{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef013425",
   "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 = \"Craniosynostosis\"\n",
    "cohort = \"GSE27976\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Craniosynostosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Craniosynostosis/GSE27976\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Craniosynostosis/GSE27976.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Craniosynostosis/gene_data/GSE27976.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Craniosynostosis/clinical_data/GSE27976.csv\"\n",
    "json_path = \"../../output/preprocess/Craniosynostosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e419df3c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9cf9152",
   "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": "47f51e48",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e76aad6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import re\n",
    "from typing import Optional, Callable, Dict, Any, List, Union\n",
    "\n",
    "# Sample characteristics from previous output\n",
    "sample_characteristics = {\n",
    "    0: ['age months: 12.87', 'age months: 10.4', 'age months: 12.3', 'age months: 11.4', 'age months: 10.1', 'age months: 11', 'age months: 4.27', 'age months: 7.97', 'age months: 4.33', 'age months: 9.33', 'age months: 7.93', 'age months: 10.27', 'age months: 10.87', 'age months: 3.87', 'age months: 3.2', 'age months: 13.27', 'age months: 5.6', 'age months: 14.9', 'age months: 3.03', 'age months: 12.4', 'age months: 8.9', 'age months: 14.17', 'age months: 6.33', 'age months: 14.87', 'age months: 8.4', 'age months: 9.07', 'age months: 13.33', 'age months: 10', 'age months: 13.23', 'age months: 10.33'],\n",
    "    1: ['gender: F', 'gender: M'],\n",
    "    2: ['type: Metopic Synostosis', 'type: Coronal Synostosis R', 'type: Sagittal Synostosis', 'type: Coronal Synostosis L', 'type: Control'],\n",
    "    3: ['cell lines: osteoblast'],\n",
    "    4: ['tissue: skull']\n",
    "}\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data for craniosynostosis patients\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# Examining the sample characteristics dictionary:\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Trait data is in row 2 - as \"type\" which indicates craniosynostosis type\n",
    "trait_row = 2\n",
    "\n",
    "# Age data is in row 0 - as \"age months\"\n",
    "age_row = 0\n",
    "\n",
    "# Gender data is in row 1 - as \"gender\"\n",
    "gender_row = 1\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert craniosynostosis type to binary (0=control, 1=case)\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"Control\" in value:\n",
    "        return 0\n",
    "    elif \"Synostosis\" in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value: str) -> float:\n",
    "    \"\"\"\n",
    "    Convert age in months to a continuous value\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Extract the numeric part\n",
    "    match = re.search(r'(\\d+\\.?\\d*)', value)\n",
    "    if match:\n",
    "        return float(match.group(1))\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert gender to binary (0=female, 1=male)\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value.upper() == 'F':\n",
    "        return 0\n",
    "    elif value.upper() == 'M':\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",
    "# Initial validation and recording of metadata\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 data is available, extract and process clinical features\n",
    "if trait_row is not None:\n",
    "    # Create a suitable dataframe structure for the geo_select_clinical_features function\n",
    "    # We need to ensure the structure works with get_feature_data called inside geo_select_clinical_features\n",
    "    \n",
    "    # The geo_select_clinical_features expects a dataframe where:\n",
    "    # - Each row corresponds to a feature (age, gender, trait)\n",
    "    # - The values should be unique values for that feature\n",
    "    clinical_data = pd.DataFrame(sample_characteristics)\n",
    "    \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,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the selected clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\", preview)\n",
    "    \n",
    "    # Create the directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the clinical data to a CSV file\n",
    "    selected_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": "647ea55c",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc863e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "I understand that we need to properly analyze the dataset to find gene expression data availability and extract clinical features. Here's the corrected code:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "from typing import Callable, Optional, Dict, Any, Union, List\n",
    "\n",
    "# Load the necessary data\n",
    "print(f\"Files in {in_cohort_dir}:\")\n",
    "for f in os.listdir(in_cohort_dir):\n",
    "    print(f\"  {f}\")\n",
    "\n",
    "# Try to load the clinical data\n",
    "clinical_file = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
    "if os.path.exists(clinical_file):\n",
    "    clinical_data = pd.read_csv(clinical_file)\n",
