{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c81a5a",
   "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 = \"Bipolar_disorder\"\n",
    "cohort = \"GSE53987\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE53987\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE53987.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE53987.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE53987.csv\"\n",
    "json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c972d1",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a798b1f9",
   "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": "f2a33812",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbab507d",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll implement a complete solution for extracting clinical features from the sample characteristics dictionary provided in the previous step.\n",
    "\n",
    "```python\n",
    "# 1. Gene Expression Data Availability\n",
    "# Check if the series contains gene expression data (vs miRNA/methylation)\n",
    "# The background information describes this as \"Microarray profiling\" with \"RNA isolated and hybridized\" \n",
    "# and U133_Plus2 Affymetrix chips, which indicates gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability for trait, age, and gender\n",
    "\n",
    "# Trait (Bipolar disorder) - from key 7: 'disease state'\n",
    "trait_row = 7  # Key for 'disease state' which includes bipolar disorder\n",
    "\n",
    "# Age - from key 0\n",
    "age_row = 0\n",
    "\n",
    "# Gender - from key 1\n",
    "gender_row = 1\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert trait value for bipolar disorder to binary:\n",
    "    1 for bipolar disorder, 0 for control/other disorders\n",
    "    \"\"\"\n",
    "    if value is None or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon\n",
    "    value = value.split(':', 1)[1].strip().lower()\n",
    "    \n",
    "    # 1 for bipolar disorder, 0 for others\n",
    "    if value == 'bipolar disorder':\n",
    "        return 1\n",
    "    elif value in ['control', 'major depressive disorder', 'schizophrenia']:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age value to continuous (integer)\n",
    "    \"\"\"\n",
    "    if value is None or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon\n",
    "    value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to convert to integer\n",
    "    try:\n",
    "        return int(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender value to binary:\n",
    "    0 for female (F), 1 for male (M)\n",
    "    \"\"\"\n",
    "    if value is None or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon\n",
    "    value = value.split(':', 1)[1].strip().upper()\n",
    "    \n",
    "    if value == 'F':\n",
    "        return 0\n",
    "    elif value == 'M':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata - initial validation\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info (initial validation)\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "# If trait_row is not None, extract and process clinical features\n",
    "if trait_row is not None:\n",
    "    # Create a DataFrame from the sample characteristics dictionary provided in the task\n",
    "    sample_chars = {\n",
    "        0: ['age: 52', 'age: 50', 'age: 28', 'age: 55', 'age: 58', 'age: 49', 'age: 42', 'age: 43', 'age: 40', 'age: 39', \n",
    "            'age: 45', 'age: 65', 'age: 51', 'age: 48', 'age: 36', 'age: 22', 'age: 41', 'age: 68', 'age: 53', 'age: 26', \n",
    "            'age: 62', 'age: 29', 'age: 54', 'age: 44', 'age: 47', 'age: 59', 'age: 34', 'age: 25', 'age: 46', 'age: 37'],\n",
    "        1: ['gender: M', 'gender: F'],\n",
    "        2: ['race: W', 'race: B'],\n",
    "        3: ['pmi: 23.5', 'pmi: 11.7', 'pmi: 22.3', 'pmi: 17.5', 'pmi: 27.7', 'pmi: 27.4', 'pmi: 21.5', 'pmi: 31.2', \n",
    "            'pmi: 31.9', 'pmi: 12.1', 'pmi: 18.5', 'pmi: 22.2', 'pmi: 27.2', 'pmi: 12.5', 'pmi: 8.9', 'pmi: 24.2', \n",
    "            'pmi: 18.1', 'pmi: 7.8', 'pmi: 14.5', 'pmi: 28', 'pmi: 20.1', 'pmi: 22.6', 'pmi: 22.7', 'pmi: 16.6', \n",
    "            'pmi: 15.4', 'pmi: 21.2', 'pmi: 21.68', 'pmi: 24.5', 'pmi: 13.8', 'pmi: 11.8'],\n",
    "        4: ['ph: 6.7', 'ph: 6.4', 'ph: 6.3', 'ph: 6.8', 'ph: 6.2', 'ph: 6.5', 'ph: 7.1', 'ph: 6.6', 'ph: 6.9', 'ph: 6.1', \n",
    "            'ph: 7.3', 'ph: 5.97', 'ph: 6.35', 'ph: 6.73', 'ph: 7.14', 'ph: 6.63', 'ph: 6.61', 'ph: 6.23', 'ph: 6.19', \n",
    "            'ph: 6.27', 'ph: 6.58', 'ph: 6.07', 'ph: 6.22', 'ph: 6.56', 'ph: 6.68', 'ph: 6.18', 'ph: 6.25'],\n",
    "        5: ['rin: 6.3', 'rin: 6.8', 'rin: 7.7', 'rin: 7.6', 'rin: 7', 'rin: 8.2', 'rin: 5.6', 'rin: 7.4', 'rin: 6.5', \n",
    "            'rin: 7.9', 'rin: 8.1', 'rin: 6.6', 'rin: 7.8', 'rin: 7.2', 'rin: 8', 'rin: 7.1', 'rin: 8.5', 'rin: 7.3', \n",
    "            'rin: 6.1', 'rin: 7.5', 'rin: 6.2', 'rin: 5.5', 'rin: 8.4', 'rin: 6', 'rin: 6.9', 'rin: 6.7', 'rin: 6.4', \n",
