{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "947e4742",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:50:06.647173Z",
     "iopub.status.busy": "2025-03-25T05:50:06.647069Z",
     "iopub.status.idle": "2025-03-25T05:50:06.812891Z",
     "shell.execute_reply": "2025-03-25T05:50:06.812564Z"
    }
   },
   "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 = \"Hypertrophic_Cardiomyopathy\"\n",
    "cohort = \"GSE36961\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Hypertrophic_Cardiomyopathy\"\n",
    "in_cohort_dir = \"../../input/GEO/Hypertrophic_Cardiomyopathy/GSE36961\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv\"\n",
    "json_path = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "586a8fe0",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0cbfa276",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:50:06.814252Z",
     "iopub.status.busy": "2025-03-25T05:50:06.814103Z",
     "iopub.status.idle": "2025-03-25T05:50:07.136772Z",
     "shell.execute_reply": "2025-03-25T05:50:07.136402Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptome Profiling of Surgical Myectomy Tissue from Patients with Hypertrophic Cardiomyopathy\"\n",
      "!Series_summary\t\"Using a high-throughput gene expression profiling technology, we have been able to develop new hypotheses regarding the molecular pathogenic mechanisms of human hypertrophic cardiomyopathy (HCM). It is hoped that these hypotheses, among others generated by this data, will fuel future research endeavors that will uncover novel biomarkers, prognostic indicators, and therapeutic targets to improve our ability to diagnose, counsel, and treat patients with this highly heterogeneous and potentially life-threatening condition.\"\n",
      "!Series_overall_design\t\"Case-control study comparing the messenger RNA transcriptome of cardiac tissues from patients with hypertrophic cardiomyopathy to the transcriptome of control donor cardiac tissues.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['Sex: male', 'Sex: female'], 1: ['age (yrs): 9', 'age (yrs): 10', 'age (yrs): 11', 'age (yrs): 13', 'age (yrs): 14', 'age (yrs): 15', 'age (yrs): 16', 'age (yrs): 17', 'age (yrs): 19', 'age (yrs): 20', 'age (yrs): 23', 'age (yrs): 26', 'age (yrs): 27', 'age (yrs): 28', 'age (yrs): 30', 'age (yrs): 31', 'age (yrs): 32', 'age (yrs): 33', 'age (yrs): 35', 'age (yrs): 37', 'age (yrs): 38', 'age (yrs): 41', 'age (yrs): 43', 'age (yrs): 44', 'age (yrs): 45', 'age (yrs): 46', 'age (yrs): 47', 'age (yrs): 48', 'age (yrs): 50', 'age (yrs): 51'], 2: ['tissue: cardiac', 'sample type: control'], 3: ['disease state: hypertrophic cardiomyopathy (HCM)', nan, 'sample type: control'], 4: ['sample type: case', nan]}\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": "361dc928",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0d7a138",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:50:07.138094Z",
     "iopub.status.busy": "2025-03-25T05:50:07.137976Z",
     "iopub.status.idle": "2025-03-25T05:50:07.158319Z",
     "shell.execute_reply": "2025-03-25T05:50:07.157990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Data Preview:\n",
      "{'GSM907203': [1.0, 9.0, 1.0], 'GSM907204': [1.0, 10.0, 1.0], 'GSM907205': [1.0, 10.0, 0.0], 'GSM907206': [1.0, 11.0, 1.0], 'GSM907207': [1.0, 13.0, 0.0], 'GSM907208': [1.0, 14.0, 1.0], 'GSM907209': [1.0, 15.0, 1.0], 'GSM907210': [1.0, 15.0, 0.0], 'GSM907211': [1.0, 15.0, 1.0], 'GSM907212': [1.0, 15.0, 1.0], 'GSM907213': [1.0, 16.0, 0.0], 'GSM907214': [1.0, 16.0, 1.0], 'GSM907215': [1.0, 17.0, 0.0], 'GSM907216': [1.0, 19.0, 1.0], 'GSM907217': [1.0, 19.0, 1.0], 'GSM907218': [1.0, 20.0, 0.0], 'GSM907219': [1.0, 23.0, 1.0], 'GSM907220': [1.0, 23.0, 0.0], 'GSM907221': [1.0, 26.0, 1.0], 'GSM907222': [1.0, 27.0, 1.0], 'GSM907223': [1.0, 28.0, 1.0], 'GSM907224': [1.0, 30.0, 1.0], 'GSM907225': [1.0, 30.0, 0.0], 'GSM907226': [1.0, 30.0, 0.0], 'GSM907227': [1.0, 31.0, 1.0], 'GSM907228': [1.0, 32.0, 0.0], 'GSM907229': [1.0, 32.0, 0.0], 'GSM907230': [1.0, 33.0, 0.0], 'GSM907231': [1.0, 35.0, 0.0], 'GSM907232': [1.0, 35.0, 0.0], 'GSM907233': [1.0, 37.0, 0.0], 'GSM907234': [1.0, 37.0, 1.0], 'GSM907235': [1.0, 38.0, 1.0], 'GSM907236': [1.0, 38.0, 0.0], 'GSM907237': [1.0, 41.0, 1.0], 'GSM907238': [1.0, 43.0, 0.0], 'GSM907239': [1.0, 43.0, 1.0], 'GSM907240': [1.0, 43.0, 1.0], 'GSM907241': [1.0, 43.0, 1.0], 'GSM907242': [1.0, 44.0, 0.0], 'GSM907243': [1.0, 44.0, 0.0], 'GSM907244': [1.0, 44.0, 1.0], 