    "    print(f\"Loaded clinical data from {clinical_file}\")\n",
    "else:\n",
    "    clinical_file = os.path.join(in_cohort_dir, f\"{cohort}_sample_characteristics.csv\")\n",
    "    if os.path.exists(clinical_file):\n",
    "        clinical_data = pd.read_csv(clinical_file)\n",
    "        print(f\"Loaded clinical data from {clinical_file}\")\n",
    "    else:\n",
    "        # Try to find any CSV file that might contain clinical data\n",
    "        csv_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.csv')]\n",
    "        clinical_data = None\n",
    "        for f in csv_files:\n",
    "            try:\n",
    "                clinical_file = os.path.join(in_cohort_dir, f)\n",
    "                df = pd.read_csv(clinical_file)\n",
    "                if 'characteristics_ch1' in df.columns or any('characteristics' in col.lower() for col in df.columns):\n",
    "                    clinical_data = df\n",
    "                    print(f\"Loaded clinical data from {clinical_file}\")\n",
    "                    break\n",
    "            except:\n",
    "                continue\n",
    "        \n",
    "        if clinical_data is None:\n",
    "            print(\"No clinical data files found\")\n",
    "            clinical_data = pd.DataFrame()\n",
    "\n",
    "# Check if gene expression data is likely available\n",
    "gene_files = [f for f in os.listdir(in_cohort_dir) if \n",
    "              \"gene\" in f.lower() or \n",
    "              \"expression\" in f.lower() or \n",
    "              \"series_matrix\" in f.lower() or\n",
    "              f.endswith('.txt') or \n",
    "              f.endswith('.tsv')]\n",
    "is_gene_available = len(gene_files) > 0\n",
    "print(f\"Gene expression data availability: {is_gene_available}\")\n",
    "\n",
    "# Print the clinical data structure to help us analyze it\n",
    "if not clinical_data.empty:\n",
    "    print(\"\\nClinical data shape:\", clinical_data.shape)\n",
    "    print(\"\\nClinical data columns:\", clinical_data.columns.tolist())\n",
    "    print(\"\\nFirst few rows of clinical data:\")\n",
    "    print(clinical_data.head())\n",
    "    \n",
    "    # Look for sample characteristics\n",
    "    if 'characteristics_ch1' in clinical_data.columns:\n",
    "        unique_values = {}\n",
    "        for i in range(len(clinical_data)):\n",
    "            val = clinical_data.loc[i, 'characteristics_ch1']\n",
    "            if i not in unique_values:\n",
    "                unique_values[i] = set()\n",
    "            unique_values[i].add(val)\n",
    "        \n",
    "        for row_idx, values in unique_values.items():\n",
    "            print(f\"Row {row_idx} unique values:\", values)\n",
    "    \n",
    "    # Or check for any columns that might contain sample characteristics\n",
    "    sample_cols = [col for col in clinical_data.columns if 'characteristics' in col.lower()]\n",
    "    for col in sample_cols:\n",
    "        print(f\"\\nUnique values in {col}:\")\n",
    "        for val in clinical_data[col].unique():\n",
    "            print(f\"  {val}\")\n",
    "\n",
    "# Based on our inspection, set the row indices for trait, age, and gender\n",
    "# Setting these based on the Craniosynostosis dataset patterns\n",
    "# After reviewing the data, these values should be updated\n",
    "trait_row = 1  # Sample row index where craniosynostosis status can be found\n",
    "age_row = 2    # Sample row index where age information can be found\n",
    "gender_row = 3 # Sample row index where gender information can be found\n",
    "\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert craniosynostosis information to binary format.\n",
    "    \n",
    "    Args:\n",
    "        value: The raw value from the clinical data\n",
    "        \n",
    "    Returns:\n",
    "        1 for cases, 0 for controls, None for unknown\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    \n",
    "    # Extract the actual value if it's in format \"label: value\"\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if 'case' in value or 'patient' in value or 'craniosynostosis' in value or 'affected' in value:\n",
    "        return 1\n",
    "    elif 'control' in value or 'normal' in value or 'unaffected' in value or 'healthy' in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value: str) -> float:\n",
    "    \"\"\"\n",
    "    Convert age information to numerical format.\n",
    "    \n",
    "    Args:\n",
    "        value: The raw age value from the clinical data\n",
    "        \n",
    "    Returns:\n",
    "        Age as a float, None for unknown\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    \n",
    "    # Extract the actual value if it's in format \"label: value\"\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract age\n",
    "    import re\n",
    "    \n",
    "    # Try to find a number, potentially followed by time units\n",
    "    age_match = re.search(r'(\\d+\\.?\\d*)\\s*(years?|yr|y|months?|mo|days?|d|weeks?|wk)?', value)\n",
    "    if age_match:\n",
    "        age_val = float(age_match.group(1))\n",
    "        unit = age_match.group(2) if age_match.group(2) else 'years'\n",
    "        \n",
    "        # Convert to years if needed\n",
    "        if 'month' in unit or unit == 'mo':\n",
    "            age_val /= 12\n",
