr_viral_status', 'extranodal_involvement', 'extranodal_involvment_site_other', 'extranodal_sites_involvement_number', 'first_progression_histology_type', 'first_progression_histology_type_other', 'first_recurrence_biopsy_confirmed', 'follicular_percent', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'genetic_abnormality_method_other', 'genetic_abnormality_results', 'genetic_abnormality_results_other', 'genetic_abnormality_tested', 'genetic_abnormality_tested_other', 'height', 'histological_type', 'history_immunological_disease', 'history_immunological_disease_other', 'history_immunosuppresive_rx', 'history_immunosuppressive_rx_other', 'history_of_neoadjuvant_treatment', 'history_relevant_infectious_dx', 'hiv_status', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'igh_genotype_results', 'immunophenotypic_analysis_method', 'immunophenotypic_analysis_results', 'immunophenotypic_analysis_tested', 'informed_consent_verified', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'ldh_level', 'ldh_norm_range_upper', 'longest_dimension', 'lost_follow_up', 'lymph_node_involvement_site', 'maximum_tumor_bulk_anatomic_site', 'maximum_tumor_dimension', 'mib1_positive_percentage_range', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'pet_scan_results', 'primary_therapy_outcome_success', 'radiation_therapy', 'sample_type', 'sample_type_id', 'shortest_dimension', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_DLBC_PDMRNAseq', '_GENOMIC_ID_TCGA_DLBC_hMethyl450', '_GENOMIC_ID_TCGA_DLBC_miRNA_HiSeq', '_GENOMIC_ID_TCGA_DLBC_gistic2thd', '_GENOMIC_ID_TCGA_DLBC_PDMRNAseqCNV', '_GENOMIC_ID_data/public/TCGA/DLBC/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_DLBC_gistic2', '_GENOMIC_ID_TCGA_DLBC_mutation_bcm_gene', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_DLBC_RPPA', '_GENOMIC_ID_TCGA_DLBC_exp_HiSeqV2_PANCAN']\n" ] } ], "source": [ "import os\n", "\n", "# List all subdirectories in tcga_root_dir\n", "subdirs = os.listdir(tcga_root_dir)\n", "print(f\"Available TCGA directories: {subdirs}\")\n", "\n", "# Check if there's any appropriate TCGA dataset for Autoinflammatory Disorders\n", "# Autoinflammatory disorders involve chronic inflammation which could be relevant to:\n", "# - Lymphomas (immune system cancers)\n", "# - Cancers with inflammatory components\n", "\n", "# Look for potential matches\n", "potential_matches = []\n", "immune_related = ['TCGA_Large_Bcell_Lymphoma_(DLBC)'] # Immune system cancers\n", "\n", "# Add immune-related cancers to potential matches\n", "for dir_name in subdirs:\n", " if dir_name in immune_related:\n", " potential_matches.append(dir_name)\n", "\n", "if potential_matches:\n", " print(f\"Potential matches for {trait}: {potential_matches}\")\n", " # Select the most specific match\n", " selected_dir = potential_matches[0] # First match as default\n", " cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n", " \n", " # Get paths to clinical and genetic files\n", " clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", " \n", " # 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", " print(f\"\\nSelected directory: {selected_dir}\")\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", " # Print column names of clinical data\n", " print(\"\\nClinical data columns:\")\n", " print(clinical_df.columns.tolist())\n", " \n", " # Check if gene expression and trait data are available\n", " is_gene_available = not genetic_df.empty\n", " is_trait_available = not clinical_df.empty\n", " \n", " # Only validate, don't finalize\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 suitable directory found for {trait}. While autoinflammatory disorders involve inflammation, TCGA datasets don't directly address these conditions.\")\n", " \n", " # Mark this cohort as not usable for Autoinflammatory Disorders research\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" ] }, { "cell_type": "markdown", "id": "e9ac5294", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "f0b27d6a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:54:15.162788Z", "iopub.status.busy": "2025-03-25T06:54:15.162669Z", "iopub.status.idle": "2025-03-25T06:54:15.168917Z", "shell.execute_reply": "2025-03-25T06:54:15.168624Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age columns preview:\n", "{'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}\n", "\n", "Gender columns 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 to preview the candidate columns\n", "clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Large_Bcell_Lymphoma_(DLBC)'))\n", "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n", "\n", "# 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", "print(\"Age columns preview:\")\n", "print(age_preview)\n", "\n", "# 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(\"\\nGender columns preview:\")\n", "print(gender_preview)\n" ] }, { "cell_type": "markdown", "id": "09839d68", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "5f6f8c33", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:54:15.169917Z", "iopub.status.busy": "2025-03-25T06:54:15.169814Z", "iopub.status.idle": "2025-03-25T06:54:15.172576Z", "shell.execute_reply": "2025-03-25T06:54:15.172300Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected age column: age_at_initial_pathologic_diagnosis\n", "Selected gender column: gender\n" ] } ], "source": [ "# Check the age columns\n", "age_col = None\n", "if 'age_at_initial_pathologic_diagnosis' in {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}:\n", " # This