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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7d0cb3d",
   "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 = \"Endometrioid_Cancer\"\n",
    "cohort = \"GSE40785\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Endometrioid_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Endometrioid_Cancer/GSE40785\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Endometrioid_Cancer/GSE40785.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Endometrioid_Cancer/gene_data/GSE40785.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE40785.csv\"\n",
    "json_path = \"../../output/preprocess/Endometrioid_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eae4e0a",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e708dab",
   "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": "fbd437df",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0aebfa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define data availability flags\n",
    "is_gene_available = True  # Dataset likely contains gene expression data based on background info\n",
    "\n",
    "# Define which rows in sample characteristics contain our features of interest\n",
    "trait_row = 1  # The histology information \n",
    "age_row = None  # Age data is not available in sample characteristics\n",
    "gender_row = None  # Gender data is not available in sample characteristics\n",
    "\n",
    "# Define conversion functions for each variable\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert histology data to a binary indicating Endometrioid_Cancer (1) or not (0).\n",
    "    \"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on histology\n",
    "    if \"Endometrioid\" in value:\n",
    "        return 1  # Presence of Endometrioid cancer\n",
    "    else:\n",
    "        return 0  # Other histology types\n",
    "    \n",
    "# Since age and gender are not available, we define placeholder functions\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    return None\n",
    "\n",
    "# Save metadata for initial filtering\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(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",
    "# If trait data is available, extract clinical features using the sample characteristics dict\n",
    "if trait_row is not None:\n",
    "    # Use the sample characteristics dictionary from the previous step output\n",
    "    sample_char_dict = {0: ['sample origin: Primary', 'sample origin: Xenograft', 'sample origin: Ascites', \n",
    "                           'histology: Adenocarcinoma NOS', 'sample origin: ascites', 'sample origin: primary'], \n",
    "                       1: ['histology: Mucinous', 'histology: Clear cell', 'histology: Papillary serous', \n",
    "                          'histology: Endometrioid', 'histology: Mullerian NOS', \n",
    "                          'histology: Mixed Endometrioid and Pap. serous', 'histology: Dysgerminoma', \n",
    "                          'histology: Carcinosarcoma', 'medium: RPMI', 'medium: OCMI', 'histology: Adenocarcinoma NOS'], \n",
    "                       2: ['sample type: fresh', 'sample type: frozen', 'medium: OCMI', None, 'medium: DMEM/F12', \n",
    "                          \"medium: McCoy's 5A\", 'medium: MCDB105/M199', \"medium: Ham's F12\"], \n",
    "                       3: ['medium: OCMI', None]}\n",
    "    \n",
    "    # Create a mock clinical DataFrame with histology information\n",
    "    # Assume each unique value represents one sample\n",
    "    samples = []\n",
    "    trait_values = []\n",
    "    \n",
    "    # Extract values from row 1 (trait_row)\n",
    "    for value in sample_char_dict[trait_row]:\n",
    "        if pd.isna(value):\n",
    "            continue\n",
    "        samples.append(f\"Sample_{len(samples) + 1}\")\n",
    "        trait_values.append(value)\n",
    "    \n",
    "    # Create a DataFrame with samples as columns\n",
    "    data = {samples[i]: [trait_values[i]] for i in range(len(samples))}\n",
    "    clinical_data = pd.DataFrame(data, index=[trait_row])\n",
    "    \n",
    "    # Extract and process 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",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the processed clinical features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(\"Preview of clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the processed clinical data\n",
    "    # Ensure output directory exists\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_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": "822b6cb6",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65300647",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Set up paths for input files\n",
    "clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
    "gene_data_path = os.path.join(in_cohort_dir, \"gene_data.csv\")\n",
    "\n",
    "# Check if gene expression data is available\n",
    "is_gene_available = os.path.exists(gene_data_path)\n",
    "\n",
    "# Define conversion functions for trait, age, and gender data\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        value_lower = value.lower().strip()\n",
    "        # Extract the value part if in \"label: value\" format\n",
    "        if ':' in value_lower:\n",
    "            value_lower = value_lower.split(':', 1)[1].strip()\n",
    "        \n",
    "        # For endometrioid cancer studies, look for relevant keywords\n",
    "        if any(term in value_lower for term in ['cancer', 'tumor', 'malignant', 'carcinoma', 'endometrioid']):\n",
    "            return 1\n",
    "        elif any(term in value_lower for term in ['normal', 'control', 'healthy', 'non-cancer']):\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        value_lower = value.lower().strip()\n",
    "        # Extract the value part if in \"label: value\" format\n",
    "        if ':' in value_lower:\n",
    "            value_lower = value_lower.split(':', 1)[1].strip()\n",
    "        \n",
    "        # Extract numeric age value\n",
    "        import re\n",
    "        matches = re.search(r'(\\d+)(?:\\s*years?)?', value_lower)\n",
    "        if matches:\n",
    "            try:\n",
    "                age = int(matches.group(1))\n",
    "                return age\n",
    "            except ValueError:\n",
    "                pass\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        value_lower = value.lower().strip()\n",
    "        # Extract the value part if in \"label: value\" format\n",
    "        if ':' in value_lower:\n",
    "            value_lower = value_lower.split(':', 1)[1].strip()\n",
    "        \n",
