{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "521f0a75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:51.697314Z",
     "iopub.status.busy": "2025-03-25T05:09:51.697206Z",
     "iopub.status.idle": "2025-03-25T05:09:51.867318Z",
     "shell.execute_reply": "2025-03-25T05:09:51.866954Z"
    }
   },
   "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 = \"Epilepsy\"\n",
    "cohort = \"GSE64123\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Epilepsy\"\n",
    "in_cohort_dir = \"../../input/GEO/Epilepsy/GSE64123\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Epilepsy/GSE64123.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Epilepsy/gene_data/GSE64123.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Epilepsy/clinical_data/GSE64123.csv\"\n",
    "json_path = \"../../output/preprocess/Epilepsy/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab49d76",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "614db7bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:51.868824Z",
     "iopub.status.busy": "2025-03-25T05:09:51.868674Z",
     "iopub.status.idle": "2025-03-25T05:09:51.991008Z",
     "shell.execute_reply": "2025-03-25T05:09:51.990645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Human embryonic stem cell based neuro-developmental toxicity assay: response to valproic acid and carbamazepine exposure\"\n",
      "!Series_summary\t\"Here we studied the effects of anticonvulsant drug exposure in a human embryonic stem cell (hESC) based neuro- developmental toxicity test (hESTn). During neural differentiation the cells were exposed, for either 1 or 7 days, to non-cytotoxic concentration ranges of valproic acid (VPA) or carbamazepine (CBZ), anti-epileptic drugs known to cause neurodevelopmental toxicity.\"\n",
      "!Series_overall_design\t\"93 samples (multiple time points, multiple exposures, multiple concentrations, multiple replicates)\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['time: 0 days', 'time: 1 days', 'time: 4 days', 'time: 7 days', 'time: 9 days', 'time: 11 days'], 1: ['exposure: unexposed', 'exposure: DMSO', 'exposure: carbamazepine', 'exposure: valproic acid'], 2: ['concentration: 0 mM', 'concentration: 0.25%', 'concentration: 0.033 mM', 'concentration: 0.1 mM', 'concentration: 0.33 mM', 'concentration: 1 mM']}\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": "3c808071",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b12b20b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:51.992234Z",
     "iopub.status.busy": "2025-03-25T05:09:51.992117Z",
     "iopub.status.idle": "2025-03-25T05:09:51.999685Z",
     "shell.execute_reply": "2025-03-25T05:09:51.999434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "import json\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset appears to be about gene expression during neural differentiation\n",
    "# and the effects of drug exposure, so it likely contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Looking at sample characteristics dictionary, we don't find direct trait (epilepsy) information\n",
    "# The dataset is about effects of anticonvulsant drugs on neural development, not patients with epilepsy\n",
    "trait_row = None  # No epilepsy trait data available\n",
    "\n",
    "# Age data is not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender data is not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert epilepsy trait value to binary (0/1)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.lower().strip()\n",
    "        \n",
    "    if value in ['yes', 'epilepsy', 'epileptic', 'seizure disorder', 'true', '1']:\n",
    "        return 1\n",
    "    elif value in ['no', 'control', 'healthy', 'normal', 'false', '0']:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to continuous numeric value\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \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: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender value to binary (0=female, 1=male)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.lower().strip()\n",
    "    \n",
    "    if value in ['female', 'f', 'woman', 'girl']:\n",
    "        return 0\n",
    "    elif value in ['male', 'm', 'man', 'boy']:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info\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",
    "# Skip this step as trait_row is None (no clinical data available for our specific trait of interest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc66cc7c",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46505f3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:52.000839Z",
     "iopub.status.busy": "2025-03-25T05:09:52.000732Z",
     "iopub.status.idle": "2025-03-25T05:09:52.213347Z",
     "shell.execute_reply": "2025-03-25T05:09:52.213015Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOFT file: ../../input/GEO/Epilepsy/GSE64123/GSE64123_family.soft.gz\n",
      "Matrix file: ../../input/GEO/Epilepsy/GSE64123/GSE64123_series_matrix.txt.gz\n",
