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    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:52.950278Z",
     "iopub.status.busy": "2025-03-25T08:31:52.950095Z",
     "iopub.status.idle": "2025-03-25T08:31:53.114459Z",
     "shell.execute_reply": "2025-03-25T08:31:53.114025Z"
    }
   },
   "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 = \"Creutzfeldt-Jakob_Disease\"\n",
    "\n",
    "# Input paths\n",
    "tcga_root_dir = \"../../input/TCGA\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/TCGA.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/gene_data/TCGA.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/clinical_data/TCGA.csv\"\n",
    "json_path = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb7174a",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cb1d690",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:53.115713Z",
     "iopub.status.busy": "2025-03-25T08:31:53.115574Z",
     "iopub.status.idle": "2025-03-25T08:31:53.121254Z",
     "shell.execute_reply": "2025-03-25T08:31:53.120741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No suitable directory found for Creutzfeldt-Jakob_Disease.\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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