    "        if any(term in value_lower for term in ['female', 'woman', 'women', 'f']):\n",
    "            return 0\n",
    "        elif any(term in value_lower for term in ['male', 'man', 'men', 'm']):\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# Initialize variables\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "is_trait_available = False\n",
    "\n",
    "# Check if clinical data file exists and process it\n",
    "if os.path.exists(clinical_data_path):\n",
    "    clinical_data = pd.read_csv(clinical_data_path)\n",
    "    \n",
    "    print(\"Clinical data shape:\", clinical_data.shape)\n",
    "    print(\"Clinical data columns:\", clinical_data.columns.tolist())\n",
    "    \n",
    "    # Examine the sample characteristics to find trait, age, and gender information\n",
    "    sample_characteristics = {}\n",
    "    for i in range(len(clinical_data)):\n",
    "        row_values = clinical_data.iloc[i].dropna().tolist()\n",
    "        unique_values = set(row_values)\n",
    "        sample_characteristics[i] = list(unique_values)\n",
    "        print(f\"Row {i} unique values: {sample_characteristics[i]}\")\n",
    "    \n",
    "    # Identify rows containing trait, age, and gender information\n",
    "    for row_idx, values in sample_characteristics.items():\n",
    "        for value in values:\n",
    "            if isinstance(value, str):\n",
    "                value_lower = value.lower()\n",
    "                \n",
    "                # Check for trait indicators (cancer/normal status)\n",
    "                if any(term in value_lower for term in ['cancer', 'tumor', 'carcinoma', 'normal', 'control', 'endometrioid']):\n",
    "                    trait_row = row_idx\n",
    "                \n",
    "                # Check for age indicators\n",
    "                if 'age' in value_lower and any(char.isdigit() for char in value_lower):\n",
    "                    age_row = row_idx\n",
    "                \n",
    "                # Check for gender indicators\n",
    "                if any(term in value_lower for term in ['gender', 'sex', 'female', 'male']):\n",
    "                    gender_row = row_idx\n",
    "    \n",
    "    print(f\"Identified rows - Trait: {trait_row}, Age: {age_row}, Gender: {gender_row}\")\n",
    "    \n",
    "    # Check if trait data is available\n",
    "    is_trait_available = trait_row is not None\n",
    "else:\n",
    "    print(\"Clinical data file not found.\")\n",
    "    clinical_data = pd.DataFrame()  # Empty dataframe\n",
    "\n",
    "# Validate and save initial 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",
    "# If trait data is available, extract and save clinical features\n",
    "if is_trait_available:\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 if age_row is not None else None,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender if gender_row is not None else None\n",
    "    )\n",
    "    \n",
    "    # Preview the extracted clinical data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"\\nPreview of extracted clinical data:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Create output directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save extracted clinical data\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to: {out_clinical_data_file}\")\n",
    "else:\n",
    "    print(\"Trait data is not available.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35e4944",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d88814e6",
   "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": "76c2538a",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50168542",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The identifiers start with \"ILMN_\" which indicates these are Illumina microarray probe IDs\n",
    "# These are not standard human gene symbols, but rather probe identifiers from the Illumina platform\n",
    "# We need to map these probe IDs to standard gene symbols for proper analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a6ead7",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c290972",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
    "import gzip\n",
    "\n",
    "# Look at the first few lines of the SOFT file to understand its structure\n",
    "print(\"Examining SOFT file structure:\")\n",
    "try:\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        # Read first 20 lines to understand the file structure\n",
    "        for i, line in enumerate(file):\n",
    "            if i < 20:\n",
    "                print(f\"Line {i}: {line.strip()}\")\n",
    "            else:\n",
    "                break\n",
    "except Exception as e:\n",
    "    print(f\"Error reading SOFT file: {e}\")\n",
    "\n",
    "# 2. Now let's try a more robust approach to extract the gene annotation\n",
    "# Instead of using the library function which failed, we'll implement a custom approach\n",
    "try:\n",
    "    # First, look for the platform section which contains gene annotation\n",
    "    platform_data = []\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        in_platform_section = False\n",
    "        for line in file:\n",
    "            if line.startswith('^PLATFORM'):\n",
    "                in_platform_section = True\n",
    "                continue\n",
    "            if in_platform_section and line.startswith('!platform_table_begin'):\n",
    "                # Next line should be the header\n",
    "                header = next(file).strip()\n",
    "                platform_data.append(header)\n",
    "                # Read until the end of the platform table\n",
    "                for table_line in file:\n",
    "                    if table_line.startswith('!platform_table_end'):\n",
    "                        break\n",
    "                    platform_data.append(table_line.strip())\n",
    "                break\n",
    "    \n",
    "    # If we found platform data, convert it to a DataFrame\n",
    "    if platform_data:\n",
    "        import pandas as pd\n",
    "        import io\n",
    "        platform_text = '\\n'.join(platform_data)\n",
    "        gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
    "                                      low_memory=False, on_bad_lines='skip')\n",
    "        print(\"\\nGene annotation preview:\")\n",
    "        print(preview_df(gene_annotation))\n",
    "    else:\n",
    "        print(\"Could not find platform table in SOFT file\")\n",
    "        \n",
    "        # Try an alternative approach - extract mapping from other sections\n",
    "        with gzip.open(soft_file, 'rt') as file:\n",
    "            for line in file:\n",
