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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "44353979",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:55:36.751525Z",
"iopub.status.busy": "2025-03-25T04:55:36.751349Z",
"iopub.status.idle": "2025-03-25T04:55:36.911928Z",
"shell.execute_reply": "2025-03-25T04:55:36.911595Z"
}
},
"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 = \"Werner_Syndrome\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Werner_Syndrome/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Werner_Syndrome/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Werner_Syndrome/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Werner_Syndrome/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7879037c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6628b8b6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:55:36.913336Z",
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"shell.execute_reply": "2025-03-25T04:55:36.917723Z"
}
},
"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 relevant directories for Werner_Syndrome: []\n",
"No directory specifically relevant to the trait: Werner_Syndrome\n",
"Task marked as completed. Werner_Syndrome is not directly represented in the TCGA dataset.\n"
]
}
],
"source": [
"# Step 1: Review subdirectories to find one related to Werner Syndrome\n",
"import os\n",
"\n",
"# List all directories in TCGA root directory\n",
"tcga_dirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA directories: {tcga_dirs}\")\n",
"\n",
"# Look for directories related to Werner Syndrome\n",
"relevant_dirs = []\n",
"for dir_name in tcga_dirs:\n",
" dir_lower = dir_name.lower()\n",
" if \"werner\" in dir_lower or \"syndrome\" in dir_lower or \"progeria\" in dir_lower:\n",
" relevant_dirs.append(dir_name)\n",
"\n",
"print(f\"Potential relevant directories for {trait}: {relevant_dirs}\")\n",
"\n",
"# Since TCGA is primarily a cancer genomics database, it's unlikely to have a specific\n",
"# directory for Werner Syndrome. We should check the clinical data columns of datasets\n",
"# to see if any contain information relevant to Werner Syndrome.\n",
"\n",
"if not relevant_dirs:\n",
" print(f\"No directory specifically relevant to the trait: {trait}\")\n",
" \n",
" # Since Werner Syndrome is a rare genetic disorder and TCGA focuses on cancer genomics,\n",
" # it's unlikely that this data exists in this database format\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",
" print(f\"Task marked as completed. {trait} is not directly represented in the TCGA dataset.\")\n",
"else:\n",
" # If by chance we did find a relevant directory, proceed with loading the data\n",
" selected_dir = relevant_dirs[0]\n",
" print(f\"Selected directory for {trait}: {selected_dir}\")\n",
" \n",
" # Get the full path to the directory\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
" \n",
" # Step 2: Find clinical and genetic data files\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" print(f\"Clinical data file: {clinical_file_path}\")\n",
" print(f\"Genetic data file: {genetic_file_path}\")\n",
" \n",
" # Step 3: Load the data files\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",
" # Step 4: Print column names of clinical data\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Check if both datasets are available\n",
" is_gene_available = not genetic_df.empty\n",
" is_trait_available = not clinical_df.empty\n",
" \n",
" # Initial validation\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",
" )"
]
}
],
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