'GSM907245': [1.0, 45.0, 0.0], 'GSM907246': [1.0, 45.0, 1.0], 'GSM907247': [1.0, 45.0, 1.0], 'GSM907248': [1.0, 45.0, 1.0], 'GSM907249': [1.0, 46.0, 1.0], 'GSM907250': [1.0, 46.0, 0.0], 'GSM907251': [1.0, 47.0, 1.0], 'GSM907252': [1.0, 48.0, 1.0], 'GSM907253': [1.0, 48.0, 0.0], 'GSM907254': [1.0, 50.0, 1.0], 'GSM907255': [1.0, 50.0, 0.0], 'GSM907256': [1.0, 51.0, 0.0], 'GSM907257': [1.0, 51.0, 0.0], 'GSM907258': [1.0, 51.0, 0.0], 'GSM907259': [1.0, 52.0, 0.0], 'GSM907260': [1.0, 52.0, 1.0], 'GSM907261': [1.0, 52.0, 0.0], 'GSM907262': [1.0, 52.0, 1.0], 'GSM907263': [1.0, 53.0, 0.0], 'GSM907264': [1.0, 53.0, 1.0], 'GSM907265': [1.0, 54.0, 0.0], 'GSM907266': [1.0, 54.0, 0.0], 'GSM907267': [1.0, 54.0, 1.0], 'GSM907268': [1.0, 55.0, 0.0], 'GSM907269': [1.0, 56.0, 0.0], 'GSM907270': [1.0, 56.0, 1.0], 'GSM907271': [1.0, 56.0, 0.0], 'GSM907272': [1.0, 56.0, 1.0], 'GSM907273': [1.0, 57.0, 1.0], 'GSM907274': [1.0, 58.0, 0.0], 'GSM907275': [1.0, 58.0, 1.0], 'GSM907276': [1.0, 59.0, 1.0], 'GSM907277': [1.0, 59.0, 1.0], 'GSM907278': [1.0, 59.0, 1.0], 'GSM907279': [1.0, 59.0, 0.0], 'GSM907280': [1.0, 59.0, 1.0], 'GSM907281': [1.0, 59.0, 1.0], 'GSM907282': [1.0, 60.0, 0.0], 'GSM907283': [1.0, 60.0, 1.0], 'GSM907284': [1.0, 62.0, 1.0], 'GSM907285': [1.0, 63.0, 1.0], 'GSM907286': [1.0, 64.0, 0.0], 'GSM907287': [1.0, 65.0, 1.0], 'GSM907288': [1.0, 65.0, 1.0], 'GSM907289': [1.0, 66.0, 0.0], 'GSM907290': [1.0, 67.0, 0.0], 'GSM907291': [1.0, 67.0, 0.0], 'GSM907292': [1.0, 67.0, 0.0], 'GSM907293': [1.0, 67.0, 1.0], 'GSM907294': [1.0, 67.0, 1.0], 'GSM907295': [1.0, 67.0, 1.0], 'GSM907296': [1.0, 69.0, 0.0], 'GSM907297': [1.0, 69.0, 0.0], 'GSM907298': [1.0, 70.0, 0.0], 'GSM907299': [1.0, 70.0, 0.0], 'GSM907300': [1.0, 71.0, 0.0], 'GSM907301': [1.0, 71.0, 0.0], 'GSM907302': [1.0, 71.0, 0.0], 'GSM907303': [1.0, 73.0, 0.0], 'GSM907304': [1.0, 73.0, 1.0], 'GSM907305': [1.0, 75.0, 1.0], 'GSM907306': [1.0, 76.0, 1.0], 'GSM907307': [1.0, 77.0, 0.0], 'GSM907308': [1.0, 78.0, 0.0], 'GSM907309': [nan, nan, 0.0], 'GSM907310': [0.0, 49.0, 0.0], 'GSM907311': [0.0, 48.0, 0.0], 'GSM907312': [nan, nan, 0.0], 'GSM907313': [0.0, 42.0, 0.0], 'GSM907314': [0.0, 53.0, 0.0], 'GSM907315': [nan, nan, 0.0], 'GSM907316': [0.0, 31.0, 0.0], 'GSM907317': [0.0, 54.0, 1.0], 'GSM907318': [0.0, 52.0, 1.0], 'GSM907319': [0.0, 47.0, 1.0], 'GSM907320': [0.0, 26.0, 1.0], 'GSM907321': [0.0, 65.0, 0.0], 'GSM907322': [0.0, 21.0, 1.0], 'GSM907323': [0.0, 41.0, 1.0], 'GSM907324': [0.0, 55.0, 1.0], 'GSM907325': [0.0, 61.0, 1.0], 'GSM907326': [0.0, 36.0, 0.0], 'GSM907327': [0.0, 7.0, 1.0], 'GSM907328': [0.0, 23.0, 1.0], 'GSM907329': [0.0, 17.0, 1.0], 'GSM907330': [0.0, 45.0, 0.0], 'GSM907331': [0.0, 40.0, 0.0], 'GSM907332': [0.0, 37.0, 0.0], 'GSM907333': [0.0, 51.0, 0.0], 'GSM907334': [0.0, 39.0, 1.0], 'GSM907335': [0.0, 37.0, 0.0], 'GSM907336': [0.0, 23.0, 1.0], 'GSM907337': [0.0, 19.0, 1.0], 'GSM907338': [0.0, 53.0, 0.0], 'GSM907339': [0.0, 48.0, 0.0], 'GSM907340': [0.0, 47.0, 0.0], 'GSM907341': [0.0, 4.0, 1.0], 'GSM907342': [0.0, 48.0, 0.0], 'GSM907343': [0.0, 25.0, 0.0], 'GSM907344': [0.0, 27.0, 1.0], 'GSM907345': [0.0, 21.0, 1.0], 'GSM907346': [0.0, 27.0, 1.0], 'GSM907347': [0.0, 21.0, 1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv\n"
     ]
    }
   ],
   "source": [
    "# Check if gene expression data is available\n",
    "# The background information mentions \"messenger RNA transcriptome\" comparing HCM to control samples\n",
    "# This suggests gene expression data is available.\n",
    "is_gene_available = True\n",
    "\n",
    "# Define conversion functions for clinical variables\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0=control, 1=HCM)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "        \n",
    "    value = value.lower() if isinstance(value, str) else str(value).lower()\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"hypertrophic cardiomyopathy\" in value or \"hcm\" in value or \"case\" in value:\n",
    "        return 1\n",
    "    elif \"control\" in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "        \n",
    "    value = str(value)\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Extract numeric age\n",
    "    match = re.search(r'(\\d+)', value)\n",
    "    if match:\n",
    "        return int(match.group(1))\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0=female, 1=male)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "        \n",
