{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5515400",
   "metadata": {},
   "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 = \"Colon_and_Rectal_Cancer\"\n",
    "cohort = \"GSE46862\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Colon_and_Rectal_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Colon_and_Rectal_Cancer/GSE46862\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/GSE46862.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE46862.csv\"\n",
    "json_path = \"../../output/preprocess/Colon_and_Rectal_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "371239d6",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d92dcc2d",
   "metadata": {},
   "outputs": [],
   "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": "89877816",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "119eae27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyzing data availability and creating conversion functions\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this study used Affymetrix GenChip arrays\n",
    "# for gene expression profiling, so gene expression data should be available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics dictionary:\n",
    "# - trait (chemoradiation therapy response) is in row 0\n",
    "# - age is in row 1\n",
    "# - gender (Sex) is in row 2\n",
    "trait_row = 0\n",
    "age_row = 1\n",
    "gender_row = 2\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert chemoradiation therapy response to binary (1 for good response, 0 for poor response)\"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    response = value.split(':', 1)[1].strip()\n",
    "    # Based on the description, NT and TO are better responses, MI and MO are worse responses\n",
    "    if response == 'NT' or response == 'TO':\n",
    "        return 1  # good response\n",
    "    elif response == 'MI' or response == 'MO':\n",
    "        return 0  # poor response\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous numeric value\"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        age = int(value.split(':', 1)[1].strip())\n",
    "        return age\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    gender = value.split(':', 1)[1].strip().lower()\n",
    "    if gender == 'male':\n",
    "        return 1\n",
    "    elif gender == 'female':\n",
    "        return 0\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",
    "# 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",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Sample characteristics dictionary from previous step\n",
    "    sample_chars = {\n",
    "        0: ['chemoradiation therapy response: MO', 'chemoradiation therapy response: TO', 'chemoradiation therapy response: MI', 'chemoradiation therapy response: NT'],\n",
    "        1: ['age: 68', 'age: 58', 'age: 66', 'age: 56', 'age: 55', 'age: 50', 'age: 37', 'age: 59', 'age: 46', 'age: 49', 'age: 62', 'age: 65', 'age: 63', 'age: 41', 'age: 33', 'age: 73', 'age: 70', 'age: 69', 'age: 39', 'age: 43', 'age: 48', 'age: 72', 'age: 76', 'age: 40', 'age: 54', 'age: 45', 'age: 71', 'age: 52', 'age: 53', 'age: 67'],\n",
    "        2: ['Sex: male', 'Sex: female'],\n",
    "        3: ['tumor stage (uicc-7th): IIIB', 'tumor stage (uicc-7th): 0', 'tumor stage (uicc-7th): I', 'tumor stage (uicc-7th): IIA', 'tumor stage (uicc-7th): IIIA', 'tumor stage (uicc-7th): IIIC', 'tumor stage (uicc-7th): IVA', 'tumor stage (uicc-7th): IV', 'tumor stage (uicc-7th): lll', 'tumor stage (uicc-7th): IIB'],\n",
    "        4: ['description (operation): LAR', 'description (operation): ULAR', 'description (operation): LAR, TAH', 'description (operation): LAR, ileostomy', \"description (operation): Hartmann's procedure\", 'description (operation): APR', 'description (operation): APR,TAH,LSO,S6 segmentectomy', 'description (operation): TEM', 'description (operation): L-LAR, Rt PLND, ileostomy', 'description (operation): ISR, colonic pouch, ileostomy', 'description (operation): ISR, coloanal, ileostomy', 'description (operation): R-LAR, ileostomy', 'description (operation): ISR, colonic pouch, Ileostomy', 'description (operation): ISR, colonic pouch, ileostomy, Rt PLND', 'description (operation): ISR, Coloanal, ileostomy', 'description (operation): ELAR', 'description (operation): ISR']\n",
    "    }\n",
    "    \n",
    "    # Create a clinical data DataFrame with the structure expected by geo_select_clinical_features\n",
    "    # Where rows are characteristics and each column would represent a sample\n",
    "    clinical_data = pd.DataFrame()\n",
    "    for key, values in sample_chars.items():\n",
    "        clinical_data.loc[key] = values\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_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",
    "    # Preview the extracted features\n",
    "    preview = preview_df(selected_features)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_features.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab64f083",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6eacdd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import gzip\n",
    "from typing import Optional, Callable, Dict, Any, List\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "# Load the clinical data \n",
    "clinical_data_file = os.path.join(in_cohort_dir, \"GSE46862_series_matrix.txt.gz\")\n",
    "\n",
    "try:\n",
    "    # Read the clinical data\n",
    "    with gzip.open(clinical_data_file, 'rt') as file:\n",
    "        lines = file.readlines()\n",
    "    \n",
    "    # Extract sample characteristics\n",
    "    sample_char_lines = [line for line in lines if line.startswith('!Sample_characteristics_ch1')]\n",
    "    \n",
    "    # Extract sample IDs\n",
