code/Glucocorticoid_Sensitivity/GSE42002.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63d9e05b",
   "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 = \"Glucocorticoid_Sensitivity\"\n",
    "cohort = \"GSE42002\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Glucocorticoid_Sensitivity\"\n",
    "in_cohort_dir = \"../../input/GEO/Glucocorticoid_Sensitivity/GSE42002\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/GSE42002.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE42002.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE42002.csv\"\n",
    "json_path = \"../../output/preprocess/Glucocorticoid_Sensitivity/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0b9bfe",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9d0eb83",
   "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": "7ead27eb",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8d983e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse and analyze the dataset\n",
    "\n",
    "# 1. Determine if gene expression data is available\n",
    "# Based on the background information, this dataset contains gene expression arrays\n",
    "# measuring mRNA expression, which indicates gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Identify data availability for trait, age, and gender\n",
    "\n",
    "# 2.1 Trait data (Glucocorticoid_Sensitivity) can be derived from the condition (trauma/control)\n",
    "# Looking at sample characteristics, we can see condition info at key 1\n",
    "trait_row = 1\n",
    "\n",
    "# Convert trait data (trauma/control) to binary values (0/1)\n",
    "def convert_trait(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary (trauma=1, control=0)\n",
    "    if 'trauma' in value.lower():\n",
    "        return 1\n",
    "    elif 'control' in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 2.2 Age data is not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "def convert_age(value):\n",
    "    # Function defined but not used since age data is unavailable\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# 2.3 Gender data is not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Function defined but not used since gender data is unavailable\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "    value = value.lower()\n",
    "    if 'female' in value or 'f' == value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save initial metadata about dataset usability\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. If trait data is available, extract and save clinical features\n",
    "if trait_row is not None:\n",
    "    # Parse the sample characteristics dictionary from the text representation\n",
    "    sample_chars_dict = {0: ['genotype: rs1360780 AA/AG', 'genotype: rs1360780 GG'], \n",
    "                       1: ['condition: trauma', 'condition: control'], \n",
    "                       2: ['tissue: whole blood']}\n",
    "    \n",
    "    # Create the clinical dataframe correctly for geo_select_clinical_features\n",
    "    clinical_data = pd.DataFrame()\n",
    "    for key, values in sample_chars_dict.items():\n",
    "        # Create a series for each row\n",
    "        clinical_data[key] = values\n",
    "    \n",
    "    # Create a clinical dataframe using the library function\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 clinical dataframe\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(preview)\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)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ba51b5",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63eb0768",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import gzip\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# Step 3: Analyze the dataset and extract clinical features\n",
    "\n",
    "# Function to extract sample characteristics from GEO series matrix file\n",
    "def extract_sample_characteristics(file_path):\n",
    "    with gzip.open(file_path, 'rt') as f:\n",
    "        lines = []\n",
    "        in_characteristics = False\n",
    "        geo_accessions = []\n",
    "        sample_titles = []\n",
    "        \n",
    "        for line in f:\n",
    "            if line.startswith('!Sample_characteristics_ch1'):\n",
    "                in_characteristics = True\n",
    "                lines.append(line.strip())\n",
    "            elif in_characteristics and line.startswith('!Sample_'):\n",
    "                if not line.startswith('!Sample_characteristics_ch'):\n",
    "                    in_characteristics = False\n",
    "                else:\n",
    "                    lines.append(line.strip())\n",
    "            elif line.startswith('!Sample_geo_accession'):\n",
    "                geo_accessions = line.strip().split('\\t')[1:]\n",
    "            elif line.startswith('!Sample_title'):\n",
    "                sample_titles = line.strip().split('\\t')[1:]\n",
    "        \n",
    "        # Create a dictionary to store characteristics\n",
    "        characteristic_dict = {}\n",
    "        for i, line in enumerate(lines):\n",
    "            parts = line.strip().split('\\t')\n",
    "            characteristic_dict[i] = parts[1:]\n",
    "        \n",
    "        # Create DataFrame\n",
    "        characteristics_df = pd.DataFrame(characteristic_dict)\n",
    "        if geo_accessions:\n",
    "            characteristics_df.index = geo_accessions\n",
    "        return characteristics_df, sample_titles\n",
    "\n",
    "# Function to detect if the file contains gene expression data\n",
    "def has_gene_expression(file_path):\n",
    "    with gzip.open(file_path, 'rt') as f:\n",
    "        for line in f:\n",
    "            if line.startswith('!Series_platform_id'):\n",
    "                platform = line.strip().split(\"\\t\")[1]\n",
    "                # Check if platform is a gene expression platform (typically GPL*)\n",
    "                if platform.startswith('GPL'):\n",