    "    value = value.lower() if isinstance(value, str) else str(value).lower()\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"female\" in value:\n",
    "        return 0\n",
    "    elif \"male\" in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Identify rows with trait, age, and gender information\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Looking at the sample characteristics dictionary\n",
    "# Row 0 contains gender information (\"Sex: male\", \"Sex: female\")\n",
    "gender_row = 0\n",
    "\n",
    "# Row 1 contains age information with format \"age (yrs): XX\"\n",
    "age_row = 1\n",
    "\n",
    "# Row 3 contains disease state information about HCM vs control\n",
    "trait_row = 3\n",
    "\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save metadata with initial filtering\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",
    "# Extract clinical features if trait data is available\n",
    "if trait_row is not None:\n",
    "    # Use geo_select_clinical_features to extract clinical features\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 clinical dataframe\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to CSV\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": "2bad2705",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59ba117c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:50:07.159515Z",
     "iopub.status.busy": "2025-03-25T05:50:07.159407Z",
     "iopub.status.idle": "2025-03-25T05:50:07.730381Z",
     "shell.execute_reply": "2025-03-25T05:50:07.729988Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting gene data from matrix file:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully extracted gene data with 37846 rows\n",
      "First 20 gene IDs:\n",
      "Index(['7A5', 'A1BG', 'A1CF', 'A26A1', 'A26B1', 'A26C1B', 'A26C3', 'A2BP1',\n",
      "       'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS',\n",
      "       'AACSL', 'AADAC', 'AADACL1', 'AADACL2'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene expression data available: True\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the file paths for the SOFT file and matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract gene expression data from the matrix file\n",
    "try:\n",
    "    print(\"Extracting gene data from matrix file:\")\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    if gene_data.empty:\n",
    "        print(\"Extracted gene expression data is empty\")\n",
    "        is_gene_available = False\n",
    "    else:\n",
    "        print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
    "        print(\"First 20 gene IDs:\")\n",
    "        print(gene_data.index[:20])\n",
    "        is_gene_available = True\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
    "    is_gene_available = False\n",
    "\n",
    "print(f\"\\nGene expression data available: {is_gene_available}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf8954c",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "972e19e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:50:07.731762Z",
     "iopub.status.busy": "2025-03-25T05:50:07.731636Z",
     "iopub.status.idle": "2025-03-25T05:50:07.733744Z",
     "shell.execute_reply": "2025-03-25T05:50:07.733419Z"
    }
   },
   "outputs": [],
   "source": [
    "# Review the gene identifiers in the gene expression data\n",
    "\n",
    "# The gene identifiers in the dataset appear to be standard human gene symbols.\n",
    "# Examples like A1BG, AAAS, AACS are recognized human gene symbols.\n",
    "# These are official HUGO Gene Nomenclature Committee (HGNC) symbols\n",
    "# and do not require additional mapping to be used in analysis.\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823862bc",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a658f2b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:50:07.734915Z",
     "iopub.status.busy": "2025-03-25T05:50:07.734805Z",
     "iopub.status.idle": "2025-03-25T05:50:23.468125Z",
     "shell.execute_reply": "2025-03-25T05:50:23.467237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape before normalization: (37846, 145)\n",
      "Gene data shape after normalization: (18660, 145)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv\n",
      "Clinical data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv\n",
      "Linked data shape: (145, 18663)\n",
      "\n",
      "Handling missing values...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After missing value handling, linked data shape: (142, 18663)\n",
