{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d66a6f7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:21.930922Z",
     "iopub.status.busy": "2025-03-25T08:32:21.930437Z",
     "iopub.status.idle": "2025-03-25T08:32:22.100818Z",
     "shell.execute_reply": "2025-03-25T08:32:22.100463Z"
    }
   },
   "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 = \"Crohns_Disease\"\n",
    "cohort = \"GSE169568\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE169568\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE169568.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE169568.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE169568.csv\"\n",
    "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cee7d9",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd213c76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:22.102315Z",
     "iopub.status.busy": "2025-03-25T08:32:22.102163Z",
     "iopub.status.idle": "2025-03-25T08:32:22.313769Z",
     "shell.execute_reply": "2025-03-25T08:32:22.313438Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"BeadChip microarray data of peripheral blood obtained from treatment-näive IBD patients and control individuals\"\n",
      "!Series_summary\t\"Comperhensive analysis of blood transcriptomes obtained from treatment-näive IBD patients.\"\n",
      "!Series_overall_design\t\"Total RNA extracted from peripheral blood samples (n = 205) was reverse transcribed and biotin-labeled using the TargetAmp-Nano Labeling Kit for Illumina Expression BeadChip (Epicentre) according to the manufacturer’s protocol. The labeled antisense RNA was hybridized to Human HT-12 v4 BeadChip array (Illumina) following the standard producer’s hybridization protocol. The array imaging was performed on an iScan system (Illumina) according to the standard manufacturer’s protocol.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['Sex: female', 'Sex: male'], 1: ['age: 20', 'age: 39', 'age: 56', 'age: 31', 'age: 22', 'age: 32', 'age: 30', 'age: 18', 'age: 60', 'age: 33', 'age: 27', 'age: 34', 'age: 57', 'age: 72', 'age: 35', 'age: 24', 'age: 21', 'age: 62', 'age: 41', 'age: 29', 'age: 46', 'age: 49', 'age: 76', 'age: 23', 'age: 37', 'age: 64', 'age: 26', 'age: 19', 'age: 17', 'age: 48'], 2: ['diagnosis: Symptomatic control', 'diagnosis: Ulcerative colitis', \"diagnosis: Crohn's disease\", 'diagnosis: Healthy control'], 3: ['annotation file: HumanHT-12_V4_0_R2_15002873_B.bgx']}\n"
     ]
    }
   ],
   "source": [
    "from tools.preprocess import *\n",
    "# 1. Identify the paths to the SOFT file and the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Read the matrix file to obtain background information and sample characteristics data\n",
    "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "\n",
    "# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "\n",
    "# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
    "print(\"Background Information:\")\n",
    "print(background_info)\n",
    "print(\"Sample Characteristics Dictionary:\")\n",
    "print(sample_characteristics_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213435cf",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75f34f93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:22.315062Z",
     "iopub.status.busy": "2025-03-25T08:32:22.314936Z",
     "iopub.status.idle": "2025-03-25T08:32:22.338382Z",
     "shell.execute_reply": "2025-03-25T08:32:22.338069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Data Preview:\n",
