{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b1b98b88", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:19:14.408918Z", "iopub.status.busy": "2025-03-25T08:19:14.408730Z", "iopub.status.idle": "2025-03-25T08:19:14.574906Z", "shell.execute_reply": "2025-03-25T08:19:14.574504Z" } }, "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 = \"Chronic_kidney_disease\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Chronic_kidney_disease/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Chronic_kidney_disease/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Chronic_kidney_disease/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Chronic_kidney_disease/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "6895d429", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "da823839", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:19:14.576143Z", "iopub.status.busy": "2025-03-25T08:19:14.575988Z", "iopub.status.idle": "2025-03-25T08:19:14.835011Z", "shell.execute_reply": "2025-03-25T08:19:14.834467Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking for a relevant cohort directory for Chronic_kidney_disease...\n", "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", "Kidney disease-related cohorts: ['TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)']\n", "Selected cohort: TCGA_Kidney_Chromophobe_(KICH)\n", "Clinical data file: TCGA.KICH.sampleMap_KICH_clinicalMatrix\n", "Genetic data file: TCGA.KICH.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', 'additional_surgery_locoregional_procedure', 'additional_surgery_metastatic_procedure', 'age_at_initial_pathologic_diagnosis', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'clinical_M', 'days_to_additional_surgery_metastatic_procedure', 'days_to_birth', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_after_initial_treatment', 'eastern_cancer_oncology_group', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'hemoglobin_result', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'intermediate_dimension', 'is_ffpe', 'karnofsky_performance_score', 'lactate_dehydrogenase_result', 'laterality', 'longest_dimension', 'lost_follow_up', 'lymph_node_examined_count', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive', 'number_pack_years_smoked', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'percent_tumor_sarcomatoid', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'platelet_qualitative_result', 'presence_of_sarcomatoid_features', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'sample_type', 'sample_type_id', 'serum_calcium_result', 'shortest_dimension', 'stopped_smoking_year', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'tumor_tissue_site', 'vial_number', 'vital_status', 'white_cell_count_result', 'year_of_initial_pathologic_diagnosis', 'year_of_tobacco_smoking_onset', '_GENOMIC_ID_TCGA_KICH_PDMRNAseq', '_GENOMIC_ID_TCGA_KICH_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_KICH_gistic2thd', '_GENOMIC_ID_TCGA_KICH_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_KICH_exp_HiSeqV2', '_GENOMIC_ID_TCGA_KICH_RPPA', '_GENOMIC_ID_TCGA_KICH_miRNA_HiSeq', '_GENOMIC_ID_TCGA_KICH_mutation_bcm_gene', '_GENOMIC_ID_TCGA_KICH_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_KICH_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_KICH_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_KICH_hMethyl450', '_GENOMIC_ID_TCGA_KICH_mutation_broad_gene', '_GENOMIC_ID_data/public/TCGA/KICH/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_KICH_gistic2']\n", "\n", "Clinical data shape: (91, 90)\n", "Genetic data shape: (20530, 91)\n" ] } ], "source": [ "import os\n", "\n", "# Check if there's a suitable cohort directory for Chronic_kidney_disease\n", "print(f\"Looking for a relevant cohort directory for {trait}...\")\n", "\n", "# Check available cohorts\n", "available_dirs = os.listdir(tcga_root_dir)\n", "print(f\"Available cohorts: {available_dirs}\")\n", "\n", "# Kidney disease-related keywords\n", "kidney_keywords = ['kidney', 'renal', 'nephro', 'kich', 'kirc', 'kirp']\n", "\n", "# Look for Kidney disease-related directories\n", "kidney_related_dirs = []\n", "for d in available_dirs:\n", " if any(keyword in d.lower() for keyword in kidney_keywords):\n", " kidney_related_dirs.append(d)\n", "\n", "print(f\"Kidney disease-related cohorts: {kidney_related_dirs}\")\n", "\n", "if not kidney_related_dirs:\n", " print(f\"No suitable cohort found for {trait}.