      "Found the matrix table marker in the file.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (18909, 93)\n",
      "First 20 gene/probe identifiers:\n",
      "['100009676_at', '10000_at', '10001_at', '10002_at', '10003_at', '100048912_at', '100049716_at', '10004_at', '10005_at', '10006_at', '10007_at', '10008_at', '100093630_at', '10009_at', '1000_at', '100101467_at', '100101938_at', '10010_at', '100113407_at', '10011_at']\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# Set gene availability flag\n",
    "is_gene_available = True  # Initially assume gene data is available\n",
    "\n",
    "# First check if the matrix file contains the expected marker\n",
    "found_marker = False\n",
    "try:\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        for line in file:\n",
    "            if \"!series_matrix_table_begin\" in line:\n",
    "                found_marker = True\n",
    "                break\n",
    "    \n",
    "    if found_marker:\n",
    "        print(\"Found the matrix table marker in the file.\")\n",
    "    else:\n",
    "        print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
    "        \n",
    "    # Try to extract gene data from the matrix file\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    \n",
    "    if gene_data.shape[0] == 0:\n",
    "        print(\"Warning: Extracted gene data has 0 rows.\")\n",
    "        is_gene_available = False\n",
    "    else:\n",
    "        print(f\"Gene data shape: {gene_data.shape}\")\n",
    "        # Print the first 20 gene/probe identifiers\n",
    "        print(\"First 20 gene/probe identifiers:\")\n",
    "        print(gene_data.index[:20].tolist())\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    is_gene_available = False\n",
    "    \n",
    "    # Try to diagnose the file format\n",
    "    print(\"Examining file content to diagnose the issue:\")\n",
    "    try:\n",
    "        with gzip.open(matrix_file, 'rt') as file:\n",
    "            for i, line in enumerate(file):\n",
    "                if i < 10:  # Print first 10 lines to diagnose\n",
    "                    print(f\"Line {i}: {line.strip()[:100]}...\")  # Print first 100 chars of each line\n",
    "                else:\n",
    "                    break\n",
    "    except Exception as e2:\n",
    "        print(f\"Error examining file: {e2}\")\n",
    "\n",
    "if not is_gene_available:\n",
    "    print(\"Gene expression data could not be successfully extracted from this dataset.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef043f5",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9e1e498",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:52.214553Z",
     "iopub.status.busy": "2025-03-25T05:09:52.214426Z",
     "iopub.status.idle": "2025-03-25T05:09:52.216377Z",
     "shell.execute_reply": "2025-03-25T05:09:52.216086Z"
    }
   },
   "outputs": [],
   "source": [
    "# Analyzing the gene identifiers in the provided list\n",
    "# The format \"100009676_at\" suggests these are Affymetrix microarray probe set IDs\n",
    "# These are not standard human gene symbols and need to be mapped to gene symbols\n",
    "# Affymetrix IDs typically end with \"_at\" and need conversion to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07167694",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f6add7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:52.217408Z",
     "iopub.status.busy": "2025-03-25T05:09:52.217297Z",
     "iopub.status.idle": "2025-03-25T05:09:54.309458Z",
     "shell.execute_reply": "2025-03-25T05:09:54.309101Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'SPOT_ID', 'Description']\n",
      "{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'SPOT_ID': ['1', '10', '100', '1000', '10000'], 'Description': ['alpha-1-B glycoprotein', 'N-acetyltransferase 2 (arylamine N-acetyltransferase)', 'adenosine deaminase', 'cadherin 2, type 1, N-cadherin (neuronal)', 'v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)']}\n",
      "\n",
      "Sample of Description column (first 5 rows):\n",
      "Row 0: alpha-1-B glycoprotein\n",
      "Row 1: N-acetyltransferase 2 (arylamine N-acetyltransferase)\n",
      "Row 2: adenosine deaminase\n",
      "Row 3: cadherin 2, type 1, N-cadherin (neuronal)\n",
      "Row 4: v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Based on the preview, 'ID' appears to be the probe ID and 'Description' contains gene names\n",
    "# Display more samples from the Description column to better understand the format\n",
    "print(\"\\nSample of Description column (first 5 rows):\")\n",
    "if 'Description' in gene_annotation.columns:\n",
    "    for i in range(min(5, len(gene_annotation))):\n",
    "        print(f\"Row {i}: {gene_annotation['Description'].iloc[i]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcf009c",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3022573",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:54.310782Z",
     "iopub.status.busy": "2025-03-25T05:09:54.310644Z",
     "iopub.status.idle": "2025-03-25T05:09:54.622213Z",