      "{'GSM5209429': [0.0, 20.0, 0.0], 'GSM5209430': [0.0, 39.0, 1.0], 'GSM5209431': [0.0, 56.0, 0.0], 'GSM5209432': [0.0, 31.0, 0.0], 'GSM5209433': [1.0, 22.0, 1.0], 'GSM5209434': [0.0, 32.0, 1.0], 'GSM5209435': [0.0, 32.0, 0.0], 'GSM5209436': [0.0, 30.0, 0.0], 'GSM5209437': [0.0, 30.0, 1.0], 'GSM5209438': [0.0, 18.0, 0.0], 'GSM5209439': [0.0, 60.0, 0.0], 'GSM5209440': [0.0, 33.0, 1.0], 'GSM5209441': [0.0, 27.0, 0.0], 'GSM5209442': [0.0, 30.0, 1.0], 'GSM5209443': [0.0, 34.0, 0.0], 'GSM5209444': [0.0, 57.0, 1.0], 'GSM5209445': [0.0, 27.0, 1.0], 'GSM5209446': [0.0, 20.0, 0.0], 'GSM5209447': [0.0, 30.0, 0.0], 'GSM5209448': [1.0, 27.0, 1.0], 'GSM5209449': [0.0, 32.0, 1.0], 'GSM5209450': [0.0, 72.0, 0.0], 'GSM5209451': [1.0, 35.0, 0.0], 'GSM5209452': [0.0, 24.0, 0.0], 'GSM5209453': [1.0, 21.0, 1.0], 'GSM5209454': [0.0, 62.0, 1.0], 'GSM5209455': [1.0, 41.0, 0.0], 'GSM5209456': [0.0, 22.0, 0.0], 'GSM5209457': [0.0, 18.0, 0.0], 'GSM5209458': [0.0, 20.0, 1.0], 'GSM5209459': [1.0, 29.0, 0.0], 'GSM5209460': [0.0, 46.0, 1.0], 'GSM5209461': [0.0, 31.0, 1.0], 'GSM5209462': [0.0, 34.0, 0.0], 'GSM5209463': [0.0, 32.0, 1.0], 'GSM5209464': [0.0, 49.0, 0.0], 'GSM5209465': [1.0, 76.0, 1.0], 'GSM5209466': [1.0, 23.0, 0.0], 'GSM5209467': [0.0, 37.0, 1.0], 'GSM5209468': [0.0, 30.0, 1.0], 'GSM5209469': [0.0, 64.0, 1.0], 'GSM5209470': [0.0, 23.0, 1.0], 'GSM5209471': [0.0, 24.0, 0.0], 'GSM5209472': [0.0, 26.0, 1.0], 'GSM5209473': [1.0, 19.0, 1.0], 'GSM5209474': [0.0, 60.0, 0.0], 'GSM5209475': [1.0, 17.0, 0.0], 'GSM5209476': [1.0, 41.0, 0.0], 'GSM5209477': [1.0, 48.0, 0.0], 'GSM5209478': [0.0, 26.0, 0.0], 'GSM5209479': [0.0, 35.0, 1.0], 'GSM5209480': [0.0, 22.0, 0.0], 'GSM5209481': [0.0, 73.0, 0.0], 'GSM5209482': [1.0, 69.0, 1.0], 'GSM5209483': [0.0, 57.0, 1.0], 'GSM5209484': [0.0, 50.0, 0.0], 'GSM5209485': [0.0, 27.0, 1.0], 'GSM5209486': [0.0, 69.0, 1.0], 'GSM5209487': [0.0, 28.0, 1.0], 'GSM5209488': [0.0, 51.0, 0.0], 'GSM5209489': [0.0, 64.0, 1.0], 'GSM5209490': [0.0, 52.0, 1.0], 'GSM5209491': [0.0, 55.0, 1.0], 'GSM5209492': [0.0, 47.0, 1.0], 'GSM5209493': [0.0, 61.0, 0.0], 'GSM5209494': [0.0, 29.0, 0.0], 'GSM5209495': [0.0, 36.0, 0.0], 'GSM5209496': [0.0, 24.0, 0.0], 'GSM5209497': [0.0, 24.0, 0.0], 'GSM5209498': [0.0, 21.0, 0.0], 'GSM5209499': [0.0, 54.0, 0.0], 'GSM5209500': [0.0, 24.0, 0.0], 'GSM5209501': [0.0, 78.0, 0.0], 'GSM5209502': [0.0, 23.0, 1.0], 'GSM5209503': [0.0, 27.0, 0.0], 'GSM5209504': [0.0, 21.0, 1.0], 'GSM5209505': [0.0, 34.0, 1.0], 'GSM5209506': [0.0, 51.0, 1.0], 'GSM5209507': [1.0, 31.0, 0.0], 'GSM5209508': [1.0, 40.0, 0.0], 'GSM5209509': [1.0, 24.0, 0.0], 'GSM5209510': [1.0, 24.0, 1.0], 'GSM5209511': [0.0, 23.0, 0.0], 'GSM5209512': [0.0, 33.0, 1.0], 'GSM5209513': [0.0, 25.0, 0.0], 'GSM5209514': [0.0, 23.0, 0.0], 'GSM5209515': [0.0, 41.0, 1.0], 'GSM5209516': [0.0, 32.0, 1.0], 'GSM5209517': [1.0, 23.0, 0.0], 'GSM5209518': [0.0, 36.0, 1.0], 'GSM5209519': [1.0, 26.0, 1.0], 'GSM5209520': [1.0, 23.0, 0.0], 'GSM5209521': [1.0, 36.0, 1.0], 'GSM5209522': [1.0, 40.0, 0.0], 'GSM5209523': [1.0, 26.0, 0.0], 'GSM5209524': [1.0, 18.0, 0.0], 'GSM5209525': [0.0, 35.0, 0.0], 'GSM5209526': [0.0, 24.0, 0.0], 'GSM5209527': [0.0, 32.0, 1.0], 'GSM5209528': [0.0, 61.0, 0.0], 'GSM5209529': [0.0, 34.0, 0.0], 'GSM5209530': [0.0, 54.0, 0.0], 'GSM5209531': [1.0, 21.0, 0.0], 'GSM5209532': [0.0, 28.0, 1.0], 'GSM5209533': [1.0, 38.0, 0.0], 'GSM5209534': [1.0, 69.0, 1.0], 'GSM5209535': [0.0, 28.0, 0.0], 'GSM5209536': [0.0, 27.0, 1.0], 'GSM5209537': [0.0, 33.0, 1.0], 'GSM5209538': [0.0, 24.0, 1.0], 'GSM5209539': [0.0, 19.0, 1.0], 'GSM5209540': [1.0, 32.0, 1.0], 'GSM5209541': [0.0, 40.0, 1.0], 'GSM5209542': [0.0, 39.0, 0.0], 'GSM5209543': [1.0, 29.0, 0.0], 'GSM5209544': [1.0, 26.0, 1.0], 'GSM5209545': [1.0, 26.0, 1.0], 'GSM5209546': [0.0, 18.0, 0.0], 'GSM5209547': [0.0, 38.0, 1.0], 'GSM5209548': [0.0, 59.0, 1.0], 'GSM5209549': [1.0, 53.0, 1.0], 'GSM5209550': [0.0, 41.0, 1.0], 'GSM5209551': [1.0, 24.0, 0.0], 'GSM5209552': [1.0, 28.0, 0.0], 'GSM5209553': [1.0, 30.0, 1.0], 'GSM5209554': [0.0, 31.0, 1.0], 'GSM5209555': [0.0, 47.0, 0.0], 'GSM5209556': [0.0, 76.0, 0.0], 'GSM5209557': [0.0, 27.0, 1.0], 'GSM5209558': [0.0, 36.0, 1.0], 'GSM5209559': [0.0, 