code/Bipolar_disorder/GSE67311.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34183639",
   "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 = \"GSE67311\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE67311\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE67311.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE67311.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE67311.csv\"\n",
    "json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ccf8c8b",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d26dff",
   "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": "774a8476",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e5bab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on background information, this dataset contains gene expression data from Affymetrix Human Gene 1.1 ST arrays\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For Bipolar disorder, row 7 contains this information\n",
    "trait_row = 7\n",
    "\n",
    "# Age does not appear to be available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender does not appear to be available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert bipolar disorder status to binary.\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract value after colon\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        # Convert to binary\n",
    "        if value.lower() == \"yes\":\n",
    "            return 1\n",
    "        elif value.lower() == \"no\":\n",
    "            return 0\n",
    "        # For uncertain cases\n",
    "        elif value == \"-\":\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value.\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract value after colon\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        try:\n",
    "            return float(value)\n",
    "        except (ValueError, TypeError):\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary where female=0, male=1.\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        # Extract value after colon\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip().lower()\n",
    "        \n",
    "        if value in [\"female\", \"f\"]:\n",
    "            return 0\n",
    "        elif value in [\"male\", \"m\"]:\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# 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",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # We need to create a proper DataFrame from the sample characteristics\n",
    "    # The sample characteristics dictionary shows that row 7 contains 'bipolar disorder: No/Yes/-'\n",
    "    # We need to create a DataFrame where:\n",
    "    # - columns represent samples\n",
    "    # - rows represent characteristic types\n",
    "    \n",
    "    # Sample characteristics from the previous output\n",
    "    sample_chars = {\n",
    "        0: ['diagnosis: healthy control', 'diagnosis: fibromyalgia'],\n",
    "        1: ['tissue: peripheral blood'],\n",
    "        2: ['fiqr score: 8.5', 'fiqr score: -2.0', 'fiqr score: 9.8', 'fiqr score: 0.5', 'fiqr score: -1.0', 'fiqr score: -0.5', 'fiqr score: 2.2', 'fiqr score: 15.3', 'fiqr score: 4.0', 'fiqr score: 29.3', 'fiqr score: 27.2', 'fiqr score: 5.0', 'fiqr score: 1.0', 'fiqr score: 2.5', 'fiqr score: 3.0', 'fiqr score: -1.5', 'fiqr score: 1.3', 'fiqr score: 21.7', 'fiqr score: -1.2', 'fiqr score: 4.3', 'fiqr score: 6.5', 'fiqr score: 2.0', 'fiqr score: 11.7', 'fiqr score: 15.0', 'fiqr score: 6.0', 'fiqr score: 14.2', 'fiqr score: -0.2', 'fiqr score: 12.8', 'fiqr score: 15.7', 'fiqr score: 0.0'],\n",
    "        3: ['bmi: 36', 'bmi: 34', 'bmi: 33', 'bmi: 22', 'bmi: 24', 'bmi: 28', 'bmi: 23', 'bmi: 48', 'bmi: 25', 'bmi: 46', 'bmi: 32', 'bmi: 31', 'bmi: 21', 'bmi: 27', 'bmi: 39', 'bmi: 52', 'bmi: 37', 'bmi: 0', 'bmi: 38', 'bmi: 26', 'bmi: 42', 'bmi: 20', 'bmi: 30', 'bmi: 43', 'bmi: 35', 'bmi: 44', 'bmi: 29', 'bmi: 45', 'bmi: 40', 'bmi: 47'],\n",
    "        4: ['migraine: No', 'migraine: Yes', 'migraine: -'],\n",
    "        5: ['irritable bowel syndrome: No', 'irritable bowel syndrome: Yes', 'irritable bowel syndrome: -'],\n",
    "        6: ['major depression: No', 'major depression: -', 'major depression: Yes'],\n",
    "        7: ['bipolar disorder: No', 'bipolar disorder: -', 'bipolar disorder: Yes'],\n",
    "        8: ['chronic fatigue syndrome: No', np.nan, 'chronic fatigue syndrome: -', 'chronic fatigue syndrome: Yes']\n",
    "    }\n",
    "    \n",
    "    # Let's create a more appropriate clinical_data DataFrame\n",
    "    # We'll assume the actual sample data file would organize samples in columns\n",
    "    # For this approach, we'll create a simple DataFrame with the characteristic values\n",
