{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "23c203ef", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:19.564116Z", "iopub.status.busy": "2025-03-25T07:27:19.563665Z", "iopub.status.idle": "2025-03-25T07:27:19.728606Z", "shell.execute_reply": "2025-03-25T07:27:19.728265Z" } }, "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 = \"Large_B-cell_Lymphoma\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Large_B-cell_Lymphoma/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Large_B-cell_Lymphoma/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Large_B-cell_Lymphoma/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Large_B-cell_Lymphoma/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "98ef1f2f", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4252668e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:19.730145Z", "iopub.status.busy": "2025-03-25T07:27:19.729988Z", "iopub.status.idle": "2025-03-25T07:27:19.883387Z", "shell.execute_reply": "2025-03-25T07:27:19.883014Z" } }, "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", "Found potential match: TCGA_Large_Bcell_Lymphoma_(DLBC) (matched keyword: lymphoma)\n", "Selected directory: TCGA_Large_Bcell_Lymphoma_(DLBC)\n", "Clinical file: TCGA.DLBC.sampleMap_DLBC_clinicalMatrix\n", "Genetic 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", "\n", "Clinical data shape: (48, 105)\n", "Genetic data shape: (20530, 48)\n" ] } ], "source": [ "import os\n", "import pandas as pd\n", "\n", "# 1. List all subdirectories in the TCGA root directory\n", "subdirectories = os.listdir(tcga_root_dir)\n", "print(f\"Available TCGA subdirectories: {subdirectories}\")\n", "\n", "# The target trait is Large_B-cell_Lymphoma\n", "target_trait = trait.lower() # \"large_b-cell_lymphoma\"\n", "\n", "# Search for a directory matching our trait\n", "best_match = None\n", "relevant_keywords = [\"lymphoma\", \"bcell\", \"dlbc\", \"large b\"]\n", "\n", "for subdir in subdirectories:\n", " if not os.path.isdir(os.path.join(tcga_root_dir, subdir)) or subdir.startswith('.'):\n", " continue\n", " \n", " subdir_lower = subdir.lower()\n", " \n", " # Check if the directory name contains any of our relevant keywords\n", " for keyword in relevant_keywords:\n", " if keyword in subdir_lower:\n", " best_match = subdir\n", " print(f\"Found potential match: {subdir} (matched keyword: {keyword})\")\n", " break\n", " \n", " if best_match:\n", " break\n", "\n", "# Handle the case where a match is found\n", "if best_match:\n", " print(f\"Selected directory: {best_match}\")\n", " \n", " # 2. Get the clinical and genetic data file paths\n", " cohort_dir = os.path.join(tcga_root_dir, best_match)\n", " clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", " \n", " print(f\"Clinical file: {os.path.basename(clinical_file_path)}\")\n", " print(f\"Genetic file: {os.path.basename(genetic_file_path)}\")\n", " \n", " # 3. Load the data 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", " # 4. Print clinical data columns for inspection\n", " print(\"\\nClinical data columns:\")\n", " print(clinical_df.columns.tolist())\n", " \n", " # Print basic information about the datasets\n", " print(f\"\\nClinical data shape: {clinical_df.shape}\")\n", " print(f\"Genetic data shape: {genetic_df.shape}\")\n", " \n", " # Check if we have both gene and trait data\n", " is_gene_available = genetic_df.shape[0] > 0\n", " is_trait_available = clinical_df.shape[0] > 0\n", " \n", "else:\n", " print(f\"No suitable directory found for {trait}.\")\n", " is_gene_available = False\n", " is_trait_available = False\n", "\n", "# Record the data availability\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", "\n", "# Exit if no suitable directory was found\n", "if not best_match:\n", " print(\"Skipping this trait as no suitable data was found in TCGA.\")\n" ] }, { "cell_type": "markdown", "id": "d03f19a3", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "9869435c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:19.884657Z", "iopub.status.busy": "2025-03-25T07:27:19.884546Z", "iopub.status.idle": "2025-03-25T07:27:19.890769Z", "shell.execute_reply": "2025-03-25T07:27:19.890468Z" } }, "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": [ "# Identify candidate age and gender columns\n", "candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n", "candidate_gender_cols = ['gender']\n", "\n", "# Load the clinical data file\n", "cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Large_Bcell_Lymphoma_(DLBC)')\n", "clinical_file_path, genetic_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 and preview the candidate 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", "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" ] }, { "cell_type": "markdown", "id": "7e8cc6ec", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "480c8aed", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:19.891872Z", "iopub.status.busy": "2025-03-25T07:27:19.891768Z", "iopub.status.idle": "2025-03-25T07:27:19.895233Z", "shell.execute_reply": "2025-03-25T07:27:19.894920Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining age candidate columns:\n", " age_at_initial_pathologic_diagnosis: [75, 67, 40, 73, 58] (Missing: 