    "    sample_id_line = next((line for line in lines if line.startswith('!Sample_geo_accession')), None)\n",
    "    if sample_id_line:\n",
    "        sample_ids = sample_id_line.strip().split('\\t')[1:]\n",
    "    else:\n",
    "        sample_ids = []\n",
    "    \n",
    "    # Create a dictionary to organize sample characteristics\n",
    "    sample_char_dict = {}\n",
    "    \n",
    "    for line in sample_char_lines:\n",
    "        values = line.strip().split('\\t')[1:]\n",
    "        sample_char_dict[len(sample_char_dict)] = values\n",
    "    \n",
    "    # Create a DataFrame from the dictionary\n",
    "    clinical_data = pd.DataFrame(sample_char_dict, index=sample_ids).T\n",
    "    \n",
    "    # Print some sample data to analyze\n",
    "    print(\"Sample characteristics dictionary:\")\n",
    "    for key, values in sample_char_dict.items():\n",
    "        unique_values = set(values)\n",
    "        print(f\"Key {key}: {list(unique_values)[:5]}{' ...' if len(unique_values) > 5 else ''} (Unique values: {len(unique_values)})\")\n",
    "    \n",
    "    # Extract title and description for background information\n",
    "    title_line = next((line for line in lines if line.startswith('!Series_title')), None)\n",
    "    title = title_line.strip().split('!Series_title\\t')[1] if title_line else \"No title found\"\n",
    "    \n",
    "    description_lines = [line for line in lines if line.startswith('!Series_summary')]\n",
    "    description = ' '.join([line.strip().split('!Series_summary\\t')[1] for line in description_lines]) if description_lines else \"No description found\"\n",
    "    \n",
    "    print(\"\\nDataset Title:\")\n",
    "    print(title)\n",
    "    print(\"\\nDataset Description:\")\n",
    "    print(description)\n",
    "    \n",
    "    # Check if this is a gene expression dataset\n",
    "    platform_line = next((line for line in lines if line.startswith('!Series_platform_id')), None)\n",
    "    platform_id = platform_line.strip().split('!Series_platform_id\\t')[1] if platform_line else \"Unknown\"\n",
    "    \n",
    "    print(\"\\nPlatform ID:\")\n",
    "    print(platform_id)\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error reading clinical data: {e}\")\n",
    "    clinical_data = pd.DataFrame()\n",
    "    sample_char_dict = {}\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on platform info (GPL6244) and description, determine if gene expression data is likely available\n",
    "is_gene_available = True  # The platform GPL6244 is for gene expression\n",
    "\n",
    "# 2. Clinical Feature Availability and Data Type Conversion\n",
    "# Analyze sample characteristics to identify trait, age, and gender information\n",
    "\n",
    "# 2.1 Trait Availability (Chemoradiation therapy response)\n",
    "trait_row = 0  # Chemoradiation therapy response is in row 0\n",
    "\n",
    "# Function to convert trait values\n",
    "def convert_trait(value):\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Remove quotes and extract value after colon\n",
    "    value = str(value).strip('\"')\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert therapy responses to binary\n",
    "    value_lower = str(value).lower()\n",
    "    if 'nt' in value_lower or 'mo' in value_lower:  # Non-responders\n",
    "        return 0\n",
    "    elif 'mi' in value_lower or 'to' in value_lower:  # Responders\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 2.2 Age Availability\n",
    "age_row = 1  # Age information is in row 1\n",
    "\n",
    "# Function to convert age values\n",
    "def convert_age(value):\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Remove quotes and extract value after colon\n",
    "    value = str(value).strip('\"')\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric age\n",
    "    try:\n",
    "        age_match = re.search(r'(\\d+)', str(value))\n",
    "        if age_match:\n",
    "            return float(age_match.group(1))\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    return None\n",
    "\n",
    "# 2.3 Gender Availability\n",
    "gender_row = 2  # Sex information is in row 2\n",
    "\n",
    "# Function to convert gender values\n",
    "def convert_gender(value):\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Remove quotes and extract value after colon\n",
    "    value = str(value).strip('\"')\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: Female = 0, Male = 1\n",
    "    value_lower = str(value).lower()\n",
    "    if 'female' in value_lower or 'f' == value_lower:\n",
    "        return 0\n",
    "    elif 'male' in value_lower or 'm' == value_lower:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata - Initial Filtering\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 (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features\n",
    "    clinical_features_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 features\n",
    "    print(\"\\nExtracted Clinical Features Preview:\")\n",
    "    print(preview_df(clinical_features_df))\n",
    "    \n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save to CSV\n",
    "    clinical_features_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a804951",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c18dad67",
   "metadata": {},
   "outputs": [],
   "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. First, let's examine the structure of the matrix file to understand its format\n",
    "import gzip\n",
    "\n",
    "# Peek at the first few lines of the file to understand its structure\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    # Read first 100 lines to find the header structure\n",
    "    for i, line in enumerate(file):\n",
    "        if '!series_matrix_table_begin' in line:\n",