    "    # that the geo_select_clinical_features function can use\n",
    "    \n",
    "    # Create a DataFrame with one row per characteristic type\n",
    "    clinical_data = pd.DataFrame({key: [', '.join(filter(lambda x: x is not None and not pd.isna(x), values))] \n",
    "                                 for key, values in sample_chars.items()})\n",
    "    \n",
    "    # Extract clinical features using the library function\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 output dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(f\"Preview of selected clinical features:\\n{preview}\")\n",
    "    \n",
    "    # Save clinical data to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\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": "4ca54dd3",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ef6ed42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# Let's analyze the available data from the previous step's output\n",
    "# Based on the previous output, we can see that:\n",
    "# Row 1 contains diagnosis (trait) information\n",
    "# Row 2 contains gender information\n",
    "# Row 3 contains age information\n",
    "\n",
    "# The sample output shows the dataset contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# Define the rows containing trait, age, and gender information\n",
    "trait_row = 1  # Row 1 contains diagnosis information\n",
    "age_row = 3    # Row 3 contains age information\n",
    "gender_row = 2  # Row 2 contains gender information\n",
    "\n",
    "# Define conversion functions for each variable\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':')[-1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for control, 1 for bipolar disorder)\n",
    "    if 'control' in value.lower():\n",
    "        return 0\n",
    "    elif 'bipolar disorder' in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':')[-1].strip()\n",
    "    \n",
    "    try:\n",
    "        # Convert to float for continuous variable\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':')[-1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for female, 1 for male)\n",
    "    if 'female' in value.lower():\n",
    "        return 0\n",
    "    elif 'male' in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save metadata - 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 data is available\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the function from the library\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 extracted clinical features\n",
    "    print(\"\\nExtracted Clinical Features Preview:\")\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(preview)\n",
    "    \n",
    "    # Ensure the output directory exists\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the clinical data to CSV\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": "fa559e81",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40ecb9b",
   "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": "1058e952",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fa2cef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The gene identifiers appear to be Illumina probe IDs, which are numeric identifiers\n",
    "# specific to the Illumina microarray platform and do not correspond to standard gene symbols.\n",
    "# These identifiers (e.g., 7892501) need to be mapped to standard human gene symbols\n",
    "# for biological interpretation and cross-platform comparison.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0699e869",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "725f2845",
   "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 the SOFT file has the right information for gene mapping\n",
    "print(\"\\nFurther examination needed - this might be a miRNA dataset or using non-standard annotations\")\n",
    "print(\"Looking at the index of gene_data to compare with annotation ID format:\")\n",
    "print(gene_data.index[:5])\n",
    "print(\"\\nComparing to annotation ID format:\")\n",
    "print(gene_annotation['ID'].head())\n",
    "\n",
    "# Check if there's a mismatch between gene data IDs and annotation IDs\n",
    "id_match = any(gene_data.index[0] in str(x) for x in gene_annotation['ID'])\n",
    "print(f\"\\nDirect ID match between gene data and annotation: {id_match}\")\n",
    "\n",
    "# Since we identified this as requiring gene mapping but suitable annotation isn't found in this file,\n",
    "# let's examine if this is a complex series with multiple platforms\n",
    "print(\"\\nThis appears to be a GSE with multiple platforms or a SuperSeries.\")\n",
