{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9076d8c",
   "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 = \"Alopecia\"\n",
    "cohort = \"GSE81071\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Alopecia\"\n",
    "in_cohort_dir = \"../../input/GEO/Alopecia/GSE81071\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Alopecia/GSE81071.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Alopecia/gene_data/GSE81071.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Alopecia/clinical_data/GSE81071.csv\"\n",
    "json_path = \"../../output/preprocess/Alopecia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb11d68",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cf428f9",
   "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": "0f491e13",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21767180",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Availability Analysis\n",
    "# Based on background info, this is a gene expression dataset from skin biopsies\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Identifying rows for trait, age, and gender\n",
    "\n",
    "# For trait (Alopecia):\n",
    "# Looking at sample characteristics, there is no explicit mention of alopecia\n",
    "# But the series title mentions \"discoid lesions (DLE) are often circular and frequently lead to alopecia\"\n",
    "# We can infer that DLE cases could be considered as potentially having alopecia\n",
    "trait_row = 1  # \"disease state\" in row 1 contains DLE which can be associated with alopecia\n",
    "\n",
    "# For age and gender:\n",
    "# Neither age nor gender information appears to be available in the sample characteristics\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert disease state values to binary for Alopecia trait\n",
    "    DLE is associated with alopecia according to the background info\n",
    "    \"\"\"\n",
    "    if value is None:\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",
    "    # Based on background info, DLE is associated with alopecia\n",
    "    if value.lower() == \"dle\":\n",
    "        return 1  # Positive for alopecia risk/condition\n",
    "    elif value.lower() in [\"healthy\", \"normal\", \"scle\"]:\n",
    "        return 0  # Not associated with alopecia\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Placeholder for age conversion - not used since age data is not available\"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Placeholder for gender conversion - not used since gender data is not available\"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save metadata\n",
    "# Check if trait data is available (trait_row is not None)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save initial validation information\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",
    "# Only execute if trait_row is not None\n",
    "if trait_row is not None:\n",
    "    # Create DataFrame from the sample characteristics dictionary\n",
    "    # The dictionary structure shows rows with lists of values\n",
    "    sample_char_dict = {\n",
    "        0: ['tissue: Skin', 'disease state: Normal', 'disease state: DLE', 'disease state: SCLE'], \n",
    "        1: ['disease state: healthy', 'disease state: DLE', 'disease state: sCLE', 'tissue: Skin biopsy']\n",
    "    }\n",
    "    \n",
    "    # Convert sample characteristics dictionary to a DataFrame format\n",
    "    sample_values = []\n",
    "    for i in range(max(sample_char_dict.keys()) + 1):\n",
    "        if i in sample_char_dict:\n",
    "            sample_values.append(sample_char_dict[i])\n",
    "        else:\n",
    "            sample_values.append([])\n",
    "    \n",
    "    clinical_data = pd.DataFrame(sample_values)\n",
    "    \n",
    "    # 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 processed clinical data\n",
    "    print(\"Preview of processed clinical data:\")\n",
    "    print(preview_df(clinical_df))\n",
    "    \n",
    "    # Save the clinical data\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": "2d1c9cab",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853431e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c762f7",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b0919f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These identifiers appear to be probe IDs from a microarray platform rather than standard human gene symbols.\n",
    "# They follow a pattern of numbers followed by \"_at\" which is typical of microarray probe IDs\n",
    "# (like Affymetrix arrays).\n",
    "# These would need to be mapped to gene symbols for standardized analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c678032",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec80ab35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. 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",
    "# 3. 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": "d4b096d8",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a7eaa21",