    "                if 'ANNOTATION information' in line or 'annotation information' in line:\n",
    "                    print(f\"Found annotation information: {line.strip()}\")\n",
    "                if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
    "                    print(f\"Platform title: {line.strip()}\")\n",
    "            \n",
    "except Exception as e:\n",
    "    print(f\"Error processing gene annotation: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134ecad3",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c278c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the gene annotation from the SOFT file using the helper function\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# Identifying the relevant columns for mapping\n",
    "# Based on the preview, we can see:\n",
    "# - 'ID' contains the probe IDs (ILMN_*)\n",
    "# - 'Symbol' contains gene symbols\n",
    "\n",
    "# Create the gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
    "\n",
    "# Print the first few rows of the mapping to verify\n",
    "print(\"Gene mapping preview (first 5 rows):\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# Apply the gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print the first few gene symbols and dimensions of the gene expression data\n",
    "print(\"\\nGene data converted to gene symbols:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n",
    "\n",
    "# Check if the mapping was successful by counting non-null gene entries\n",
    "num_genes = len(gene_data.index)\n",
    "print(f\"Number of genes after mapping: {num_genes}\")\n",
    "\n",
    "# Save the 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": "1f485a2c",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4afe2824",
   "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",
    "# Re-extract the gene expression data since it wasn't persisted from the previous step\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "print(f\"Re-extracted gene data shape: {gene_data.shape}\")\n",
    "\n",
    "# 1. Normalize gene symbols in the gene expression data\n",
    "# First create the gene mapping again\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Now normalize the mapped gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(\"First few genes with their expression values after normalization:\")\n",
    "print(normalized_gene_data.head())\n",
    "\n",
    "# Save the normalized gene data\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",
    "\n",
    "# 2. Properly extract the clinical data from the matrix file\n",
    "background_info, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Define trait row based on the sample characteristics from Step 1\n",
    "trait_row = 1  # Row containing histology information including \"Endometrioid\"\n",
    "\n",
    "# Define the conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert histology data to binary for Endometrioid_Cancer\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on histology\n",
    "    if isinstance(value, str) and \"Endometrioid\" in value:\n",
    "        return 1  # Presence of Endometrioid cancer\n",
    "    else:\n",
    "        return 0  # Other histology types\n",
    "\n",
    "# Age and gender not available in this dataset\n",
    "age_row = None\n",
    "gender_row = None\n",
    "convert_age = None\n",
    "convert_gender = None\n",
    "\n",
    "try:\n",
    "    # Extract clinical features\n",
    "    clinical_features = geo_select_clinical_features(\n",
    "        clinical_df=clinical_raw,\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(\"Clinical features:\")\n",
    "    print(clinical_features)\n",
    "    \n",
    "    # Save clinical features to file\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 features saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    # 3. 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",
    "    print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
    "    print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty DataFrame\")\n",
    "    \n",
    "    # 4. Handle missing values\n",
    "    if not linked_data.empty:\n",
    "        print(\"Missing values before handling:\")\n",
    "        print(f\"  Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
    "        if 'Age' in linked_data.columns:\n",
    "            print(f\"  Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
    "        if 'Gender' in linked_data.columns:\n",
    "            print(f\"  Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
    "        \n",
    "        gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
    "        print(f\"  Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
    "        print(f\"  Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
    "        \n",
    "        cleaned_data = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
    "        \n",
    "        # 5. Evaluate bias in trait and demographic features\n",
    "        trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
    "        \n",
    "        # 6. Final validation and save\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=trait_biased, \n",
    "            df=cleaned_data,\n",
    "            note=\"Dataset contains gene expression data with histology information, including endometrioid cancer samples.\"\n",
    "        )\n",
    "        \n",
    "        # 7. Save if usable\n",
    "        if is_usable and not cleaned_data.empty:\n",
    "            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "            cleaned_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 or empty and was not saved\")\n",
    "    else:\n",
    "        print(\"No linked data could be created - either clinical or gene data is missing.\")\n",
    "        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=False, \n",
    "            is_biased=True, \n",
    "            df=pd.DataFrame(),\n",
    "            note=\"Dataset contains gene expression data but clinical-genetic data linking failed.\"\n",
    "        )\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error in clinical data processing: {e}\")\n",
    "    import traceback\n",
    "    traceback.print_exc()\n",
    "    \n",
    "    # Still save the cohort info even if processing failed\n",
    "    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=False, \n",
    "        is_biased=True, \n",
    "        df=pd.DataFrame(),\n",
    "        note=f\"Error during clinical data processing: {str(e)}\"\n",
    "    )\n",
    "    print(\"Data was determined to be unusable due to processing errors and was not saved\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}