    "            'rin: 8.6', 'rin: 8.3', 'rin: 8.7'],\n",
    "        6: ['tissue: hippocampus', 'tissue: Pre-frontal cortex (BA46)', 'tissue: Associative striatum'],\n",
    "        7: ['disease state: bipolar disorder', 'disease state: control', 'disease state: major depressive disorder', \n",
    "            'disease state: schizophrenia']\n",
    "    }\n",
    "    \n",
    "    # Convert dictionary to DataFrame\n",
    "    clinical_data = pd.DataFrame(sample_chars)\n",
    "    \n",
    "    # Extract clinical features\n",
    "    clinical_features_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9bf9b7",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e44c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import json\n",
    "import gzip\n",
    "import re\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# First, let's analyze the dataset by reading the compressed GEO series matrix file\n",
    "raw_data_path = os.path.join(in_cohort_dir, \"GSE53987_series_matrix.txt.gz\")\n",
    "\n",
    "# Read the compressed file and extract sample characteristics\n",
    "sample_characteristics = {}\n",
    "platform_info = \"\"\n",
    "is_gene_available = False\n",
    "\n",
    "try:\n",
    "    with gzip.open(raw_data_path, 'rt') as file:\n",
    "        for line in file:\n",
    "            if line.startswith('!Sample_characteristics_ch1'):\n",
    "                parts = line.strip().split('\\t')\n",
    "                for i, part in enumerate(parts[1:], 1):\n",
    "                    if ':' in part:\n",
    "                        key, value = part.split(':', 1)\n",
    "                        key = key.strip()\n",
    "                        if key not in sample_characteristics:\n",
    "                            sample_characteristics[key] = []\n",
    "                        sample_characteristics[key].append(value.strip())\n",
    "            # Check for platform to determine if gene expression data is available\n",
    "            elif line.startswith('!Platform_technology'):\n",
    "                platform_info = line.strip()\n",
    "            # If we see gene expression related lines, mark as available\n",
    "            elif line.startswith('!platform_table_begin') or 'gene' in line.lower() or 'expression' in line.lower():\n",
    "                is_gene_available = True\n",
    "            # Break after reading a significant portion to improve efficiency\n",
    "            elif line.startswith('!series_matrix_table_begin'):\n",
    "                # We've reached the data matrix, stop reading\n",
    "                break\n",
    "                \n",
    "    print(\"Sample characteristics found:\")\n",
    "    for key, values in sample_characteristics.items():\n",
    "        unique_values = set(values)\n",
    "        print(f\"{key}: {unique_values}\")\n",
    "    print(f\"Platform info: {platform_info}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error reading series matrix file: {e}\")\n",
    "    sample_characteristics = {}\n",
    "    is_gene_available = False\n",
    "\n",
    "# Parse clinical data from sample characteristics\n",
    "clinical_data = None\n",
    "if sample_characteristics:\n",
    "    # Convert sample characteristics to dataframe for geo_select_clinical_features function\n",
    "    clinical_rows = []\n",
    "    for key, values in sample_characteristics.items():\n",
    "        row = [key] + values\n",
    "        clinical_rows.append(row)\n",
    "    \n",
    "    # Create dataframe with header being sample IDs\n",
    "    sample_ids = [f\"Sample_{i+1}\" for i in range(len(list(sample_characteristics.values())[0]))]\n",
    "    clinical_data = pd.DataFrame(clinical_rows, columns=['Feature'] + sample_ids)\n",
    "    print(\"\\nClinical data preview:\")\n",
    "    print(clinical_data.head())\n",
    "\n",
    "# Determine trait, age, and gender rows based on the sample characteristics\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Find trait row\n",
    "disease_keywords = ['diagnosis', 'disease', 'disorder', 'condition', 'group', 'subject', 'bipolar']\n",
    "for i, feature in enumerate(clinical_data['Feature'] if clinical_data is not None else []):\n",
    "    feature_lower = feature.lower()\n",
    "    if any(keyword in feature_lower for keyword in disease_keywords):\n",
    "        # Check if there's more than one unique value (excluding None, nan, etc.)\n",
    "        unique_values = set(v for v in clinical_data.iloc[i, 1:] if v and not pd.isna(v))\n",
    "        if len(unique_values) > 1:\n",
    "            trait_row = i\n",
    "            print(f\"Found trait row: {i} - {feature}\")\n",
    "            print(f\"Unique values: {unique_values}\")\n",
    "            break\n",
    "\n",
    "# Find age row\n",
    "age_keywords = ['age', 'years']\n",
    "for i, feature in enumerate(clinical_data['Feature'] if clinical_data is not None else []):\n",
    "    feature_lower = feature.lower()\n",