      "\n",
      "Evaluating feature bias...\n",
      "For the feature 'Hypertrophic_Cardiomyopathy', the least common label is '0.0' with 36 occurrences. This represents 25.35% of the dataset.\n",
      "The distribution of the feature 'Hypertrophic_Cardiomyopathy' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 30.0\n",
      "  50% (Median): 47.0\n",
      "  75%: 58.0\n",
      "Min: 4.0\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 '0.0' with 69 occurrences. This represents 48.59% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is fine.\n",
      "\n",
      "Trait bias evaluation result: False\n",
      "A new JSON file was created at: ../../output/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json\n",
      "\n",
      "Dataset usability: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols and extract from step 3 and 6\n",
    "# Load the gene expression data (already loaded from Step 6)\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "\n",
    "try:\n",
    "    # Normalize gene symbols using the NCBI Gene database information\n",
    "    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "    \n",
    "    if normalized_gene_data.empty:\n",
    "        print(\"Normalization resulted in empty dataframe. Using original gene data instead.\")\n",
    "        normalized_gene_data = gene_data\n",
    "    \n",
    "    print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "    \n",
    "    # Save the normalized gene data to the output file\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    normalized_gene_data.to_csv(out_gene_data_file)\n",
    "    print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "except Exception as e:\n",
    "    print(f\"Error normalizing gene data: {e}. Using original gene data instead.\")\n",
    "    normalized_gene_data = gene_data\n",
    "    # Save the original gene data if normalization fails\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    normalized_gene_data.to_csv(out_gene_data_file)\n",
    "\n",
    "# 2. Link clinical and genetic data\n",
    "# Use the trait_row identified in Step 2 (trait_row = 1) to extract trait data\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "if is_trait_available:\n",
    "    # Extract clinical features using the function and conversion methods from Step 2\n",
    "    clinical_features = 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",
    "    # Save clinical features\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_features.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    # Link clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "else:\n",
    "    # Create a minimal dataframe with just the trait column\n",
    "    linked_data = pd.DataFrame({trait: [np.nan]})\n",
    "    print(\"No trait data available, creating minimal dataframe for validation.\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "if is_trait_available:\n",
    "    print(\"\\nHandling missing values...\")\n",
    "    linked_data = handle_missing_values(linked_data, trait)\n",
    "    print(f\"After missing value handling, linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether trait and demographic features are biased\n",
    "if is_trait_available and not linked_data.empty and len(linked_data.columns) > 1:\n",
    "    print(\"\\nEvaluating feature bias...\")\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "    print(f\"Trait bias evaluation result: {is_biased}\")\n",
    "else:\n",
    "    is_biased = False\n",
    "    print(\"Skipping bias evaluation due to insufficient data.\")\n",
    "\n",
    "# 5. Final validation and save metadata\n",
    "note = \"\"\n",
    "if not is_trait_available:\n",
    "    note = f\"Dataset contains gene expression data but no {trait} measurements.\"\n",
    "elif is_biased:\n",
    "    note = f\"Dataset contains {trait} data but its distribution is severely biased.\"\n",
    "\n",
    "# Validate and save cohort info\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=is_gene_available, \n",
    "    is_trait_available=is_trait_available, \n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if usable\n",
    "print(f\"\\nDataset usability: {is_usable}\")\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(f\"Dataset is not usable for {trait} association studies. Data not saved.\")"
   ]
  }
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