19.0, 0.0], 'GSM5209560': [0.0, 38.0, 1.0], 'GSM5209561': [1.0, 24.0, 1.0], 'GSM5209562': [0.0, 33.0, 1.0], 'GSM5209563': [0.0, 23.0, 0.0], 'GSM5209564': [0.0, 20.0, 0.0], 'GSM5209565': [1.0, 38.0, 1.0], 'GSM5209566': [0.0, 68.0, 0.0], 'GSM5209567': [0.0, 23.0, 1.0], 'GSM5209568': [1.0, 39.0, 1.0], 'GSM5209569': [1.0, 23.0, 1.0], 'GSM5209570': [1.0, 23.0, 0.0], 'GSM5209571': [0.0, 39.0, 1.0], 'GSM5209572': [0.0, 38.0, 0.0], 'GSM5209573': [0.0, 20.0, 0.0], 'GSM5209574': [1.0, 54.0, 1.0], 'GSM5209575': [0.0, 41.0, 1.0], 'GSM5209576': [0.0, 48.0, 0.0], 'GSM5209577': [0.0, 74.0, 1.0], 'GSM5209578': [0.0, 69.0, 0.0], 'GSM5209579': [0.0, 42.0, 0.0], 'GSM5209580': [1.0, 25.0, 1.0], 'GSM5209581': [0.0, 35.0, 1.0], 'GSM5209582': [1.0, 30.0, 1.0], 'GSM5209583': [1.0, 23.0, 0.0], 'GSM5209584': [0.0, 36.0, 0.0], 'GSM5209585': [0.0, 61.0, 1.0], 'GSM5209586': [0.0, 37.0, 1.0], 'GSM5209587': [0.0, 50.0, 1.0], 'GSM5209588': [0.0, 46.0, 0.0], 'GSM5209589': [0.0, 22.0, 1.0], 'GSM5209590': [0.0, 21.0, 0.0], 'GSM5209591': [0.0, 44.0, 0.0], 'GSM5209592': [0.0, 24.0, 0.0], 'GSM5209593': [0.0, 24.0, 1.0], 'GSM5209594': [0.0, 23.0, 0.0], 'GSM5209595': [0.0, 47.0, 0.0], 'GSM5209596': [0.0, 21.0, 0.0], 'GSM5209597': [0.0, 19.0, 0.0], 'GSM5209598': [0.0, 56.0, 0.0], 'GSM5209599': [0.0, 25.0, 1.0], 'GSM5209600': [0.0, 54.0, 1.0], 'GSM5209601': [0.0, 51.0, 1.0], 'GSM5209602': [0.0, 43.0, 0.0], 'GSM5209603': [0.0, 53.0, 0.0], 'GSM5209604': [0.0, 66.0, 1.0], 'GSM5209605': [0.0, 69.0, 1.0], 'GSM5209606': [0.0, 22.0, 0.0], 'GSM5209607': [0.0, 56.0, 0.0], 'GSM5209608': [0.0, 51.0, 1.0], 'GSM5209609': [0.0, 69.0, 1.0], 'GSM5209610': [0.0, 53.0, 0.0], 'GSM5209611': [0.0, 61.0, 1.0], 'GSM5209612': [0.0, 52.0, 1.0], 'GSM5209613': [0.0, 42.0, 0.0], 'GSM5209614': [0.0, 56.0, 1.0], 'GSM5209615': [1.0, 58.0, 0.0], 'GSM5209616': [1.0, 20.0, 0.0], 'GSM5209617': [1.0, 17.0, 1.0], 'GSM5209618': [0.0, 40.0, 0.0], 'GSM5209619': [1.0, 44.0, 1.0], 'GSM5209620': [0.0, 45.0, 0.0], 'GSM5209621': [1.0, 19.0, 1.0], 'GSM5209622': [0.0, 28.0, 0.0], 'GSM5209623': [0.0, 57.0, 0.0], 'GSM5209624': [1.0, 41.0, 0.0], 'GSM5209625': [0.0, 34.0, 0.0], 'GSM5209626': [0.0, 54.0, 0.0], 'GSM5209627': [1.0, 59.0, 1.0], 'GSM5209628': [0.0, 20.0, 1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE169568.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Check if this dataset likely contains gene expression data\n",
    "# Based on the background information, this dataset contains BeadChip microarray data (Illumina Human HT-12 v4), \n",
    "# which is indeed gene expression data. So we set is_gene_available to True.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Identify keys and conversion functions for trait, age, and gender data\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# Trait - Crohn's Disease (key 2 contains diagnostic information)\n",
    "trait_row = 2\n",
    "\n",
    "# Age data (key 1 contains age information)\n",
    "age_row = 1\n",
    "\n",
    "# Gender data (key 0 contains sex information)\n",
    "gender_row = 0\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert trait values to binary format:\n",
    "    1 for Crohn's disease, 0 for controls (healthy or symptomatic)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on diagnosis\n",
    "    if \"Crohn's disease\" in value:\n",
    "        return 1\n",
    "    elif \"Healthy control\" in value or \"Symptomatic control\" in value or \"Ulcerative colitis\" in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age values to continuous format\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender values to binary format:\n",
    "    0 for female, 1 for male\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    \n",
    "    if \"female\" in value:\n",
    "        return 0\n",