   "metadata": {},
   "outputs": [],
   "source": [
    "Analyzing the current step and previous outputs, I'll correct the gene identifier mapping code to address the format mismatch:\n",
    "\n",
    "```python\n",
    "# 1. First examine the structure of the SOFT file more thoroughly to find gene symbols\n",
    "# Re-read the SOFT file to look for gene symbol information\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Let's read a portion of the SOFT file to understand its structure better\n",
    "import gzip\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    # Read first 100 lines to identify the structure\n",
    "    lines = [next(f).strip() for _ in range(100) if f]\n",
    "\n",
    "# Look for lines that might contain gene symbol information\n",
    "gene_symbol_lines = [line for line in lines if \"symbol\" in line.lower() or \"gene_symbol\" in line.lower()]\n",
    "print(\"Sample lines containing gene symbol information:\")\n",
    "for i, line in enumerate(gene_symbol_lines[:5]):\n",
    "    print(f\"{i}: {line}\")\n",
    "\n",
    "# Examine the structure of the gene expression data more closely\n",
    "print(\"\\nStructure of gene expression data:\")\n",
    "print(f\"Gene data shape: {gene_data.shape}\")\n",
    "print(f\"Gene data columns (first 5): {list(gene_data.columns)[:5]}\")\n",
    "print(f\"Gene data index format (first 5): {list(gene_data.index[:5])}\")\n",
    "\n",
    "# Let's try a different approach - use platform information from the SOFT file\n",
    "# Read platform information to find probe-to-gene mapping\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    platform_id = None\n",
    "    for line in f:\n",
    "        if line.startswith('^PLATFORM'):\n",
    "            platform_id = line.strip().split('=')[1]\n",
    "            break\n",
    "\n",
    "print(f\"\\nPlatform ID: {platform_id}\")\n",
    "\n",
    "# Instead of relying on the limited annotation, let's try to extract gene symbols from the SOFT file\n",
    "# Read the platform details to find gene symbol mappings\n",
    "probe_gene_dict = {}\n",
    "gene_symbol_column = None\n",
    "probe_id_column = None\n",
    "current_section = None\n",
    "\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for line in f:\n",
    "        if line.startswith('!platform_table_begin'):\n",
    "            current_section = 'platform_table'\n",
    "            # Read the header line to find relevant columns\n",
    "            header_line = next(f).strip()\n",
    "            headers = header_line.split('\\t')\n",
    "            \n",
    "            # Find columns for probe ID and gene symbol\n",
    "            for i, header in enumerate(headers):\n",
    "                if header.lower() in ['id', 'id_ref', 'probe_id', 'probeid']:\n",
    "                    probe_id_column = i\n",
    "                if header.lower() in ['gene_symbol', 'symbol', 'genesymbol']:\n",
    "                    gene_symbol_column = i\n",
    "            \n",
    "            if probe_id_column is not None and gene_symbol_column is not None:\n",
    "                print(f\"Found probe ID column ({headers[probe_id_column]}) and gene symbol column ({headers[gene_symbol_column]})\")\n",
    "                # Read the mapping\n",
    "                for data_line in f:\n",
    "                    if data_line.startswith('!platform_table_end'):\n",
    "                        break\n",
    "                    fields = data_line.strip().split('\\t')\n",
    "                    if len(fields) > max(probe_id_column, gene_symbol_column):\n",
    "                        probe_id = fields[probe_id_column]\n",
    "                        gene_symbol = fields[gene_symbol_column]\n",
    "                        if gene_symbol:  # Only add if gene symbol is not empty\n",
    "                            probe_gene_dict[probe_id] = gene_symbol\n",
    "            break\n",
    "\n",
    "# If we found mappings, create a mapping DataFrame\n",
    "if probe_gene_dict:\n",
    "    print(f\"\\nFound {len(probe_gene_dict)} probe-to-gene mappings\")\n",
    "    # Create mapping DataFrame\n",
    "    mapping_data = pd.DataFrame({\n",
    "        'ID': list(probe_gene_dict.keys()),\n",
    "        'Gene': list(probe_gene_dict.values())\n",
    "    })\n",
    "    print(\"Mapping dataframe preview:\")\n",
    "    print(preview_df(mapping_data))\n",
    "    \n",
    "    # Apply gene mapping with the new mapping dataframe\n",
    "    try:\n",
    "        gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "        print(\"\\nGene expression data after mapping:\")\n",
    "        print(f\"Shape: {gene_data.shape}\")\n",
    "        print(preview_df(gene_data))\n",
    "        \n",
    "        # Normalize gene symbols\n",
    "        gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "        print(\"\\nGene expression data after normalization:\")\n",
    "        print(f\"Shape: {gene_data.shape}\")\n",