    "        elif 'day' in unit or unit == 'd':\n",
    "            age_val /= 365\n",
    "        elif 'week' in unit or unit == 'wk':\n",
    "            age_val /= 52\n",
    "            \n",
    "        return age_val\n",
    "    \n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert gender information to binary format.\n",
    "    \n",
    "    Args:\n",
    "        value: The raw gender value from the clinical data\n",
    "        \n",
    "    Returns:\n",
    "        0 for female, 1 for male, None for unknown\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    \n",
    "    # Extract the actual value if it's in format \"label: value\"\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if 'female' in value or 'f' == value.strip() or 'woman' in value or 'girl' in value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value.strip() or 'man' in value or 'boy' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save initial metadata\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 clinical features\n",
    "if is_trait_available and not clinical_data.empty:\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,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the extracted features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f58aa24",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c80a868",
   "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": "6707fb59",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc4c675a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the identifier format (7892501, 7892502, etc), these appear to be probe IDs from a microarray\n",
    "# platform rather than standard human gene symbols (which typically have alphabetic characters like BRCA1, TP53).\n",
    "# \n",
    "# These numeric identifiers likely come from an Affymetrix or Illumina microarray platform and need to be\n",
    "# mapped to standard gene symbols for proper analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f3f8e4",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f88cefde",
   "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": "396a3fb6",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02a7c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine which columns contain identifiers and gene symbols\n",
    "# Examining the gene_annotation dataframe:\n",
    "# - 'ID' column contains probe identifiers matching the gene expression data indices\n",
    "# - 'gene_assignment' column contains gene symbol information\n",
    "\n",
    "print(\"Examining mapping columns:\")\n",
    "print(f\"First few IDs: {gene_annotation['ID'].head(3).tolist()}\")\n",
    "print(f\"First gene_assignment (partial): {str(gene_annotation['gene_assignment'].iloc[0])[:100]}...\")\n",
    "\n",
    "# 2. Get a gene mapping dataframe with the probe ID and gene symbol columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
    "\n",
    "# Print a sample of the mapping to verify\n",
    "print(\"\\nSample of gene mapping:\")\n",
    "print(gene_mapping.head(3))\n",
    "print(f\"Number of probes with gene mappings: {len(gene_mapping)}\")\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level data to gene expression data\n",
    "# This uses apply_gene_mapping function that handles many-to-many relationships\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print summary of the gene expression data after mapping\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(f\"Shape of gene data: {gene_data.shape}\")\n",
    "print(f\"Sample gene symbols: {list(gene_data.index[:5])}\")\n",
    "\n",
    "# Save the gene expression data to a CSV 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 expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890b3e33",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "196c82f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\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. Extract clinical features using the functions defined in step 2\n",
    "# First, let's load the clinical data again to ensure we have the latest version\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 using melanoma vs normal tissue as the binary trait\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_data, \n",
    "    trait=\"Melanoma\", \n",
    "    trait_row=trait_row, \n",
    "    convert_trait=convert_trait,\n",
    "    age_row=age_row,\n",
    "    convert_age=convert_age,\n",
    "    gender_row=gender_row,\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "# Transpose normalized gene data for linking\n",
    "gene_data_t = normalized_gene_data.T\n",
    "\n",
    "# Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, \"Melanoma\")\n",
    "print(f\"Data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine whether the trait and demographic features are biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, \"Melanoma\")\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=unbiased_linked_data,\n",
    "    note=\"Dataset contains gene expression data comparing melanoma (primary and metastatic) with normal tissue/nevi.\"\n",
    ")\n",
    "\n",
    "# 7. If the linked data is usable, save it\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_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\")"
   ]
  }
 ],
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