    "    elif \"male\" in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Determine trait data availability and save metadata\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the provided library function\n",
    "    clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the extracted clinical data\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to the specified path\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db3cd6c",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e43c924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:22.339554Z",
     "iopub.status.busy": "2025-03-25T08:32:22.339439Z",
     "iopub.status.idle": "2025-03-25T08:32:22.699990Z",
     "shell.execute_reply": "2025-03-25T08:32:22.699537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ILMN_1651209', 'ILMN_1651229', 'ILMN_1651254', 'ILMN_1651262',\n",
      "       'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651315',\n",
      "       'ILMN_1651336', 'ILMN_1651341', 'ILMN_1651343', 'ILMN_1651347',\n",
      "       'ILMN_1651354', 'ILMN_1651358', 'ILMN_1651373', 'ILMN_1651378',\n",
      "       'ILMN_1651385', 'ILMN_1651405', 'ILMN_1651415', 'ILMN_1651429'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 11727 genes × 205 samples\n"
     ]
    }
   ],
   "source": [
    "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract the gene expression data from the matrix file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "print(\"\\nFirst 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# 4. Print the dimensions of the gene expression data\n",
    "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bfd468e",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c93598b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:22.701393Z",
     "iopub.status.busy": "2025-03-25T08:32:22.701261Z",
     "iopub.status.idle": "2025-03-25T08:32:22.703303Z",
     "shell.execute_reply": "2025-03-25T08:32:22.702987Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers, I can see they use the format ILMN_XXXXXXX\n",
    "# These are Illumina BeadArray probe IDs, not human gene symbols\n",
    "# Illumina probe IDs need to be mapped to human gene symbols for biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757f43ca",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0cd84a88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:22.704495Z",
     "iopub.status.busy": "2025-03-25T08:32:22.704380Z",
     "iopub.status.idle": "2025-03-25T08:32:28.328415Z",
     "shell.execute_reply": "2025-03-25T08:32:28.328019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation dataframe column names:\n",
      "Index(['ID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene',\n",
      "       'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID',\n",
      "       'GI', 'Accession', 'Symbol', 'Protein_Product', 'Probe_Id',\n",
      "       'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE',\n",
      "       'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband',\n",
      "       'Definition', 'Ontology_Component', 'Ontology_Process',\n",
      "       'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC'],\n",
      "      dtype='object')\n",
      "\n",
      "Preview of gene annotation data:\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050'], 'Species': [nan, nan, nan], 'Source': [nan, nan, nan], 'Search_Key': [nan, nan, nan], 'Transcript': [nan, nan, nan], 'ILMN_Gene': [nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan], 'RefSeq_ID': [nan, nan, nan], 'Unigene_ID': [nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan], 'GI': [nan, nan, nan], 'Accession': [nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low'], 'Protein_Product': [nan, nan, nan], 'Probe_Id': [nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0], 'Probe_Type': [nan, nan, nan], 'Probe_Start': [nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT'], 'Chromosome': [nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan], 'Cytoband': [nan, nan, nan], 'Definition': [nan, nan, nan], 'Ontology_Component': [nan, nan, nan], 'Ontology_Process': [nan, nan, nan], 'Ontology_Function': [nan, nan, nan], 'Synonyms': [nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan], 'GB_ACC': [nan, nan, nan]}\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract gene annotation data from the SOFT file\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 3. Preview the gene annotation dataframe\n",