    "    if any(keyword in feature_lower for keyword in age_keywords):\n",
    "        # Check if there's variation in age values\n",
    "        unique_values = set(v for v in clinical_data.iloc[i, 1:] if v and not pd.isna(v))\n",
    "        if len(unique_values) > 1:\n",
    "            age_row = i\n",
    "            print(f\"Found age row: {i} - {feature}\")\n",
    "            print(f\"Sample unique values: {list(unique_values)[:5]}\")\n",
    "            break\n",
    "\n",
    "# Find gender row\n",
    "gender_keywords = ['gender', 'sex']\n",
    "for i, feature in enumerate(clinical_data['Feature'] if clinical_data is not None else []):\n",
    "    feature_lower = feature.lower()\n",
    "    if any(keyword in feature_lower for keyword in gender_keywords):\n",
    "        # Check if there's variation in gender values\n",
    "        unique_values = set(v for v in clinical_data.iloc[i, 1:] if v and not pd.isna(v))\n",
    "        if len(unique_values) > 1:\n",
    "            gender_row = i\n",
    "            print(f\"Found gender row: {i} - {feature}\")\n",
    "            print(f\"Unique values: {unique_values}\")\n",
    "            break\n",
    "\n",
    "# Define conversion functions based on identified rows\n",
    "def convert_trait(value):\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value_lower = value.lower()\n",
    "    if 'bipolar' in value_lower or 'bd' in value_lower or 'bpd' in value_lower:\n",
    "        return 1  # Has bipolar disorder\n",
    "    elif 'control' in value_lower or 'normal' in value_lower or 'healthy' in value_lower or 'con' in value_lower:\n",
    "        return 0  # Control\n",
    "    else:\n",
    "        # If not clear, return None\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Try to extract numbers\n",
    "    numbers = re.findall(r'\\d+\\.?\\d*', str(value))\n",
    "    if numbers:\n",
    "        try:\n",
    "            return float(numbers[0])\n",
    "        except:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value_lower = value.lower()\n",
    "    if 'female' in value_lower or 'f' == value_lower:\n",
    "        return 0\n",
    "    elif 'male' in value_lower or 'm' == value_lower:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Check if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info for initial filtering\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# Extract clinical features if trait_row is not None and clinical_data exists\n",
    "if is_trait_available and clinical_data is not None:\n",
    "    # Use the provided function to select clinical features\n",
    "    clinical_features = geo_select_clinical_features(\n",
    "        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 features\n",
    "    print(\"\\nPreview of extracted clinical features:\")\n",
    "    print(preview_df(clinical_features))\n",
    "    \n",
    "    # Save the clinical features\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 features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4959176",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f577bef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13ba2b8",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae7a8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The gene/probe identifiers (e.g., '1007_s_at', '1053_at') appear to be Affymetrix probe IDs \n",
    "# rather than standard human gene symbols (which would be like BRCA1, TP53, etc.)\n",
    "# These probe IDs need to be mapped to human gene symbols for proper analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f4b9d0",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2da1c90a",
   "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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Check if there are any columns that might contain gene information\n",
    "sample_row = gene_annotation.iloc[0].to_dict()\n",
    "print(\"\\nFirst row as dictionary:\")\n",
    "for col, value in sample_row.items():\n",
    "    print(f\"{col}: {value}\")\n",
    "\n",
    "# Check if IDs in gene_data match IDs in annotation\n",
    "print(\"\\nComparing gene data IDs with annotation IDs:\")\n",
    "print(\"First 5 gene data IDs:\", gene_data.index[:5].tolist())\n",
    "print(\"First 5 annotation IDs:\", gene_annotation['ID'].head().tolist())\n",
    "\n",
    "# Properly check for exact ID matches between gene data and annotation\n",
    "gene_data_ids = set(gene_data.index)\n",
    "annotation_ids = set(gene_annotation['ID'].astype(str))\n",
    "matching_ids = gene_data_ids.intersection(annotation_ids)\n",
    "id_match_percentage = len(matching_ids) / len(gene_data_ids) * 100 if len(gene_data_ids) > 0 else 0\n",
    "\n",
    "print(f\"\\nExact ID match between gene data and annotation:\")\n",
    "print(f\"Matching IDs: {len(matching_ids)} out of {len(gene_data_ids)} ({id_match_percentage:.2f}%)\")\n",
    "\n",
    "# Check which columns might contain gene symbols for mapping\n",