     "shell.execute_reply": "2025-03-25T05:09:54.621872Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example probe IDs in gene expression data: ['100009676_at', '10000_at', '10001_at', '10002_at', '10003_at']\n",
      "Example IDs in annotation data: ['1_at', '10_at', '100_at', '1000_at', '10000_at']\n",
      "Gene mapping preview:\n",
      "{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'Gene': ['alpha-1-B glycoprotein', 'N-acetyltransferase 2 (arylamine N-acetyltransferase)', 'adenosine deaminase', 'cadherin 2, type 1, N-cadherin (neuronal)', 'v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)']}\n",
      "Shape of gene mapping dataframe: (18876, 2)\n",
      "Number of probes in gene expression data that can be mapped: 18876\n",
      "Gene expression data after mapping:\n",
      "Shape of gene expression data: (2024, 93)\n",
      "First few gene symbols:\n",
      "['A-', 'A-2', 'A-52', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1', 'A10']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene expression data after normalizing gene symbols:\n",
      "Shape of gene expression data: (1168, 93)\n",
      "First few normalized gene symbols:\n",
      "['A1BG', 'A4GALT', 'AAA1', 'ABCC11', 'ABCD1', 'ABCE1', 'ABI3', 'ABO', 'ACSM3', 'ADAT2']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Epilepsy/gene_data/GSE64123.csv\n"
     ]
    }
   ],
   "source": [
    "# Analyze the identifiers in gene annotation and gene expression data\n",
    "print(\"Example probe IDs in gene expression data:\", gene_data.index[:5].tolist())\n",
    "print(\"Example IDs in annotation data:\", gene_annotation['ID'][:5].tolist())\n",
    "\n",
    "# 1. Determine which columns to use for mapping\n",
    "# The gene annotation 'ID' column contains probe IDs like \"1_at\"\n",
    "# The gene expression data index contains probes like \"100009676_at\"\n",
    "# The 'Description' column contains gene symbols/descriptions\n",
    "\n",
    "# 2. Create the gene mapping dataframe\n",
    "# Extract the relevant columns for mapping\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping, n=5))\n",
    "print(f\"Shape of gene mapping dataframe: {gene_mapping.shape}\")\n",
    "\n",
    "# Count how many probes can be mapped to the gene expression data\n",
    "common_probes = set(gene_data.index).intersection(set(gene_mapping['ID']))\n",
    "print(f\"Number of probes in gene expression data that can be mapped: {len(common_probes)}\")\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene-level expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(\"Gene expression data after mapping:\")\n",
    "print(f\"Shape of gene expression data: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10].tolist())\n",
    "\n",
    "# Apply standardization to gene symbols\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(\"\\nGene expression data after normalizing gene symbols:\")\n",
    "print(f\"Shape of gene expression data: {gene_data.shape}\")\n",
    "print(\"First few normalized gene symbols:\")\n",
    "print(gene_data.index[:10].tolist())\n",
    "\n",
    "# Save the processed gene expression data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde555d2",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "439de11b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:09:54.623604Z",
     "iopub.status.busy": "2025-03-25T05:09:54.623477Z",
     "iopub.status.idle": "2025-03-25T05:09:54.758215Z",
     "shell.execute_reply": "2025-03-25T05:09:54.757865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape before normalization: (1168, 93)\n",
      "Gene data shape after normalization: (1168, 93)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Epilepsy/gene_data/GSE64123.csv\n",
      "No trait data (Epilepsy) available in this dataset based on previous analysis.\n",
      "Cannot proceed with data linking due to missing trait or gene data.\n",
      "Abnormality detected in the cohort: GSE64123. Preprocessing failed.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "try:\n",
    "    # Make sure the directory exists\n",
    "    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "    \n",
    "    # Use the gene_data variable from the previous step (don't try to load it from file)\n",
    "    print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "    \n",
    "    # Apply normalization to gene symbols\n",
    "    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "    \n",
    "    # Save the normalized gene data\n",
    "    normalized_gene_data.to_csv(out_gene_data_file)\n",
    "    print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "    \n",
    "    # Use the normalized data for further processing\n",
    "    gene_data = normalized_gene_data\n",
    "    is_gene_available = True\n",
    "except Exception as e:\n",
    "    print(f\"Error normalizing gene data: {e}\")\n",
    "    is_gene_available = False\n",
    "\n",
    "# 2. Load clinical data - respecting the analysis from Step 2\n",
    "# From Step 2, we determined:\n",
    "# trait_row = None  # No Epilepsy data available\n",