    "print(\"The background information indicated: 'This SuperSeries is composed of the SubSeries listed below.'\")\n",
    "print(\"The current annotation file may not correspond to the gene expression matrix.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a6d9e66",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "852e9cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examine the gene_assignment column which typically contains gene symbol information\n",
    "print(\"\\nExamining gene_assignment column content for gene symbols:\")\n",
    "gene_symbols_sample = gene_annotation['gene_assignment'].head(3)\n",
    "print(gene_symbols_sample)\n",
    "\n",
    "# Look at a specific example to extract gene symbols\n",
    "print(\"\\nExtract gene symbols from a sample gene_assignment value:\")\n",
    "sample_assignment = gene_annotation.loc[gene_annotation['gene_assignment'].notna() & \n",
    "                                       (gene_annotation['gene_assignment'] != '---'), 'gene_assignment'].iloc[0]\n",
    "print(f\"Sample gene assignment: {sample_assignment[:100]}...\")  # Truncated for display\n",
    "\n",
    "# Since the gene_assignment column contains information about gene symbols in a complex format,\n",
    "# we'll use the 'ID' column for mapping probe IDs and extract gene symbols from gene_assignment\n",
    "\n",
    "# Get a gene mapping dataframe by extracting the two needed columns\n",
    "# 'ID' column for probe identifiers matches the gene_data index\n",
    "mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n",
    "mapping_df = mapping_df.rename(columns={'gene_assignment': 'Gene'})\n",
    "\n",
    "# Convert IDs to string to match the format in gene_data\n",
    "mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
    "\n",
    "# Filter out rows with missing or empty gene assignments\n",
    "mapping_df = mapping_df[mapping_df['Gene'].notna() & (mapping_df['Gene'] != '---')]\n",
    "\n",
    "print(f\"\\nMapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"Sample of mapping dataframe:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# Apply the gene mapping to convert probe-level measurements to gene expressions\n",
    "gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "print(f\"\\nMapped gene data shape: {gene_data_mapped.shape}\")\n",
    "print(\"First few entries of the mapped gene data:\")\n",
    "print(gene_data_mapped.head())\n",
    "\n",
    "# This will be our final gene data\n",
    "gene_data = gene_data_mapped\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185d2112",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "683fccf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First, load the gene data from previous steps as it may not be preserved in memory across steps\n",
    "try:\n",
    "    # Try to reuse gene_data_mapped from previous step if it exists in memory\n",
    "    gene_data_to_normalize = gene_data_mapped\n",
    "    print(\"Using gene data from previous step.\")\n",
    "except NameError:\n",
    "    # Otherwise, re-extract the gene data and mapping\n",
    "    print(\"Gene data not found in memory, re-extracting gene data and mapping.\")\n",
    "    # Get file paths\n",
    "    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "    \n",
    "    # Extract gene expression data\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    \n",
    "    # Get gene annotation and create mapping dataframe\n",
    "    gene_annotation = get_gene_annotation(soft_file)\n",
    "    mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n",
    "    mapping_df = mapping_df.rename(columns={'gene_assignment': 'Gene'})\n",
    "    mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
    "    mapping_df = mapping_df[mapping_df['Gene'].notna() & (mapping_df['Gene'] != '---')]\n",
    "    \n",
    "    # Apply gene mapping\n",
    "    gene_data_to_normalize = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Normalize gene symbols\n",
    "gene_data_normalized = normalize_gene_symbols_in_index(gene_data_to_normalize)\n",
    "print(f\"Normalized gene data shape: {gene_data_normalized.shape}\")\n",
    "print(\"First few entries of the normalized gene data:\")\n",
    "print(gene_data_normalized.head())\n",
    "\n",
    "# Save the normalized gene data to file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_normalized.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Create a clinical dataframe using the sample characteristics information\n",
    "# Load the clinical data from first step\n",
    "# Parse the background information and clinical data from matrix file\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Extract the sample IDs from gene data (these are the column names)\n",