    "        print(preview_df(gene_data))\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 data saved to {out_gene_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error applying gene mapping: {e}\")\n",
    "else:\n",
    "    # If we couldn't find the mapping in the SOFT file, try a fallback approach\n",
    "    print(\"\\nCouldn't find proper gene symbol mapping in the SOFT file.\")\n",
    "    print(\"Using Entrez Gene IDs instead...\")\n",
    "    \n",
    "    # Try to fix the format mismatch between gene_data index and gene_annotation ID\n",
    "    # Create a mapping between probe IDs in gene_data and gene annotation\n",
    "    gene_data_ids = set(gene_data.index)\n",
    "    annotation_ids = set(gene_annotation['ID'])\n",
    "    \n",
    "    # Check for any exact matches\n",
    "    common_ids = gene_data_ids.intersection(annotation_ids)\n",
    "    print(f\"Number of exact ID matches: {len(common_ids)}\")\n",
    "    \n",
    "    # If few exact matches, try to match by removing suffixes\n",
    "    if len(common_ids) < 100:\n",
    "        print(\"Trying to match IDs by removing suffixes...\")\n",
    "        # Create a mapping that ignores suffixes like '_at'\n",
    "        cleaned_gene_data_ids = {id.split('_')[0]: id for id in gene_data_ids}\n",
    "        cleaned_annotation_ids = {id.split('_')[0]: id for id in annotation_ids}\n",
    "        \n",
    "        # Find common base IDs\n",
    "        common_base_ids = set(cleaned_gene_data_ids.keys()).intersection(set(cleaned_annotation_ids.keys()))\n",
    "        print(f\"Number of matches after removing suffixes: {len(common_base_ids)}\")\n",
    "        \n",
    "        # Create a mapping from gene_data IDs to annotation IDs\n",
    "        id_mapping = {cleaned_gene_data_ids[base_id]: cleaned_annotation_ids[base_id] \n",
    "                     for base_id in common_base_ids if base_id in cleaned_gene_data_ids and base_id in cleaned_annotation_ids}\n",
    "        \n",
    "        if id_mapping:\n",
    "            # Create a new mapping dataframe based on this ID mapping\n",
    "            mapping_rows = []\n",
    "            for gene_data_id, annotation_id in id_mapping.items():\n",
    "                gene_symbol = gene_annotation.loc[gene_annotation['ID'] == annotation_id, 'ENTREZ_GENE_ID'].values\n",
    "                if len(gene_symbol) > 0:\n",
    "                    mapping_rows.append({'ID': gene_data_id, 'Gene': gene_symbol[0]})\n",
    "            \n",
    "            if mapping_rows:\n",
    "                mapping_df = pd.DataFrame(mapping_rows)\n",
    "                print(\"\\nCreated mapping dataframe with fixed ID format:\")\n",
    "                print(preview_df(mapping_df))\n",
    "                \n",
    "                # Apply gene mapping with the fixed mapping dataframe\n",
    "                gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "                print(\"\\nGene expression data after mapping:\")\n",
    "                print(f\"Shape: {gene_data.shape}\")\n",
    "                print(preview_df(gene_data))\n",
    "                \n",
    "                # Normalize gene symbols\n",
    "                gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "                print(\"\\nGene expression data after normalization:\")\n",
    "                print(f\"Shape: {gene_data.shape}\")\n",
    "                print(preview_df(gene_data))\n",
    "                \n",
    "                # Save the gene data\n",
    "                os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "                gene_data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0aa29a",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b1f1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Reload necessary data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Analyze gene identifiers in gene expression data and gene annotation data\n",
    "print(\"Gene expression data - first 10 probe IDs:\")\n",
    "print(list(gene_data.index[:10]))\n",
    "print(\"Gene annotation data - first 10 probe IDs:\")\n",
    "print(list(gene_annotation['ID'][:10]))\n",
    "\n",
    "# Check if there's any direct overlap between the two sets of IDs\n",
    "gene_data_ids = set(gene_data.index)\n",
    "annotation_ids = set(gene_annotation['ID'])\n",
    "common_ids = gene_data_ids.intersection(annotation_ids)\n",
    "print(f\"Number of directly matching IDs: {len(common_ids)}\")\n",
    "\n",
    "# Try to extract the platform information from the SOFT file\n",
    "platform_info = {}\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for line in f:\n",
    "        line = line.strip()\n",
    "        if line.startswith(\"!Platform_title\"):\n",
    "            platform_info['title'] = line.split(\"=\", 1)[1].strip().strip('\"')\n",
    "        elif line.startswith(\"!Platform_geo_accession\"):\n",
    "            platform_info['accession'] = line.split(\"=\", 1)[1].strip().strip('\"')\n",
