{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a66d0751", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:50:24.335964Z", "iopub.status.busy": "2025-03-25T05:50:24.335642Z", "iopub.status.idle": "2025-03-25T05:50:24.500372Z", "shell.execute_reply": "2025-03-25T05:50:24.499936Z" } }, "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 = \"Hypertrophic_Cardiomyopathy\"\n", "\n", "# Input paths\n", "tcga_root_dir = \"../../input/TCGA\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/TCGA.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/gene_data/TCGA.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/TCGA.csv\"\n", "json_path = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "1e311673", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4dff5875", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:50:24.501825Z", "iopub.status.busy": "2025-03-25T05:50:24.501687Z", "iopub.status.idle": "2025-03-25T05:50:24.508006Z", "shell.execute_reply": "2025-03-25T05:50:24.507623Z" } }, "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", "No suitable directory found for Hypertrophic_Cardiomyopathy.\n", "Skipping this trait as no suitable data was found in TCGA.\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 Hutchinson-Gilford Progeria Syndrome\n", "# Define key terms relevant to Progeria Syndrome\n", "key_terms = [\"progeria\", \"aging\", \"premature\", \"gilford\", \"hutchinson\", \"skin\", \"aging\", \"lamin\"]\n", "\n", "# Initialize variables for best match\n", "best_match = None\n", "best_match_score = 0\n", "min_threshold = 1 # Require at least 1 matching term\n", "\n", "# Convert trait to lowercase for case-insensitive matching\n", "target_trait = trait.lower() # \"hutchinson-gilford_progeria_syndrome\"\n", "\n", "# Search for relevant directories\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 for exact matches with key parts of the syndrome name\n", " if \"progeria\" in subdir_lower or \"hutchinson\" in subdir_lower or \"gilford\" in subdir_lower:\n", " best_match = subdir\n", " print(f\"Found exact match: {subdir}\")\n", " break\n", " \n", " # Calculate score based on key terms\n", " score = 0\n", " for term in key_terms:\n", " if term.lower() in subdir_lower:\n", " score += 1\n", " \n", " # Update best match if score is higher than current best\n", " if score > best_match_score and score >= min_threshold:\n", " best_match_score = score\n", " best_match = subdir\n", " print(f\"Found potential match: {subdir} (score: {score})\")\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.\")" ] } ], "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 }