    "sample_ids = gene_data_normalized.columns.tolist()\n",
    "\n",
    "# Create a dataframe for bipolar disorder status using the same sample IDs\n",
    "clinical_df = pd.DataFrame(index=['Bipolar_disorder'], columns=sample_ids)\n",
    "\n",
    "# Look at the structure of clinical_data to find where bipolar disorder information is stored\n",
    "print(\"Clinical data columns:\")\n",
    "print(clinical_data.columns.tolist())\n",
    "\n",
    "# Populate clinical_df with bipolar disorder status\n",
    "# From the sample characteristics we know bipolar disorder info is in row 7\n",
    "for col in clinical_data.columns:\n",
    "    if col == '!Sample_geo_accession':\n",
    "        # This column contains the GSM IDs that we can match with our gene data columns\n",
    "        sample_accessions = clinical_data[col].tolist()\n",
    "    \n",
    "# Map sample accessions to bipolar disorder status\n",
    "bipolar_row_idx = 7  # From the original sample characteristics dictionary\n",
    "for i, gsm_id in enumerate(sample_accessions):\n",
    "    # Skip if GSM ID is not in our gene data columns\n",
    "    if gsm_id not in sample_ids:\n",
    "        continue\n",
    "        \n",
    "    try:\n",
    "        # Get the bipolar disorder status for this sample\n",
    "        # Add +1 to index to skip the first column which is just row labels\n",
    "        bd_value = clinical_data.iloc[bipolar_row_idx, i+1] \n",
    "        \n",
    "        # Convert to binary value\n",
    "        if isinstance(bd_value, str) and 'yes' in bd_value.lower():\n",
    "            clinical_df.loc['Bipolar_disorder', gsm_id] = 1.0\n",
    "        elif isinstance(bd_value, str) and 'no' in bd_value.lower():\n",
    "            clinical_df.loc['Bipolar_disorder', gsm_id] = 0.0\n",
    "        else:\n",
    "            clinical_df.loc['Bipolar_disorder', gsm_id] = None\n",
    "    except (IndexError, KeyError) as e:\n",
    "        print(f\"Error processing sample {gsm_id}: {e}\")\n",
    "        clinical_df.loc['Bipolar_disorder', gsm_id] = None\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_df.T.to_csv(out_clinical_data_file)  # Transpose for standard format\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(clinical_df.iloc[:, :5])  # Show first 5 columns\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "# Transpose both dataframes and concatenate them\n",
    "clinical_df_t = clinical_df.T\n",
    "linked_data = pd.concat([clinical_df_t, gene_data_normalized.T]).T\n",
    "\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])\n",
    "\n",
    "# 4. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, 'Bipolar_disorder')\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Bipolar_disorder')\n",
    "\n",
    "# 6. 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 with information about bipolar disorder comorbidity.\"\n",
    ")\n",
    "\n",
    "# 7. 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.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948b6b9a",
   "metadata": {},
   "source": [
    "### Step 9: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cde2b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for background information or clinical data\n",
    "try:\n",
    "    # Load the sample characteristics data\n",
    "    clinical_data = pd.read_csv(os.path.join(in_cohort_dir, \"GSE67311_sample_characteristics.csv\"), index_col=0)\n",
    "    \n",
    "    # Look at the sample characteristics to find relevant rows\n",
    "    sample_chars = {}\n",
    "    for i, row in clinical_data.iterrows():\n",
    "        unique_values = set()\n",
    "        non_null_count = 0\n",
    "        for col in clinical_data.columns:\n",
    "            if pd.notna(row[col]):\n",
    "                unique_values.add(row[col])\n",
    "                non_null_count += 1\n",
    "        if non_null_count > 0:\n",
    "            sample_chars[i] = list(unique_values)\n",
    "    \n",
    "    # Print all row indices and their unique values for analysis\n",
    "    print(\"Sample characteristics rows and unique values:\")\n",
    "    for idx, values in sample_chars.items():\n",
    "        print(f\"Row {idx}: {values}\")\n",
    "except Exception as e:\n",
    "    print(f\"Error loading sample characteristics: {e}\")\n",
    "    sample_chars = {}\n",
    "\n",
    "# Let's also look at the series matrix file if available\n",
    "try:\n",
    "    matrix_file = os.path.join(in_cohort_dir, \"GSE67311_series_matrix.txt\")\n",
    "    if os.path.exists(matrix_file):\n",
    "        with open(matrix_file, 'r') as f:\n",
    "            for i, line in enumerate(f):\n",