    "print(\"Gene annotation dataframe column names:\")\n",
    "print(gene_annotation.columns)\n",
    "\n",
    "# Preview the first few rows to understand the data structure\n",
    "print(\"\\nPreview of gene annotation data:\")\n",
    "annotation_preview = preview_df(gene_annotation, n=3)\n",
    "print(annotation_preview)\n",
    "\n",
    "# Maintain gene availability status as True based on previous steps\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f653d6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f187ccba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:28.329823Z",
     "iopub.status.busy": "2025-03-25T08:32:28.329690Z",
     "iopub.status.idle": "2025-03-25T08:32:28.529372Z",
     "shell.execute_reply": "2025-03-25T08:32:28.529019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mapped gene data dimensions: 11148 genes × 205 samples\n",
      "\n",
      "First 10 gene symbols after mapping:\n",
      "Index(['A2BP1', 'A2LD1', 'A4GNT', 'AAAS', 'AACS', 'AADACL1', 'AADACL4',\n",
      "       'AAGAB', 'AAK1', 'AAMP'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the appropriate columns in the gene annotation dataframe\n",
    "# The 'ID' column in gene_annotation contains probe IDs that match gene_data.index\n",
    "# The 'Symbol' column contains gene symbols that we want to map to\n",
    "\n",
    "# 2. Get gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "\n",
    "# Ensure no empty gene symbols\n",
    "gene_mapping = gene_mapping.dropna(subset=['Gene'])\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print dimensions of the processed gene expression data\n",
    "print(f\"\\nMapped gene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Preview the first few gene symbols after mapping\n",
    "print(\"\\nFirst 10 gene symbols after mapping:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767e30fc",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a08b637",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:28.530833Z",
     "iopub.status.busy": "2025-03-25T08:32:28.530713Z",
     "iopub.status.idle": "2025-03-25T08:32:38.607836Z",
     "shell.execute_reply": "2025-03-25T08:32:38.607384Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: 11039 genes × 205 samples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE169568.csv\n",
      "Extracting clinical features from the original source...\n",
      "Extracted clinical features preview:\n",
      "{'GSM5209429': [0.0, 20.0, 0.0], 'GSM5209430': [0.0, 39.0, 1.0], 'GSM5209431': [0.0, 56.0, 0.0], 'GSM5209432': [0.0, 31.0, 0.0], 'GSM5209433': [1.0, 22.0, 1.0], 'GSM5209434': [0.0, 32.0, 1.0], 'GSM5209435': [0.0, 32.0, 0.0], 'GSM5209436': [0.0, 30.0, 0.0], 'GSM5209437': [0.0, 30.0, 1.0], 'GSM5209438': [0.0, 18.0, 0.0], 'GSM5209439': [0.0, 60.0, 0.0], 'GSM5209440': [0.0, 33.0, 1.0], 'GSM5209441': [0.0, 27.0, 0.0], 'GSM5209442': [0.0, 30.0, 1.0], 'GSM5209443': [0.0, 34.0, 0.0], 'GSM5209444': [0.0, 57.0, 1.0], 'GSM5209445': [0.0, 27.0, 1.0], 'GSM5209446': [0.0, 20.0, 0.0], 'GSM5209447': [0.0, 30.0, 0.0], 'GSM5209448': [1.0, 27.0, 1.0], 'GSM5209449': [0.0, 32.0, 1.0], 'GSM5209450': [0.0, 72.0, 0.0], 'GSM5209451': [1.0, 35.0, 0.0], 'GSM5209452': [0.0, 24.0, 0.0], 'GSM5209453': [1.0, 21.0, 1.0], 'GSM5209454': [0.0, 