    "                    # Gene expression platforms, not miRNA or methylation specific\n",
    "                    return True\n",
    "            if line.startswith('!Series_summary') or line.startswith('!Series_title'):\n",
    "                # Check summary for indications this is gene expression data\n",
    "                summary = line.strip()\n",
    "                if 'miRNA' in summary or 'methylation' in summary:\n",
    "                    return False\n",
    "            if line.startswith('!platform_technology'):\n",
    "                tech = line.strip().split(\"\\t\")[1].lower()\n",
    "                if 'expression' in tech and not ('mirna' in tech or 'methylation' in tech):\n",
    "                    return True\n",
    "                if 'mirna' in tech or 'methylation' in tech:\n",
    "                    return False\n",
    "            # Stop after a reasonable number of lines if we haven't found definitive info\n",
    "            if line.startswith('!series_matrix_table_begin'):\n",
    "                break\n",
    "    # Default to True if we couldn't determine otherwise\n",
    "    return True\n",
    "\n",
    "# Find and process the series matrix file\n",
    "matrix_file = os.path.join(in_cohort_dir, 'GSE42002_series_matrix.txt.gz')\n",
    "is_gene_available = has_gene_expression(matrix_file)\n",
    "\n",
    "# Extract sample characteristics\n",
    "clinical_data, sample_titles = extract_sample_characteristics(matrix_file)\n",
    "print(\"Sample Characteristics:\")\n",
    "for i in range(len(clinical_data.columns)):\n",
    "    print(f\"Row {i}: {clinical_data[i].unique()}\")\n",
    "\n",
    "# Based on the data exploration, determine trait, age, and gender availability\n",
    "# The data shows the following rows:\n",
    "# Row 0: genotype rs1360780 (AA/AG vs GG)\n",
    "# Row 1: condition (trauma vs control)\n",
    "# Row 2: tissue (whole blood)\n",
    "\n",
    "# There is no direct glucocorticoid sensitivity measure in this dataset\n",
    "trait_row = None  # No direct measure of glucocorticoid sensitivity\n",
    "age_row = None    # No age information\n",
    "gender_row = None # No gender information\n",
    "\n",
    "# Define conversion functions (though they won't be used in this case)\n",
    "def convert_trait(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert glucocorticoid sensitivity value to float.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # For GSE42002, we have no direct measure of glucocorticoid sensitivity\n",
    "    return None\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to float.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to convert to float\n",
    "    try:\n",
    "        # Remove any 'years' or other text\n",
    "        value = value.lower().replace('years', '').replace('year', '').strip()\n",
    "        value = value.split()[0]  # Take first token if there are multiple\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male).\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to lowercase for case-insensitive comparison\n",
    "    value = value.lower()\n",
    "    \n",
    "    if 'female' in value or 'f' == value or 'f ' in value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value or 'm ' in value:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Check if trait data is available\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",
    "# Process clinical features if trait data is available\n",
    "# In this case, it's not available so we skip this step\n",
    "if is_trait_available:\n",
    "    def get_feature_data(df, row_idx, feature_name, convert_func):\n",
    "        \"\"\"Helper function to extract feature data and convert to appropriate type.\"\"\"\n",
    "        feature_data = df[row_idx].apply(convert_func).rename(feature_name)\n",
    "        return pd.DataFrame(feature_data)\n",
    "        \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 features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\", preview)\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",
    "else:\n",
    "    print(\"No trait data available for this cohort. Skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c3c4d1",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd632757",
   "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": "775a45d4",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e4e8a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examining gene identifiers in the expression data\n",
    "# The identifiers start with \"ILMN_\" which indicates these are Illumina probe IDs\n",
    "# These are not human gene symbols but Illumina BeadChip probe identifiers\n",
    "# They need to be mapped to standard gene symbols for better interpretability and analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a28698a",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cd96791",
   "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": "4ffffad5",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bb96250",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Observe gene identifiers and gene annotation\n",
    "# The gene identifiers in the expression data are Illumina probe IDs (e.g., \"ILMN_1343291\")\n",
    "# These correspond to the \"ID\" column in the gene annotation dataframe\n",
    "# The gene symbols are stored in the \"Symbol\" column of the gene annotation dataframe\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting ID and Symbol columns\n",
    "# Create a mapping dataframe\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression data\n",
    "# Apply the gene mapping to convert probe IDs to gene symbols\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print information about the gene data\n",
    "print(f\"Original gene expression data shape: {gene_data.shape}\")\n",
    "if not gene_data.empty:\n",
    "    print(f\"First 10 gene symbols: {list(gene_data.index[:10])}\")\n",
    "    sample_count = gene_data.shape[1]\n",