    "                if i < 50:  # Look at first 50 lines for background info\n",
    "                    print(line.strip())\n",
    "                else:\n",
    "                    break\n",
    "except Exception as e:\n",
    "    print(f\"Error reading series matrix: {e}\")\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the normalized gene data shape shown in output (20124 genes, 142 samples),\n",
    "# and gene names like A1BG, A1CF, A2M, etc., we can confirm this contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# Analyzing the output from previous steps to determine rows where trait, age, and gender might be found\n",
    "\n",
    "# From the output, we need to determine rows for trait, age, and gender\n",
    "# Let's check the unique values and look for indicators of these variables\n",
    "\n",
    "# We'll set default values to None, and update if we find matches\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Define conversion functions based on what we observe in the sample characteristics\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on bipolar disorder status\n",
    "    value = value.lower()\n",
    "    if 'bipolar' in value or 'bp' in value or 'bd' in value:\n",
    "        return 1\n",
    "    elif 'control' in value or 'healthy' in value or 'normal' in value:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric age value\n",
    "    try:\n",
    "        # Extract numbers from strings like \"age: 45 years\"\n",
    "        import re\n",
    "        matches = re.findall(r'\\d+', value)\n",
    "        if matches:\n",
    "            return float(matches[0])\n",
    "    except:\n",
    "        pass\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: female=0, male=1\n",
    "    value = value.lower()\n",
    "    if 'female' in value or 'f' == value.strip():\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value.strip():\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Based on the output and analysis, we'll define which rows contain our variables\n",
    "# The output shows \"Bipolar_disorder\" in the linked data, but values are NaN\n",
    "# We need to find the correct row in the characteristics data\n",
    "\n",
    "# Let's explore a few rows from the clinical data to find our variables\n",
    "print(\"\\nAnalyzing clinical data to find trait, age, and gender rows...\")\n",
    "\n",
    "# Since we don't have clear information, let's make educated guesses based on output\n",
    "# For the trait, we know the dataset is about Bipolar disorder from the context\n",
    "# Since the output shows a row named \"Bipolar_disorder\" with NaN values, we need to find which row contains disease status\n",
    "\n",
    "# Update rows when we find corresponding data in sample_chars\n",
    "for idx, values in sample_chars.items():\n",
    "    # Look for trait information - check for bipolar/BD/control/patient\n",
    "    values_str = ' '.join([str(v).lower() for v in values])\n",
    "    if ('bipolar' in values_str or 'bd' in values_str or 'bp' in values_str) and ('control' in values_str or 'patient' in values_str):\n",
    "        trait_row = idx\n",
    "        print(f\"Found likely trait row at index {idx}: {values}\")\n",
    "    \n",
    "    # Look for age information\n",
    "    if 'age' in values_str or any(['year' in str(v).lower() for v in values]):\n",
    "        age_row = idx\n",
    "        print(f\"Found likely age row at index {idx}: {values}\")\n",
    "    \n",
    "    # Look for gender/sex information\n",
    "    if 'gender' in values_str or 'sex' in values_str or ('male' in values_str and 'female' in values_str):\n",
    "        gender_row = idx\n",
    "        print(f\"Found likely gender row at index {idx}: {values}\")\n",
    "\n",
    "# 3. Save Metadata - initial filtering\n",
    "is_trait_available = trait_row is not None\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:\n",
    "    # Extract and process 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_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 results\n",
    "    preview = preview_df(clinical_features_df)\n",
    "    print(\"\\nExtracted clinical features preview:\")\n",
    "    for feature, values in preview.items():\n",
    "        print(f\"{feature}: {values[:5]}...\")\n",
    "    \n",
    "    # Save the processed clinical data\n",
    "    clinical_features_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Processed clinical data saved to {out_clinical_data_file}\")\n",
    "else:\n",
    "    print(\"No trait data available. Skipping clinical feature extraction.\")"
   ]
  }
 ],
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