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"cells": [
{
"cell_type": "code",
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"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:24.335964Z",
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"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"
]
},
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"execution_count": 2,
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"metadata": {
"execution": {
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{
"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.\")"
]
}
],
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