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"cell_type": "code",
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"execution": {
"iopub.execute_input": "2025-03-25T08:37:09.128009Z",
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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 = \"Cystic_Fibrosis\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cystic_Fibrosis/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "75541007",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
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"cell_type": "code",
"execution_count": 2,
"id": "a4a86602",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:09.295593Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No suitable directory found for Cystic_Fibrosis.\n",
"Skipping this trait as no suitable data was found.\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# 1. Find the most relevant directory for Colon and Rectal Cancer\n",
"subdirectories = os.listdir(tcga_root_dir)\n",
"target_trait = trait.lower().replace(\"_\", \" \") # Convert to lowercase for case-insensitive matching\n",
"\n",
"# Start with no match, then find the best match based on similarity to target trait\n",
"best_match = None\n",
"best_match_score = 0\n",
"\n",
"for subdir in subdirectories:\n",
" subdir_lower = subdir.lower()\n",
" \n",
" # Calculate a simple similarity score - more matching words = better match\n",
" # This prioritizes exact matches over partial matches\n",
" score = 0\n",
" for word in target_trait.split():\n",
" if word in subdir_lower:\n",
" score += 1\n",
" \n",
" # Track the best match\n",
" if score > best_match_score:\n",
" best_match_score = score\n",
" best_match = subdir\n",
" print(f\"Found potential match: {subdir} (score: {score})\")\n",
"\n",
"# Use the best match if 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.\")"
]
}
],
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