    "            print(f\"Found data marker at line {i}\")\n",
    "            # Read the next line which should be the header\n",
    "            header_line = next(file)\n",
    "            print(f\"Header line: {header_line.strip()}\")\n",
    "            # And the first data line\n",
    "            first_data_line = next(file)\n",
    "            print(f\"First data line: {first_data_line.strip()}\")\n",
    "            break\n",
    "        if i > 100:  # Limit search to first 100 lines\n",
    "            print(\"Matrix table marker not found in first 100 lines\")\n",
    "            break\n",
    "\n",
    "# 3. Now try to get the genetic data with better error handling\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(gene_data.index[:20])\n",
    "except KeyError as e:\n",
    "    print(f\"KeyError: {e}\")\n",
    "    \n",
    "    # Alternative approach: manually extract the data\n",
    "    print(\"\\nTrying alternative approach to read the gene data:\")\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        # Find the start of the data\n",
    "        for line in file:\n",
    "            if '!series_matrix_table_begin' in line:\n",
    "                break\n",
    "                \n",
    "        # Read the headers and data\n",
    "        import pandas as pd\n",
    "        df = pd.read_csv(file, sep='\\t', index_col=0)\n",
    "        print(f\"Column names: {df.columns[:5]}\")\n",
    "        print(f\"First 20 row IDs: {df.index[:20]}\")\n",
    "        gene_data = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1898c17",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abd87d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the identifiers in the gene expression data\n",
    "# The identifiers appear to be numeric codes (like 7892501, 7892502, etc.)\n",
    "# These are not standard human gene symbols (which would be like BRCA1, TP53, etc.)\n",
    "# These are likely probe IDs from a microarray platform that need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ca76e6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b9c95ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fc70fd",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08403493",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze the format of gene identifiers in both datasets\n",
    "print(\"\\nIdentifying mapping columns:\")\n",
    "print(f\"Gene expression data has identifiers like: {gene_data.index[:5]}\")\n",
    "print(f\"Gene annotation data columns: {gene_annotation.columns}\")\n",
    "\n",
    "# The 'ID' column in gene_annotation matches the index in gene_data\n",
    "# The 'gene_assignment' column contains gene symbols\n",
    "\n",
    "# Identifying gene symbols in the gene_assignment column\n",
    "print(\"\\nChecking gene_assignment format:\")\n",
    "sample_gene = gene_annotation['gene_assignment'].iloc[2]\n",
    "print(f\"Sample gene assignment: {sample_gene[:200]}...\")\n",
    "\n",
    "# Extract the mapping between probe IDs and gene symbols\n",
    "gene_mapping = get_gene_mapping(\n",
    "    annotation=gene_annotation, \n",
    "    prob_col='ID', \n",
    "    gene_col='gene_assignment'\n",
    ")\n",
    "\n",
    "print(\"\\nMapped genes preview:\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# Apply the gene mapping to convert probe-based expression to gene expression\n",
    "gene_data = apply_gene_mapping(\n",
    "    expression_df=gene_data,\n",
    "    mapping_df=gene_mapping\n",
    ")\n",
    "\n",
    "# Preview the gene expression data\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(f\"First 5 genes: {gene_data.index[:5]}\")\n",
    "print(f\"Sample columns: {gene_data.columns[:5]}\")\n",
    "\n",
    "# Save the gene 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\"\\nGene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3daa7cf",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d00aab4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Retrieve the original clinical data directly from step 2\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Extract clinical features using the geo_select_clinical_features function as in step 3\n",
    "clinical_features_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_df,\n",
    "    trait=trait,\n",
    "    trait_row=0,  # chemoradiation therapy response\n",
    "    convert_trait=lambda x: 1 if any(resp in str(x).upper() for resp in [\"NT\", \"TO\"]) else 0,  # better response = 1\n",
    "    age_row=1,    # age information\n",
    "    convert_age=lambda x: int(str(x).split(\":\", 1)[1].strip()) if \":\" in str(x) else None,\n",
    "    gender_row=2,  # gender information\n",
    "    convert_gender=lambda x: 0 if \"female\" in str(x).lower() else 1 if \"male\" in str(x).lower() else None\n",
    ")\n",
    "\n",
    "# Fix column names in gene data to match clinical data format\n",
    "normalized_gene_data.columns = [col.strip('\"') for col in normalized_gene_data.columns]\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
    "print(f\"Initial linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine whether the trait and demographic features are severely biased\n",
    "is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Conduct final quality validation and save cohort 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=True, \n",
    "    is_trait_available=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from rectal cancer patients with variable responses to chemoradiation therapy.\"\n",
    ")\n",
    "\n",
    "# 7. If the linked data is usable, save it as a CSV file\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 was determined to be unusable and was not saved\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}