62.0, 1.0], 'GSM5209455': [1.0, 41.0, 0.0], 'GSM5209456': [0.0, 22.0, 0.0], 'GSM5209457': [0.0, 18.0, 0.0], 'GSM5209458': [0.0, 20.0, 1.0], 'GSM5209459': [1.0, 29.0, 0.0], 'GSM5209460': [0.0, 46.0, 1.0], 'GSM5209461': [0.0, 31.0, 1.0], 'GSM5209462': [0.0, 34.0, 0.0], 'GSM5209463': [0.0, 32.0, 1.0], 'GSM5209464': [0.0, 49.0, 0.0], 'GSM5209465': [1.0, 76.0, 1.0], 'GSM5209466': [1.0, 23.0, 0.0], 'GSM5209467': [0.0, 37.0, 1.0], 'GSM5209468': [0.0, 30.0, 1.0], 'GSM5209469': [0.0, 64.0, 1.0], 'GSM5209470': [0.0, 23.0, 1.0], 'GSM5209471': [0.0, 24.0, 0.0], 'GSM5209472': [0.0, 26.0, 1.0], 'GSM5209473': [1.0, 19.0, 1.0], 'GSM5209474': [0.0, 60.0, 0.0], 'GSM5209475': [1.0, 17.0, 0.0], 'GSM5209476': [1.0, 41.0, 0.0], 'GSM5209477': [1.0, 48.0, 0.0], 'GSM5209478': [0.0, 26.0, 0.0], 'GSM5209479': [0.0, 35.0, 1.0], 'GSM5209480': [0.0, 22.0, 0.0], 'GSM5209481': [0.0, 73.0, 0.0], 'GSM5209482': [1.0, 69.0, 1.0], 'GSM5209483': [0.0, 57.0, 1.0], 'GSM5209484': [0.0, 50.0, 0.0], 'GSM5209485': [0.0, 27.0, 1.0], 'GSM5209486': [0.0, 69.0, 1.0], 'GSM5209487': [0.0, 28.0, 1.0], 'GSM5209488': [0.0, 51.0, 0.0], 'GSM5209489': [0.0, 64.0, 1.0], 'GSM5209490': [0.0, 52.0, 1.0], 'GSM5209491': [0.0, 55.0, 1.0], 'GSM5209492': [0.0, 47.0, 1.0], 'GSM5209493': [0.0, 61.0, 0.0], 'GSM5209494': [0.0, 29.0, 0.0], 'GSM5209495': [0.0, 36.0, 0.0], 'GSM5209496': [0.0, 24.0, 0.0], 'GSM5209497': [0.0, 24.0, 0.0], 'GSM5209498': [0.0, 21.0, 0.0], 'GSM5209499': [0.0, 54.0, 0.0], 'GSM5209500': [0.0, 24.0, 0.0], 'GSM5209501': [0.0, 78.0, 0.0], 'GSM5209502': [0.0, 23.0, 1.0], 'GSM5209503': [0.0, 27.0, 0.0], 'GSM5209504': [0.0, 21.0, 1.0], 'GSM5209505': [0.0, 34.0, 1.0], 'GSM5209506': [0.0, 51.0, 1.0], 'GSM5209507': [1.0, 31.0, 0.0], 'GSM5209508': [1.0, 40.0, 0.0], 'GSM5209509': [1.0, 24.0, 0.0], 'GSM5209510': [1.0, 24.0, 1.0], 'GSM5209511': [0.0, 23.0, 0.0], 'GSM5209512': [0.0, 33.0, 1.0], 'GSM5209513': [0.0, 25.0, 0.0], 'GSM5209514': [0.0, 23.0, 0.0], 'GSM5209515': [0.0, 41.0, 1.0], 'GSM5209516': [0.0, 32.0, 1.0], 'GSM5209517': [1.0, 23.0, 0.0], 'GSM5209518': [0.0, 36.0, 1.0], 'GSM5209519': [1.0, 26.0, 1.0], 'GSM5209520': [1.0, 23.0, 0.0], 'GSM5209521': [1.0, 36.0, 1.0], 'GSM5209522': [1.0, 40.0, 0.0], 'GSM5209523': [1.0, 26.0, 0.0], 'GSM5209524': [1.0, 18.0, 0.0], 'GSM5209525': [0.0, 35.0, 0.0], 'GSM5209526': [0.0, 24.0, 0.0], 'GSM5209527': [0.0, 32.0, 1.0], 'GSM5209528': [0.0, 61.0, 0.0], 'GSM5209529': [0.0, 34.0, 0.0], 'GSM5209530': [0.0, 54.0, 0.0], 'GSM5209531': [1.0, 21.0, 0.0], 'GSM5209532': [0.0, 28.0, 1.0], 'GSM5209533': [1.0, 38.0, 0.0], 'GSM5209534': [1.0, 69.0, 1.0], 'GSM5209535': [0.0, 28.0, 0.0], 'GSM5209536': [0.0, 27.0, 1.0], 'GSM5209537': [0.0, 33.0, 1.0], 'GSM5209538': [0.0, 24.0, 1.0], 'GSM5209539': [0.0, 19.0, 1.0], 'GSM5209540': [1.0, 32.0, 1.0], 'GSM5209541': [0.0, 40.0, 1.0], 'GSM5209542': [0.0, 39.0, 0.0], 'GSM5209543': [1.0, 29.0, 0.0], 'GSM5209544': [1.0, 26.0, 1.0], 'GSM5209545': [1.0, 26.0, 1.0], 'GSM5209546': [0.0, 18.0, 0.0], 'GSM5209547': [0.0, 38.0, 1.0], 'GSM5209548': [0.0, 59.0, 1.0], 'GSM5209549': [1.0, 53.0, 1.0], 'GSM5209550': [0.0, 41.0, 1.0], 'GSM5209551': [1.0, 24.0, 0.0], 'GSM5209552': [1.0, 28.0, 0.0], 'GSM5209553': [1.0, 30.0, 1.0], 'GSM5209554': [0.0, 31.0, 1.0], 'GSM5209555': [0.0, 47.0, 0.0], 'GSM5209556': [0.0, 76.0, 0.0], 'GSM5209557': [0.0, 27.0, 1.0], 'GSM5209558': [0.0, 36.0, 1.0], 'GSM5209559': [0.0, 19.0, 0.0], 'GSM5209560': [0.0, 38.0, 1.0], 'GSM5209561': [1.0, 24.0, 1.0], 'GSM5209562': [0.0, 33.0, 1.0], 'GSM5209563': [0.0, 23.0, 0.0], 'GSM5209564': [0.0, 20.0, 0.0], 'GSM5209565': [1.0, 38.0, 1.0], 'GSM5209566': [0.0, 68.0, 0.0], 'GSM5209567': [0.0, 23.0, 1.0], 'GSM5209568': [1.0, 39.0, 1.0], 'GSM5209569': [1.0, 23.0, 1.0], 'GSM5209570': [1.0, 23.0, 0.0], 'GSM5209571': [0.0, 39.0, 1.0], 'GSM5209572': [0.0, 38.0, 0.0], 'GSM5209573': [0.0, 20.0, 0.0], 'GSM5209574': [1.0, 54.0, 1.0], 