\")\n", " # Mark the task as completed by recording the unavailability\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", " # Exit the script early since no suitable cohort was found\n", " selected_cohort = None\n", "else:\n", " # Since we're looking for chronic kidney disease specifically, prioritize \n", " # directories that might be more relevant to this specific condition\n", " # For now, we'll take all matches as they're all kidney-related cancers\n", " selected_cohort = kidney_related_dirs[0] # We'll use the first match if multiple exist\n", "\n", "if selected_cohort:\n", " print(f\"Selected cohort: {selected_cohort}\")\n", " \n", " # Get the full path to the selected cohort directory\n", " cohort_dir = os.path.join(tcga_root_dir, selected_cohort)\n", " \n", " # Get the clinical and genetic data file paths\n", " clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", " \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", " # Load the 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 the column names of the clinical data\n", " print(\"\\nClinical data columns:\")\n", " print(clinical_df.columns.tolist())\n", " \n", " # Basic info about the datasets\n", " print(f\"\\nClinical data shape: {clinical_df.shape}\")\n", " print(f\"Genetic data shape: {genetic_df.shape}\")\n" ] }, { "cell_type": "markdown", "id": "8948b43d", "metadata": {}, "source": [ "### Step 2: Find Candidate Demographic Features" ] }, { "cell_type": "code", "execution_count": 3, "id": "59411f9a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:19:14.836574Z", "iopub.status.busy": "2025-03-25T08:19:14.836444Z", "iopub.status.idle": "2025-03-25T08:19:14.846280Z", "shell.execute_reply": "2025-03-25T08:19:14.845830Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age columns preview:\n", "{'age_at_initial_pathologic_diagnosis': [53.0, 71.0, 71.0, 67.0, 80.0], 'days_to_birth': [-19603.0, -26244.0, -26134.0, -24626.0, -29275.0]}\n", "Gender columns preview:\n", "{'gender': ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']}\n" ] } ], "source": [ "# Identifying candidate age and gender columns from clinical data columns\n", "candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n", "candidate_gender_cols = ['gender']\n", "\n", "# Loading clinical data to preview candidate columns\n", "clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)'))\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", "print(\"Age columns preview:\")\n", "print(age_preview)\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(\"Gender columns preview:\")\n", "print(gender_preview)\n" ] }, { "cell_type": "markdown", "id": "3d9a43c8", "metadata": {}, "source": [ "### Step 3: Select Demographic Features" ] }, { "cell_type": "code", "execution_count": 4, "id": "9caf008e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:19:14.847754Z", "iopub.status.busy": "2025-03-25T08:19:14.847632Z", "iopub.status.idle": "2025-03-25T08:19:14.851271Z", "shell.execute_reply": "2025-03-25T08:19:14.850830Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected age column: age_at_initial_pathologic_diagnosis\n", "Age column preview: [53.0, 71.0, 71.0, 67.0, 80.0]\n", "Selected gender column: gender\n", "Gender column preview: ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']\n" ] } ], "source": [ "# Step: Select Demographic Features\n", "\n", "# Selecting age column\n", "age_columns = {'age_at_initial_pathologic_diagnosis': [53.0, 71.0, 71.0, 67.0, 80.0], \n", " 'days_to_birth': [-19603.0, -26244.0, -26134.0, -24626.0, -29275.0]}\n", "\n", "# Examine age columns\n", "# 'age_at_initial_pathologic_diagnosis' contains direct age values (in years)\n", "# 'days_to_birth' contains negative values representing days before birth (more complex to interpret)\n", "# Choose 'age_at_initial_pathologic_diagnosis' as it directly represents age in years\n", "age_col = 'age_at_initial_pathologic_diagnosis'\n", "\n", "# Selecting gender column\n", "gender_columns = {'gender': ['MALE', 'MALE', 'FEMALE', 'MALE', 'MALE']}\n", "\n", "# There's only one gender column and it contains valid values (MALE/FEMALE)\n", "gender_col = 'gender'\n", "\n", "# Print the chosen columns\n", "print(f\"Selected age column: {age_col}\")\n", "print(f\"Age column preview: {age_columns[age_col]}\")\n", "print(f\"Selected gender column: {gender_col}\")\n", "print(f\"Gender column preview: {gender_columns[gender_col]}\")\n" ] }, { "cell_type": "markdown", "id": "07c078a0", "metadata": {}, "source": [ "### Step 4: Feature Engineering and Validation" ] }, { "cell_type": "code", "execution_count": 5, "id": "b1c60afd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:19:14.852682Z", "iopub.status.busy": "2025-03-25T08:19:14.852574Z", "iopub.status.idle": "2025-03-25T08:19:52.373363Z", "shell.execute_reply": "2025-03-25T08:19:52.372794Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical features (first 5 rows):\n", " Chronic_kidney_disease Age Gender\n", "sampleID \n", "TCGA-2K-A9WE-01 1 53.0 1\n", "TCGA-2Z-A9J1-01 1 71.0 1\n", "TCGA-2Z-A9J2-01 1 71.0 0\n", "TCGA-2Z-A9J3-01 1 67.0 1\n", "TCGA-2Z-A9J5-01 1 80.0 1\n", "\n", "Processing gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Original gene data shape: (20530, 323)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Attempting to normalize gene symbols...\n", "Gene data shape after normalization: (19848, 323)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to: ../../output/preprocess/Chronic_kidney_disease/gene_data/TCGA.csv\n", "\n", "Linking clinical and genetic data...\n", "Clinical data shape: (352, 3)\n", "Genetic data shape: (19848, 323)\n", "Number of common samples: 323\n", "\n", "Linked data shape: (323, 19851)\n", "Linked data preview (first 5 rows, first few columns):\n", " Chronic_kidney_disease Age Gender A1BG A1BG-AS1\n", "TCGA-5P-A9KE-01 1 70.0 1 -1.832274 -2.060683\n", "TCGA-B9-7268-01 1 59.0 1 -2.074374 -2.547183\n", "TCGA-BQ-5879-01 1 32.0 0 0.389126 0.522517\n", "TCGA-P4-A5ED-01 1 51.0 1 1.791126 1.324417\n", "TCGA-Y8-A8RY-01 1 63.0 1 -0.335474 -0.016783\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Data shape after handling missing values: (323, 19851)\n", "\n", "Checking for bias in features:\n", "For the feature 'Chronic_kidney_disease', the least common label is '0' with 32 occurrences. This represents 9.91% of the dataset.\n", "The distribution of the feature 'Chronic_kidney_disease' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 54.0\n", " 50% (Median): 61.459375\n", " 75%: 71.0\n", "Min: 28.0\n", "Max: 88.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0' with 87 occurrences. This represents 26.93% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "\n", "Performing final validation...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to: ../../output/preprocess/Chronic_kidney_disease/TCGA.csv\n", "Clinical data saved to: ../../output/preprocess/Chronic_kidney_disease/clinical_data/TCGA.csv\n" ] } ], "source": [ "# 1. Extract and standardize clinical features\n", "# Use tcga_select_clinical_features which will automatically create the trait variable and add age/gender if provided\n", "# Use the correct cohort identified in Step 1\n", "cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)')\n", "clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n", "\n", "# Load the clinical data if not already loaded\n", "clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n", "\n", "linked_clinical_df = tcga_select_clinical_features(\n", " clinical_df, \n", " trait=trait, \n", " age_col=age_col, \n", " gender_col=gender_col\n", ")\n", "\n", "# Print preview of clinical features\n", "print(\"Clinical features (first 5 rows):\")\n", "print(linked_clinical_df.head())\n", "\n", "# 2. Process gene expression data\n", "print(\"\\nProcessing gene expression data...\")\n", "# Load genetic data from the same cohort directory\n", "genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n", "\n", "# Check gene data shape\n", "print(f\"Original gene data shape: {genetic_df.shape}\")\n", "\n", "# Save a version of the gene data before normalization (as a backup)\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "genetic_df.to_csv(out_gene_data_file.replace('.csv', '_original.csv'))\n", "\n", "# We need to transpose genetic data so genes are rows and samples are columns for normalization\n", "gene_df_for_norm = genetic_df.copy() # Keep original orientation for now\n", "\n", "# Try to normalize gene symbols - adding debug output to understand what's happening\n", "print(\"Attempting to normalize gene symbols...