    "# age_row = None\n",
    "# gender_row = None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Skip clinical feature extraction when trait_row is None\n",
    "if is_trait_available:\n",
    "    try:\n",
    "        # Load the clinical data from file\n",
    "        soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "        \n",
    "        # Extract clinical features\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",
    "            gender_row=gender_row,\n",
    "            convert_gender=convert_gender,\n",
    "            age_row=age_row,\n",
    "            convert_age=convert_age\n",
    "        )\n",
    "        \n",
    "        print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n",
    "        print(\"Preview of clinical data (first 5 samples):\")\n",
    "        print(clinical_features.iloc[:, :5])\n",
    "        \n",
    "        # Save the properly extracted clinical data\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",
    "    except Exception as e:\n",
    "        print(f\"Error extracting clinical data: {e}\")\n",
    "        is_trait_available = False\n",
    "else:\n",
    "    print(\"No trait data (Epilepsy) available in this dataset based on previous analysis.\")\n",
    "\n",
    "# 3. Link clinical and genetic data if both are available\n",
    "if is_trait_available and is_gene_available:\n",
    "    try:\n",
    "        # Debug the column names to ensure they match\n",
    "        print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n",
    "        print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n",
    "        \n",
    "        # Check for common sample IDs\n",
    "        common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n",
    "        print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n",
    "        \n",
    "        if len(common_samples) > 0:\n",
    "            # Link the clinical and genetic data\n",
    "            linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
    "            print(f\"Initial linked data shape: {linked_data.shape}\")\n",
    "            \n",
    "            # Debug the trait values before handling missing values\n",
    "            print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n",
    "            print(linked_data.iloc[:5, :5])\n",
    "            \n",
    "            # Handle missing values\n",
    "            linked_data = handle_missing_values(linked_data, trait)\n",
    "            print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "            \n",
    "            if linked_data.shape[0] > 0:\n",
    "                # Check for bias in trait and demographic features\n",
    "                is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "                \n",
    "                # Validate the data quality and save cohort info\n",
    "                note = \"Dataset contains gene expression data from GBM cell cultures, but no epilepsy phenotype information.\"\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",
    "                # Save the linked data if it's 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(\"Data not usable for the trait study - not saving final linked data.\")\n",
    "            else:\n",
    "                print(\"After handling missing values, no samples remain.\")\n",
    "                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=True,\n",
    "                    df=pd.DataFrame(),\n",
    "                    note=\"No valid samples after handling missing values.\"\n",
    "                )\n",
    "        else:\n",
    "            print(\"No common samples found between gene expression and clinical data.\")\n",
    "            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=True,\n",
    "                df=pd.DataFrame(),\n",
    "                note=\"No common samples between gene expression and clinical data.\"\n",
    "            )\n",
    "    except Exception as e:\n",
    "        print(f\"Error linking or processing data: {e}\")\n",
    "        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=True,  # Assume biased if there's an error\n",
    "            df=pd.DataFrame(),  # Empty dataframe for metadata\n",
    "            note=f\"Error in data processing: {str(e)}\"\n",
    "        )\n",
    "else:\n",
    "    # Create an empty DataFrame for metadata purposes\n",
    "    empty_df = pd.DataFrame()\n",
    "    \n",
    "    # We can't proceed with linking if either trait or gene data is missing\n",
    "    print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n",
    "    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=True,  # Data is unusable if we're missing components\n",
    "        df=empty_df,  # Empty dataframe for metadata\n",
    "        note=\"Missing essential data components for linking: dataset contains gene expression data from GBM cell cultures, but no epilepsy phenotype information.\"\n",
    "    )"
   ]
  }
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