'GSM5209575': [0.0, 41.0, 1.0], 'GSM5209576': [0.0, 48.0, 0.0], 'GSM5209577': [0.0, 74.0, 1.0], 'GSM5209578': [0.0, 69.0, 0.0], 'GSM5209579': [0.0, 42.0, 0.0], 'GSM5209580': [1.0, 25.0, 1.0], 'GSM5209581': [0.0, 35.0, 1.0], 'GSM5209582': [1.0, 30.0, 1.0], 'GSM5209583': [1.0, 23.0, 0.0], 'GSM5209584': [0.0, 36.0, 0.0], 'GSM5209585': [0.0, 61.0, 1.0], 'GSM5209586': [0.0, 37.0, 1.0], 'GSM5209587': [0.0, 50.0, 1.0], 'GSM5209588': [0.0, 46.0, 0.0], 'GSM5209589': [0.0, 22.0, 1.0], 'GSM5209590': [0.0, 21.0, 0.0], 'GSM5209591': [0.0, 44.0, 0.0], 'GSM5209592': [0.0, 24.0, 0.0], 'GSM5209593': [0.0, 24.0, 1.0], 'GSM5209594': [0.0, 23.0, 0.0], 'GSM5209595': [0.0, 47.0, 0.0], 'GSM5209596': [0.0, 21.0, 0.0], 'GSM5209597': [0.0, 19.0, 0.0], 'GSM5209598': [0.0, 56.0, 0.0], 'GSM5209599': [0.0, 25.0, 1.0], 'GSM5209600': [0.0, 54.0, 1.0], 'GSM5209601': [0.0, 51.0, 1.0], 'GSM5209602': [0.0, 43.0, 0.0], 'GSM5209603': [0.0, 53.0, 0.0], 'GSM5209604': [0.0, 66.0, 1.0], 'GSM5209605': [0.0, 69.0, 1.0], 'GSM5209606': [0.0, 22.0, 0.0], 'GSM5209607': [0.0, 56.0, 0.0], 'GSM5209608': [0.0, 51.0, 1.0], 'GSM5209609': [0.0, 69.0, 1.0], 'GSM5209610': [0.0, 53.0, 0.0], 'GSM5209611': [0.0, 61.0, 1.0], 'GSM5209612': [0.0, 52.0, 1.0], 'GSM5209613': [0.0, 42.0, 0.0], 'GSM5209614': [0.0, 56.0, 1.0], 'GSM5209615': [1.0, 58.0, 0.0], 'GSM5209616': [1.0, 20.0, 0.0], 'GSM5209617': [1.0, 17.0, 1.0], 'GSM5209618': [0.0, 40.0, 0.0], 'GSM5209619': [1.0, 44.0, 1.0], 'GSM5209620': [0.0, 45.0, 0.0], 'GSM5209621': [1.0, 19.0, 1.0], 'GSM5209622': [0.0, 28.0, 0.0], 'GSM5209623': [0.0, 57.0, 0.0], 'GSM5209624': [1.0, 41.0, 0.0], 'GSM5209625': [0.0, 34.0, 0.0], 'GSM5209626': [0.0, 54.0, 0.0], 'GSM5209627': [1.0, 59.0, 1.0], 'GSM5209628': [0.0, 20.0, 1.0]}\n",
      "Clinical data shape: (3, 205)\n",
      "Clinical features saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE169568.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (205, 11042)\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (205, 11042)\n",
      "\n",
      "Checking for bias in feature variables:\n",
      "For the feature 'Crohns_Disease', the least common label is '1.0' with 52 occurrences. This represents 25.37% of the dataset.\n",
      "The distribution of the feature 'Crohns_Disease' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 24.0\n",
      "  50% (Median): 34.0\n",
      "  75%: 51.0\n",
      "Min: 17.0\n",
      "Max: 78.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '1.0' with 98 occurrences. This represents 47.80% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Crohns_Disease/GSE169568.csv\n",
      "Final dataset shape: (205, 11042)\n"
     ]
    }
   ],
   "source": [
    "# 1. Check if gene data is available after mapping\n",
    "if gene_data.shape[0] == 0:\n",
    "    print(\"Error: Gene expression matrix is empty after mapping.\")\n",
    "    # Mark the dataset as not usable due to lack of gene expression data\n",
    "    is_usable = validate_and_save_cohort_info(\n",
    "        is_final=True,\n",
    "        cohort=cohort,\n",
    "        info_path=json_path,\n",
    "        is_gene_available=False,  # No usable gene data\n",
    "        is_trait_available=True,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Failed to map probe IDs to gene symbols. The annotation format may not be compatible with the extraction methods.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of gene expression data.\")\n",
    "else:\n",
    "    # Only proceed with normalization if we have gene data\n",
    "    print(\"Normalizing gene symbols...\")\n",
    "    gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
    "\n",
    "    # Save the normalized gene data\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    gene_data_normalized.to_csv(out_gene_data_file)\n",
    "    print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