\")\n", "try:\n", " # First check if we need to transpose based on the data format\n", " # In TCGA data, typically genes are rows and samples are columns\n", " if gene_df_for_norm.shape[0] > gene_df_for_norm.shape[1]:\n", " # More rows than columns, likely genes are rows already\n", " normalized_gene_df = normalize_gene_symbols_in_index(gene_df_for_norm)\n", " else:\n", " # Need to transpose first\n", " normalized_gene_df = normalize_gene_symbols_in_index(gene_df_for_norm.T)\n", " \n", " print(f\"Gene data shape after normalization: {normalized_gene_df.shape}\")\n", " \n", " # Check if normalization returned empty DataFrame\n", " if normalized_gene_df.shape[0] == 0:\n", " print(\"WARNING: Gene symbol normalization returned an empty DataFrame.\")\n", " print(\"Using original gene data instead of normalized data.\")\n", " # Use original data\n", " normalized_gene_df = genetic_df\n", " \n", "except Exception as e:\n", " print(f\"Error during gene symbol normalization: {e}\")\n", " print(\"Using original gene data instead.\")\n", " normalized_gene_df = genetic_df\n", "\n", "# Save gene data\n", "normalized_gene_df.to_csv(out_gene_data_file)\n", "print(f\"Gene data saved to: {out_gene_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "# TCGA data uses the same sample IDs in both datasets\n", "print(\"\\nLinking clinical and genetic data...\")\n", "print(f\"Clinical data shape: {linked_clinical_df.shape}\")\n", "print(f\"Genetic data shape: {normalized_gene_df.shape}\")\n", "\n", "# Find common samples between clinical and genetic data\n", "# In TCGA, samples are typically columns in the gene data and index in the clinical data\n", "common_samples = set(linked_clinical_df.index).intersection(set(normalized_gene_df.columns))\n", "print(f\"Number of common samples: {len(common_samples)}\")\n", "\n", "if len(common_samples) == 0:\n", " print(\"ERROR: No common samples found between clinical and genetic data.\")\n", " # Try the alternative orientation\n", " common_samples = set(linked_clinical_df.index).intersection(set(normalized_gene_df.index))\n", " print(f\"Checking alternative orientation: {len(common_samples)} common samples found.\")\n", " \n", " if len(common_samples) == 0:\n", " # Use is_final=False mode which doesn't require df and is_biased\n", " validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True\n", " )\n", " print(\"The dataset was determined to be unusable for this trait due to no common samples. No data files were saved.\")\n", "else:\n", " # Filter clinical data to only include common samples\n", " linked_clinical_df = linked_clinical_df.loc[list(common_samples)]\n", " \n", " # Create linked data by merging\n", " linked_data = pd.concat([linked_clinical_df, normalized_gene_df[list(common_samples)].T], axis=1)\n", " \n", " print(f\"\\nLinked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first few columns):\")\n", " display_cols = [trait, 'Age', 'Gender'] + list(linked_data.columns[3:5])\n", " print(linked_data[display_cols].head())\n", " \n", " # 4. Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"\\nData shape after handling missing values: {linked_data.shape}\")\n", " \n", " # 5. Check for bias in features\n", " print(\"\\nChecking for bias in features:\")\n", " is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # 6. Validate and save cohort info\n", " print(\"\\nPerforming final validation...\")\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=\"TCGA\",\n", " info_path=json_path,\n", " is_gene_available=len(linked_data.columns) > 3, # More than just trait/age/gender columns\n", " is_trait_available=trait in linked_data.columns,\n", " is_biased=is_trait_biased,\n", " df=linked_data,\n", " note=\"Data from TCGA Kidney Papillary Cell Carcinoma cohort used for chronic kidney disease analysis.\"\n", " )\n", " \n", " # 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", " \n", " # Also save clinical data separately\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_columns = [col for col in linked_data.columns if col in [trait, 'Age', 'Gender']]\n", " linked_data[clinical_columns].to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n", " else:\n", " print(\"The dataset was determined to be unusable for this trait. No data files were 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 }