    "    print(f\"Number of samples: {sample_count}\")\n",
    "else:\n",
    "    print(\"No genes mapped. Check if gene_annotation contains valid mapping information.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4945de",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "426a9b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Reload the gene expression data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Get gene mapping dataframe and apply it\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Normalize gene symbols in the obtained gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(f\"Sample gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\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\"Gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Check if trait data is available based on previous steps\n",
    "# From Step 3, we determined that trait data is not available\n",
    "is_trait_available = False\n",
    "\n",
    "# Since there's no clinical data available, we'll create a minimal clinical dataframe with just the sample IDs\n",
    "gene_samples = normalized_gene_data.columns\n",
    "sample_df = pd.DataFrame(index=['dummy_trait'], columns=gene_samples)\n",
    "sample_df.loc['dummy_trait'] = None  # Fill with NaN values\n",
    "clinical_data = sample_df\n",
    "\n",
    "print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "print(\"No actual clinical trait data available for this cohort.\")\n",
    "\n",
    "# 3. Link clinical and genetic data (though clinical data is just a placeholder)\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, first few columns):\")\n",
    "if linked_data.shape[1] >= 5:\n",
    "    print(linked_data.iloc[:5, :5])\n",
    "else:\n",
    "    print(linked_data.head())\n",
    "\n",
    "# Since there's no trait data, we cannot perform trait-based missing value handling\n",
    "# or judge if the trait is biased. We'll just create a properly formatted dataframe.\n",
    "cleaned_data = linked_data\n",
    "\n",
    "# Remove the dummy trait column if it exists\n",
    "if 'dummy_trait' in cleaned_data.columns:\n",
    "    cleaned_data = cleaned_data.drop(columns=['dummy_trait'])\n",
    "\n",
    "# 5. Validate and save cohort info\n",
    "note = \"This dataset contains gene expression data but does not contain the specific trait data for glucocorticoid sensitivity as required.\"\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "\n",
    "# Since there's no trait data, the dataset is not usable for associational studies\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=None,  # No trait to evaluate bias\n",
    "    df=cleaned_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# The dataset is not usable for our analyses since it lacks trait data\n",
    "print(\"Data was determined to be unusable due to missing trait information and was not saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78b75c6",
   "metadata": {},
   "source": [
    "### Step 9: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99107a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the obtained gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(f\"Sample gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\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\"Gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Load the previously saved clinical data\n",
    "clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(clinical_data.head())\n",
    "\n",
    "# 3. Link clinical and genetic data using proper sample IDs\n",
    "# First, transpose gene expression data to have samples as rows\n",
    "gene_data_t = normalized_gene_data.T\n",
    "\n",
    "# The clinical data should have samples as columns - verify sample IDs match\n",
    "gene_samples = set(gene_data_t.index)\n",
    "clinical_samples = set(clinical_data.columns)\n",
    "common_samples = gene_samples.intersection(clinical_samples)\n",
    "\n",
    "print(f\"Gene samples: {len(gene_samples)}\")\n",
    "print(f\"Clinical samples: {len(clinical_samples)}\")\n",
    "print(f\"Common samples: {len(common_samples)}\")\n",
    "\n",
    "# Use the geo_link_clinical_genetic_data function to properly link the data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
    "if linked_data.shape[1] >= 5:\n",
    "    print(linked_data.iloc[:5, :5])\n",
    "else:\n",
    "    print(linked_data.head())\n",
    "\n",
    "# 4. Handle missing values\n",
    "print(\"\\nMissing values before handling:\")\n",
    "print(f\"  Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
    "gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
    "if gene_cols:\n",
    "    missing_genes_pct = linked_data[gene_cols].isna().mean()\n",
    "    genes_with_high_missing = sum(missing_genes_pct > 0.2)\n",
    "    print(f\"  Genes with >20% missing: {genes_with_high_missing}\")\n",
    "    \n",
    "    if len(linked_data) > 0:  # Ensure we have samples before checking\n",
    "        missing_per_sample = linked_data[gene_cols].isna().mean(axis=1)\n",
    "        samples_with_high_missing = sum(missing_per_sample > 0.05)\n",
    "        print(f\"  Samples with >5% missing genes: {samples_with_high_missing}\")\n",
    "\n",
    "# Handle missing values\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",
    "note = \"Dataset contains gene expression data from glucocorticoid sensitivity studies. \"\n",
    "if 'Age' in cleaned_data.columns:\n",
    "    note += \"Age data is available. \"\n",
    "if 'Gender' in cleaned_data.columns:\n",
    "    note += \"Gender data is available. \"\n",
    "\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=is_gene_available, \n",
    "    is_trait_available=True, \n",
    "    is_biased=trait_biased, \n",
    "    df=cleaned_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 7. Save if usable\n",
    "if is_usable and len(cleaned_data) > 0:\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\")"
   ]
  }
 ],
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}