column directly contains age values in years\n", " age_col = 'age_at_initial_pathologic_diagnosis'\n", "elif 'days_to_birth' in {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}:\n", " # This column contains negative days from birth, which can be converted to age\n", " age_col = 'days_to_birth'\n", "\n", "# Check the gender columns\n", "gender_col = None\n", "if 'gender' in {'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}:\n", " gender_col = 'gender'\n", "\n", "# Print the selected columns\n", "print(f\"Selected age column: {age_col}\")\n", "print(f\"Selected gender column: {gender_col}\")\n" ] }, { "cell_type": "markdown", "id": "e5d4eae3", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "2fbce4da", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:54:15.173577Z", "iopub.status.busy": "2025-03-25T06:54:15.173478Z", "iopub.status.idle": "2025-03-25T06:54:21.522870Z", "shell.execute_reply": "2025-03-25T06:54:21.522544Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Extracting clinical features...\n", "Clinical features shape: (48, 3)\n", "Preview of clinical features:\n", " Autoinflammatory_Disorders Age Gender\n", "sampleID \n", "TCGA-FA-8693-01 1 75 1\n", "TCGA-FA-A4BB-01 1 67 1\n", "TCGA-FA-A4XK-01 1 40 1\n", "TCGA-FA-A6HN-01 1 73 1\n", "TCGA-FA-A6HO-01 1 58 0\n", "Clinical data saved to ../../output/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv\n", "\n", "Normalizing gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Original gene expression data shape: (20530, 48)\n", "Normalized gene expression data shape: (19848, 48)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv\n", "\n", "Linking clinical and genetic data...\n", "Number of common samples: 48\n", "Linked data shape: (48, 19851)\n", "\n", "Handling missing values...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (48, 19851)\n", "\n", "Checking for biased features...\n", "Quartiles for 'Autoinflammatory_Disorders':\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 'Autoinflammatory_Disorders' in this dataset is severely biased.\n", "\n", "Quartiles for 'Age':\n", " 25%: 46.0\n", " 50% (Median): 57.5\n", " 75%: 67.0\n", "Min: 23\n", "Max: 82\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 22 occurrences. This represents 45.83% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "\n", "Validating final data quality...\n", "\n", "Data is not usable. Linked data will not be saved.\n" ] } ], "source": [ "# Step: Feature Engineering and Validation\n", "\n", "# 1. Extract and standardize clinical features\n", "print(\"\\nExtracting clinical features...\")\n", "clinical_features = tcga_select_clinical_features(\n", " clinical_df, \n", " trait=trait, \n", " age_col=age_col, \n", " gender_col=gender_col\n", ")\n", "\n", "print(f\"Clinical features shape: {clinical_features.shape}\")\n", "print(f\"Preview of clinical features:\\n{clinical_features.head()}\")\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", "\n", "# 2. Normalize gene symbols in the gene expression data\n", "print(\"\\nNormalizing gene expression data...\")\n", "genetic_df_normalized = normalize_gene_symbols_in_index(genetic_df)\n", "print(f\"Original gene expression data shape: {genetic_df.shape}\")\n", "print(f\"Normalized gene expression data shape: {genetic_df_normalized.shape}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "genetic_df_normalized.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n", "\n", "# 3. Link the clinical and genetic data on sample IDs\n", "print(\"\\nLinking clinical and genetic data...\")\n", "# Transpose genetic data to have samples as rows and genes as columns\n", "genetic_df_for_linking = genetic_df_normalized.T\n", "\n", "# Ensure sample IDs in clinical features match those in genetic data\n", "common_samples = clinical_features.index.intersection(genetic_df_for_linking.index)\n", "print(f\"Number of common samples: {len(common_samples)}\")\n", "\n", "# Filter both dataframes to keep only common samples\n", "clinical_features_common = clinical_features.loc[common_samples]\n", "genetic_df_common = genetic_df_for_linking.loc[common_samples]\n", "\n", "# Combine clinical and genetic data\n", "linked_data = pd.concat([clinical_features_common, genetic_df_common], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values systematically\n", "print(\"\\nHandling missing values...\")\n", "linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n", "print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n", "\n", "# 5. Determine if trait or demographic features are biased\n", "print(\"\\nChecking for biased features...\")\n", "is_trait_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n", "\n", "# 6. Validate data quality and save cohort information\n", "print(\"\\nValidating final 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=genetic_df_normalized.shape[0] > 0,\n", " is_trait_available=clinical_features.shape[0] > 0,\n", " is_biased=is_trait_biased,\n", " df=linked_data_clean,\n", " note=\"Pancreatic cancer dataset used as proxy for Type 2 Diabetes due to pancreatic involvement in diabetes.\"\n", ")\n", "\n", "# 7. Save the linked data if usable\n", "if is_usable:\n", " print(f\"\\nData is usable. Saving linked data to {out_data_file}...\")\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data_clean.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"\\nData is not usable. Linked data will not be 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 }