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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f88252d5",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:52.473153Z",
"iopub.status.busy": "2025-03-25T06:30:52.473042Z",
"iopub.status.idle": "2025-03-25T06:30:52.637314Z",
"shell.execute_reply": "2025-03-25T06:30:52.636933Z"
}
},
"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 = \"Ankylosing_Spondylitis\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Ankylosing_Spondylitis/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Ankylosing_Spondylitis/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Ankylosing_Spondylitis/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Ankylosing_Spondylitis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "9eed7bbf",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1de2aba3",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:52.638801Z",
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"shell.execute_reply": "2025-03-25T06:30:52.643301Z"
}
},
"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",
"Potential Ankylosing_Spondylitis-related directories found: []\n",
"No TCGA subdirectory contains terms directly related to Ankylosing_Spondylitis.\n",
"TCGA is primarily a cancer genomics database and may not have specific data for this inflammatory condition.\n",
"Task completed: Ankylosing_Spondylitis data not available in TCGA dataset.\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Step 1: Look for directories related to Ankylosing Spondylitis (inflammatory arthritis affecting the spine)\n",
"tcga_subdirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA subdirectories: {tcga_subdirs}\")\n",
"\n",
"# Check if any directories contain relevant terms to Ankylosing Spondylitis\n",
"as_related_terms = [\"spondylitis\", \"arthritis\", \"inflammatory\", \"spine\", \"joint\", \"sacroiliac\", \"rheumatic\"]\n",
"potential_dirs = []\n",
"\n",
"for directory in tcga_subdirs:\n",
" if any(term.lower() in directory.lower() for term in as_related_terms):\n",
" potential_dirs.append(directory)\n",
"\n",
"print(f\"Potential {trait}-related directories found: {potential_dirs}\")\n",
"\n",
"if potential_dirs:\n",
" # Select the most specific match if found\n",
" target_dir = potential_dirs[0]\n",
" target_path = os.path.join(tcga_root_dir, target_dir)\n",
" \n",
" print(f\"Selected directory: {target_dir}\")\n",
" \n",
" # Get the clinical and genetic data file paths\n",
" clinical_path, genetic_path = tcga_get_relevant_filepaths(target_path)\n",
" \n",
" # Load the datasets\n",
" clinical_df = pd.read_csv(clinical_path, sep='\\t', index_col=0)\n",
" genetic_df = pd.read_csv(genetic_path, sep='\\t', index_col=0)\n",
" \n",
" # Print column names of clinical data\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Check if we have both gene data and potential trait data\n",
" has_gene_data = not genetic_df.empty\n",
" has_potential_trait_data = not clinical_df.empty\n",
" \n",
" # Record our initial assessment\n",
" validate_and_save_cohort_info(\n",
" is_final=False, \n",
" cohort=\"TCGA\", \n",
" info_path=json_path, \n",
" is_gene_available=has_gene_data, \n",
" is_trait_available=has_potential_trait_data\n",
" )\n",
"else:\n",
" print(f\"No TCGA subdirectory contains terms directly related to {trait}.\")\n",
" print(\"TCGA is primarily a cancer genomics database and may not have specific data for this inflammatory condition.\")\n",
" \n",
" # Marking the trait as unavailable in the cohort_info.json\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",
" \n",
" print(f\"Task completed: {trait} data not available in TCGA dataset.\")"
]
}
],
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