0.0%)\n", " days_to_birth: [-27468, -24590, -14723, -27025, -21330] (Missing: 0.0%)\n", "Examining gender candidate columns:\n", " gender: ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE'] (Missing: 0.0%)\n", "\n", "Selected demographic columns:\n", "Age column: age_at_initial_pathologic_diagnosis\n", "Gender column: gender\n" ] } ], "source": [ "# Step: Select Demographic Features\n", "\n", "# Examine age columns\n", "print(\"Examining age candidate columns:\")\n", "for col, values in {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'days_to_birth': [-27468, -24590, -14723, -27025, -21330]}.items():\n", " missing_count = values.count(None) if None in values else 0\n", " missing_percentage = missing_count / len(values) * 100\n", " print(f\" {col}: {values} (Missing: {missing_percentage:.1f}%)\")\n", "\n", "# Examine gender columns\n", "print(\"Examining gender candidate columns:\")\n", "for col, values in {'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}.items():\n", " missing_count = values.count(None) if None in values else 0\n", " missing_percentage = missing_count / len(values) * 100\n", " print(f\" {col}: {values} (Missing: {missing_percentage:.1f}%)\")\n", "\n", "# Select appropriate columns\n", "# For age: 'age_at_initial_pathologic_diagnosis' is more intuitive and directly usable than 'days_to_birth'\n", "age_col = 'age_at_initial_pathologic_diagnosis'\n", "\n", "# For gender: Only one column is available and it seems to contain valid values\n", "gender_col = 'gender'\n", "\n", "# Print chosen columns\n", "print(\"\\nSelected demographic columns:\")\n", "print(f\"Age column: {age_col}\")\n", "print(f\"Gender column: {gender_col}\")\n" ] }, { "cell_type": "markdown", "id": "04d3c96c", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "180303db", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:19.896341Z", "iopub.status.busy": "2025-03-25T07:27:19.896236Z", "iopub.status.idle": "2025-03-25T07:27:26.196705Z", "shell.execute_reply": "2025-03-25T07:27:26.196352Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Large_B-cell_Lymphoma/gene_data/TCGA.csv\n", "Gene expression data shape after normalization: (19848, 48)\n", "Clinical data saved to ../../output/preprocess/Large_B-cell_Lymphoma/clinical_data/TCGA.csv\n", "Clinical data shape: (48, 3)\n", "Number of samples in clinical data: 48\n", "Number of samples in genetic data: 48\n", "Number of common samples: 48\n", "Linked data shape: (48, 19851)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (48, 19851)\n", "Quartiles for 'Large_B-cell_Lymphoma':\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 'Large_B-cell_Lymphoma' 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", "Dataset deemed not usable based on validation criteria. Data not saved.\n", "Preprocessing completed.\n" ] } ], "source": [ "# Step 1: Extract and standardize clinical features\n", "# Create clinical features dataframe with trait (Canavan Disease) using patient IDs\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", "# Step 2: Normalize gene symbols in the gene expression data\n", "# The gene symbols in TCGA genetic data are already standardized, but we'll normalize them for consistency\n", "normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)\n", "\n", "# Save the normalized gene 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\"Normalized gene expression data saved to {out_gene_data_file}\")\n", "print(f\"Gene expression data shape after normalization: {normalized_gene_df.shape}\")\n", "\n", "# Step 3: Link clinical and genetic data\n", "# Transpose genetic data to have samples as rows and genes as columns\n", "genetic_df_t = normalized_gene_df.T\n", "# Save the clinical data for reference\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", "\n", "# Verify common indices between clinical and genetic data\n", "clinical_indices = set(clinical_features.index)\n", "genetic_indices = set(genetic_df_t.index)\n", "common_indices = clinical_indices.intersection(genetic_indices)\n", "print(f\"Number of samples in clinical data: {len(clinical_indices)}\")\n", "print(f\"Number of samples in genetic data: {len(genetic_indices)}\")\n", "print(f\"Number of common samples: {len(common_indices)}\")\n", "\n", "# Link the data by using the common indices\n", "linked_data = pd.concat([clinical_features.loc[list(common_indices)], genetic_df_t.loc[list(common_indices)]], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# Step 4: Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait_col=trait)\n", "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# Step 5: Determine whether the trait and demographic features are severely biased\n", "trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)\n", "\n", "# Step 6: Conduct final quality validation and save information\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=linked_data,\n", " note=f\"Dataset contains TCGA glioma and brain tumor samples with gene expression and clinical information for {trait}.\"\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(\"Dataset deemed not usable based on validation criteria. Data not saved.\")\n", "\n", "print(\"Preprocessing completed.\")" ] } ], "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 }