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{
"cells": [
{
"cell_type": "code",
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"metadata": {
"execution": {
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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 = \"Atherosclerosis\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Atherosclerosis/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Atherosclerosis/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Atherosclerosis/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Atherosclerosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "675f7f26",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "679a9f06",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking for a relevant cohort directory for Atherosclerosis...\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",
"Atherosclerosis-related cohorts: []\n",
"No suitable cohort found for Atherosclerosis.\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Check if there's a suitable cohort directory for Atherosclerosis\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",
"# Atherosclerosis-related keywords \n",
"atherosclerosis_keywords = ['cardio', 'vascular', 'arter', 'heart', 'blood', 'vessel', 'atherosclerosis']\n",
"\n",
"# Look for atherosclerosis-related directories\n",
"atherosclerosis_related_dirs = []\n",
"for d in available_dirs:\n",
" if any(keyword in d.lower() for keyword in atherosclerosis_keywords):\n",
" atherosclerosis_related_dirs.append(d)\n",
"\n",
"print(f\"Atherosclerosis-related cohorts: {atherosclerosis_related_dirs}\")\n",
"\n",
"if not atherosclerosis_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",
" # Select the most specific match for atherosclerosis conditions\n",
" selected_cohort = atherosclerosis_related_dirs[0] # Take 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}\")"
]
}
],
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