    "    \n",
    "    # Extract clinical features from the original data source\n",
    "    print(\"Extracting clinical features from the original source...\")\n",
    "    # Get background information and clinical data again\n",
    "    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "    background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    print(\"Extracted clinical features preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    \n",
    "    # Save the extracted clinical features\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    # Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # Check if the linked data has adequate data\n",
    "    if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4:  # 4 is an arbitrary small number\n",
    "        print(\"Error: Linked data has insufficient samples or features.\")\n",
    "        is_usable = validate_and_save_cohort_info(\n",
    "            is_final=True,\n",
    "            cohort=cohort,\n",
    "            info_path=json_path,\n",
    "            is_gene_available=True,\n",
    "            is_trait_available=True,\n",
    "            is_biased=True,\n",
    "            df=linked_data,\n",
    "            note=\"Failed to properly link gene expression data with clinical features.\"\n",
    "        )\n",
    "        print(\"Dataset deemed not usable due to linking failure.\")\n",
    "    else:\n",
    "        # Handle missing values systematically\n",
    "        print(\"Handling missing values...\")\n",
    "        linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "        \n",
    "        # Check if there are still samples after missing value handling\n",
    "        if linked_data_clean.shape[0] == 0:\n",
    "            print(\"Error: No samples remain after handling missing values.\")\n",
    "            is_usable = validate_and_save_cohort_info(\n",
    "                is_final=True,\n",
    "                cohort=cohort,\n",
    "                info_path=json_path,\n",
    "                is_gene_available=True,\n",
    "                is_trait_available=True,\n",
    "                is_biased=True,\n",
    "                df=pd.DataFrame(),\n",
    "                note=\"All samples were removed during missing value handling.\"\n",
    "            )\n",
    "            print(\"Dataset deemed not usable as all samples were filtered out.\")\n",
    "        else:\n",
    "            # Check if the dataset is biased\n",
    "            print(\"\\nChecking for bias in feature variables:\")\n",
    "            is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "            \n",
    "            # Conduct final quality validation\n",
    "            is_usable = validate_and_save_cohort_info(\n",
    "                is_final=True,\n",
    "                cohort=cohort,\n",
    "                info_path=json_path,\n",
    "                is_gene_available=True,\n",
    "                is_trait_available=True,\n",
    "                is_biased=is_biased,\n",
    "                df=linked_data_final,\n",
    "                note=\"Dataset contains gene expression data for Crohn's Disease patients, examining response to Infliximab treatment.\"\n",
    "            )\n",
    "            \n",
    "            # Save linked data if usable\n",
    "            if is_usable:\n",
    "                os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "                linked_data_final.to_csv(out_data_file)\n",
    "                print(f\"Linked data saved to {out_data_file}\")\n",
    "                print(f\"Final dataset shape: {linked_data_final.shape}\")\n",
    "            else:\n",
    "                print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")"
   ]
  }
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