    "potential_gene_symbol_cols = [col for col in gene_annotation.columns \n",
    "                             if any(term in col.upper() for term in ['GENE', 'SYMBOL', 'NAME'])]\n",
    "print(f\"\\nPotential columns for gene symbols: {potential_gene_symbol_cols}\")\n",
    "\n",
    "# Check if the identified columns contain non-null values\n",
    "for col in potential_gene_symbol_cols:\n",
    "    non_null_count = gene_annotation[col].notnull().sum()\n",
    "    non_null_percent = non_null_count / len(gene_annotation) * 100\n",
    "    print(f\"Column '{col}': {non_null_count} non-null values ({non_null_percent:.2f}%)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fef23a0",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef62504a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify which columns in gene_annotation hold the probe IDs and gene symbols\n",
    "# From previous analysis, 'ID' contains the Affymetrix probe IDs and 'Gene Symbol' contains the gene symbols\n",
    "prob_col = 'ID'\n",
    "gene_col = 'Gene Symbol'\n",
    "\n",
    "print(f\"Using {prob_col} as probe identifier column and {gene_col} as gene symbol column\")\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting these two columns\n",
    "mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "print(f\"Mapping data shape: {mapping_data.shape}\")\n",
    "print(f\"Sample of mapping data (first 5 rows):\")\n",
    "print(mapping_data.head())\n",
    "\n",
    "# Check how many probes map to multiple genes\n",
    "mapping_data['Gene'] = mapping_data['Gene'].astype(str)\n",
    "multi_gene_probes = mapping_data[mapping_data['Gene'].str.contains('///')]\n",
    "print(f\"\\nNumber of probes mapping to multiple genes: {len(multi_gene_probes)}\")\n",
    "print(f\"Sample of probes with multiple genes (first 5):\")\n",
    "if len(multi_gene_probes) > 0:\n",
    "    print(multi_gene_probes.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene expression data\n",
    "# The library function apply_gene_mapping handles the many-to-many mapping\n",
    "print(\"\\nConverting probe-level measurements to gene expression data...\")\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(f\"First 10 gene symbols after mapping:\")\n",
    "print(gene_data.index[:10].tolist())\n",
    "\n",
    "# Save the gene expression data to 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": "d2ba04cc",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c103e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. We already normalized gene symbols in the gene expression data in step 6\n",
    "# Save the normalized gene data to 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",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "# First, let's extract the clinical features properly\n",
    "# Based on the sample characteristics dictionary from step 1:\n",
    "# {0: ['tissue: Blood'], 1: ['illness: Major Depressive Disorder'], 2: ['age: 16', 'age: 13', 'age: 12', 'age: 14', 'age: 17', 'age: 15'], 3: ['Sex: female', 'Sex: male']}\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert depression status to binary format.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    value = value.split(\": \")[-1].strip().lower()\n",
    "    if \"major depressive disorder\" in value:\n",
    "        return 1  # Has depression\n",
    "    return 0  # Control/no depression\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to continuous format.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    value = value.split(\": \")[-1].strip()\n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender data to binary format (0 for female, 1 for male).\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    value = value.split(\": \")[-1].strip().lower()\n",
    "    if \"female\" in value:\n",
    "        return 0\n",
    "    elif \"male\" in value:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Get clinical data using the correct row index identified in step 1\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=1,  # Using row 1 for depression status (major depressive disorder)\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=2,    # Age data is in row 2\n",
    "    convert_age=convert_age,\n",
    "    gender_row=3, # Gender data is in row 3\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Save clinical data for future reference\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",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
    "\n",
    "# 3. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Validate 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_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from blood samples of children and adolescents with Major Depressive Disorder, before and after Fluoxetine treatment.\"\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for analysis. No linked data file saved.\")"
   ]
  }
 ],
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 "nbformat": 4,
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}