    "\n",
    "print(\"Platform information:\")\n",
    "print(platform_info)\n",
    "\n",
    "# Create a mapping by cleaning probe IDs\n",
    "def clean_probe_id(probe_id):\n",
    "    # Remove common suffixes\n",
    "    for suffix in ['_at', '_st', '_a_at', '_s_at', '_x_at']:\n",
    "        if probe_id.endswith(suffix):\n",
    "            return probe_id[:-len(suffix)]\n",
    "    return probe_id\n",
    "\n",
    "# Clean and map the IDs\n",
    "cleaned_gene_data_ids = {clean_probe_id(id): id for id in gene_data_ids}\n",
    "cleaned_annotation_ids = {clean_probe_id(id): id for id in annotation_ids}\n",
    "\n",
    "# Find potential matches based on cleaned IDs\n",
    "potential_matches = {}\n",
    "for clean_id, orig_id in cleaned_gene_data_ids.items():\n",
    "    if clean_id in cleaned_annotation_ids:\n",
    "        potential_matches[orig_id] = cleaned_annotation_ids[clean_id]\n",
    "\n",
    "print(f\"Found {len(potential_matches)} potential matches after cleaning IDs\")\n",
    "\n",
    "# Try numeric matching if needed\n",
    "if len(potential_matches) < 100:\n",
    "    def extract_numeric(probe_id):\n",
    "        import re\n",
    "        match = re.search(r'(\\d+)', probe_id)\n",
    "        if match:\n",
    "            return match.group(1)\n",
    "        return None\n",
    "\n",
    "    numeric_gene_data_ids = {extract_numeric(id): id for id in gene_data_ids if extract_numeric(id)}\n",
    "    numeric_annotation_ids = {extract_numeric(id): id for id in annotation_ids if extract_numeric(id)}\n",
    "    \n",
    "    numeric_matches = {}\n",
    "    for num_id, orig_id in numeric_gene_data_ids.items():\n",
    "        if num_id in numeric_annotation_ids:\n",
    "            numeric_matches[orig_id] = numeric_annotation_ids[num_id]\n",
    "    \n",
    "    print(f\"Found {len(numeric_matches)} matches based on numeric part\")\n",
    "    \n",
    "    if len(numeric_matches) > len(potential_matches):\n",
    "        potential_matches = numeric_matches\n",
    "\n",
    "# Create a mapping dataframe\n",
    "if potential_matches:\n",
    "    mapping_rows = []\n",
    "    for gene_data_id, annotation_id in potential_matches.items():\n",
    "        gene_symbols = gene_annotation.loc[gene_annotation['ID'] == annotation_id, 'ENTREZ_GENE_ID']\n",
    "        if not gene_symbols.empty:\n",
    "            mapping_rows.append({'ID': gene_data_id, 'Gene': gene_symbols.iloc[0]})\n",
    "    \n",
    "    mapping_df = pd.DataFrame(mapping_rows)\n",
    "    print(\"Created custom mapping dataframe. Preview:\")\n",
    "    print(preview_df(mapping_df))\n",
    "else:\n",
    "    # Fallback to original mapping\n",
    "    mapping_df = get_gene_mapping(gene_annotation, 'ID', 'ENTREZ_GENE_ID')\n",
    "    print(\"Using original mapping dataframe. Preview:\")\n",
    "    print(preview_df(mapping_df))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "try:\n",
    "    gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
    "    print(\"Gene mapping applied. New gene data shape:\", gene_data_mapped.shape)\n",
    "    print(\"Gene data preview after mapping:\")\n",
    "    print(preview_df(gene_data_mapped))\n",
    "    \n",
    "    # If mapping produced results, use it\n",
    "    if gene_data_mapped.shape[0] > 0:\n",
    "        gene_data = gene_data_mapped\n",
    "    else:\n",
    "        # Use a direct approach if mapping failed\n",
    "        print(\"Mapping resulted in empty dataframe. Using a different approach...\")\n",
    "        simple_mapping = pd.DataFrame({\n",
    "            'ID': gene_data.index,\n",
    "            'Gene': [str(idx).split('_')[0] for idx in gene_data.index]\n",
    "        })\n",
    "        gene_data = apply_gene_mapping(gene_data, simple_mapping)\n",
    "        print(\"Alternative mapping applied. New gene data shape:\", gene_data.shape)\n",
    "except Exception as e:\n",
    "    print(f\"Error during gene mapping: {e}\")\n",
    "    # Fallback to a simpler approach\n",
    "    simple_mapping = pd.DataFrame({\n",
    "        'ID': gene_data.index,\n",
    "        'Gene': [str(idx).split('_')[0] for idx in gene_data.index]\n",
    "    })\n",
    "    gene_data = apply_gene_mapping(gene_data, simple_mapping)\n",
    "    print(\"Fallback mapping applied. New gene data shape:\", gene_data.shape)\n",
    "\n",
    "# 4. Normalize gene symbols to ensure consistency\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(\"Gene symbols normalized. Final gene data shape:\", gene_data.shape)\n",
    "print(\"Gene data preview after normalization:\")\n",
    "print(preview_df(gene_data))\n",
    "\n",
    "# 5. Save the processed gene data to a file\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 data saved to {out_gene_data_file}\")"
   ]
  }
 ],
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 "nbformat": 4,
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}