code/Cystic_Fibrosis/GSE53543.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9cae4a6",
   "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 = \"Cystic_Fibrosis\"\n",
    "cohort = \"GSE53543\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE53543\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE53543.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE53543.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE53543.csv\"\n",
    "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e377f77e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73aec0a9",
   "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": "7c8dcf64",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fc87145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This dataset appears to contain gene expression data based on the series title and summary\n",
    "is_gene_available = True\n",
    "\n",
    "# Checking trait data availability\n",
    "# From the Sample Characteristics Dictionary, we can see sample group (row 2) indicates \n",
    "# RV infection status which is the trait we'll analyze in this study\n",
    "trait_row = 2  # 'sample group: Uninfected', 'sample group: RV_infected'\n",
    "\n",
    "# Define conversion function for trait - RV infection status\n",
    "def convert_trait(value):\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "        if 'uninfected' in value.lower():\n",
    "            return 0  # Uninfected\n",
    "        elif 'rv_infected' in value.lower() or 'rhinovirus' in value.lower():\n",
    "            return 1  # RV infected\n",
    "    return None\n",
    "\n",
    "# Check for age data availability\n",
    "# Age data is not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "def convert_age(value):\n",
    "    # This function won't be used but is defined for completeness\n",
    "    if value and ':' in value:\n",
    "        age_str = value.split(':', 1)[1].strip()\n",
    "        try:\n",
    "            return float(age_str)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    return None\n",
    "\n",
    "# Check for gender data availability\n",
    "gender_row = 1  # 'gender: Female', 'gender: Male'\n",
    "\n",
    "def convert_gender(value):\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        gender = value.split(':', 1)[1].strip().lower()\n",
    "        if 'female' in gender:\n",
    "            return 0\n",
    "        elif 'male' in gender:\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save metadata using validate_and_save_cohort_info\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",
    "# Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Assuming clinical_data should be provided from a previous step\n",
    "    # For now, we'll create a simple representation that matches what the function expects\n",
    "    # This is a placeholder that should be replaced with the actual clinical_data\n",
    "    \n",
    "    # Create a sample DataFrame that matches the expected format for geo_select_clinical_features\n",
    "    sample_chars = {\n",
    "        0: ['subject id: FS119', 'subject id: FS114', 'subject id: FS64', 'subject id: FS98', 'subject id: FS156', 'subject id: FS65', 'subject id: FS144', 'subject id: FS133', 'subject id: FS95', 'subject id: FS161', 'subject id: FS106', 'subject id: FS52', 'subject id: FS159', 'subject id: FS142', 'subject id: FS73', 'subject id: FS118', 'subject id: FS101', 'subject id: FS67', 'subject id: FS88', 'subject id: FS83', 'subject id: FS110', 'subject id: FS82', 'subject id: FS76', 'subject id: FS108', 'subject id: FS107', 'subject id: FS134', 'subject id: FS115', 'subject id: FS84', 'subject id: FS136', 'subject id: FS140'],\n",
    "        1: ['gender: Female', 'gender: Male'],\n",
    "        2: ['sample group: Uninfected', 'sample group: RV_infected'],\n",
    "        3: ['cell type: peripheral blood mononuclear cells'],\n",
    "        4: ['treated with: media alone for 24 hours', 'treated with: media containing rhinovirus (RV16) for 24 hrs']\n",
    "    }\n",
    "    \n",
    "    # Instead of trying to recreate the data, we'll use the sample_chars dictionary directly\n",
    "    # and assume the get_feature_data function in geo_select_clinical_features can handle this format\n",
    "    clinical_data = sample_chars\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of clinical data:\")\n",
    "    print(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, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacd1e1b",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30141074",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll fix the syntax issues and complete the required implementation:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import glob\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# First, check what files are available in the cohort directory\n",
    "print(f\"Checking files in: {in_cohort_dir}\")\n",
    "available_files = glob.glob(f\"{in_cohort_dir}/*\")\n",
    "print(\"Available files:\", available_files)\n",
    "\n",
    "# Look for the series matrix file which typically contains both expression data and sample info\n",
    "series_matrix_files = [f for f in available_files if 'series_matrix' in f.lower()]\n",
    "if series_matrix_files:\n",
    "    print(f\"Found series matrix file: {series_matrix_files[0]}\")\n",
    "    # Read the series matrix file\n",
    "    with open(series_matrix_files[0], 'r') as file:\n",
    "        lines = file.readlines()\n",
    "    \n",
    "    # Extract the sample characteristics and other metadata\n",
    "    sample_char_lines = []\n",
    "    in_sample_char_section = False\n",
    "    \n",
    "    for line in lines:\n",
    "        if line.startswith('!Sample_characteristics_ch1'):\n",
    "            sample_char_lines.append(line.strip())\n",
    "            in_sample_char_section = True\n",
    "        elif in_sample_char_section and not line.startswith('!Sample_characteristics_ch1'):\n",
    "            in_sample_char_section = False\n",
    "    \n",
    "    # Parse sample characteristics\n",
    "    clinical_data_dict = {}\n",
    "    for i, line in enumerate(sample_char_lines):\n",
    "        parts = line.split('\\t')\n",
    "        header = parts[0]\n",
    "        values = parts[1:]\n",
    "        \n",
    "        # Organize data by characteristic type\n",
    "        characteristic_type = None\n",
    "        for val in values:\n",
    "            if ':' in val:\n",
    "                # Extract characteristic type (before colon)\n",
    "                potential_type = val.split(':', 1)[0].strip().lower()\n",
    "                if i == 0 or potential_type not in [v.split(':', 1)[0].strip().lower() for v in clinical_data_dict.get(i-1, [])]:\n",
    "                    characteristic_type = potential_type\n",
    "                    if characteristic_type not in clinical_data_dict:\n",
    "                        clinical_data_dict[characteristic_type] = []\n",
    "                    clinical_data_dict[characteristic_type].append(val)\n",
    "    \n",
    "    # Convert to DataFrame for easier analysis\n",
    "    clinical_data = pd.DataFrame(clinical_data_dict)\n",
    "    \n",
    "    # If clinical_data is empty, look for other sources of information\n",
    "    if clinical_data.empty:\n",
    "        # Try to find sample info from the !Sample_ lines\n",
    "        sample_info_lines = [line for line in lines if line.startswith('!Sample_')]\n",
    "        sample_info = {}\n",
    "        for line in sample_info_lines:\n",
    "            parts = line.strip().split('\\t')\n",
    "            key = parts[0].replace('!Sample_', '')\n",
    "            values = parts[1:]\n",
    "            sample_info[key] = values\n",
    "        \n",
    "        # Convert to DataFrame\n",
    "        clinical_data = pd.DataFrame(sample_info)\n",
    "else:\n",
    "    print(\"No series matrix file found. Looking for alternative files...\")\n",
    "    # Look for other potential files that might contain clinical data\n",
    "    clinical_files = [f for f in available_files if 'clinical' in f.lower() or 'sample' in f.lower()]\n",
    "    if clinical_files:\n",
    "        print(f\"Found potential clinical data file: {clinical_files[0]}\")\n",
    "        try:\n",
    "            # Try reading as CSV first\n",
    "            clinical_data = pd.read_csv(clinical_files[0])\n",
    "        except:\n",
    "            try:\n",
    "                # Try reading as Excel\n",
    "                clinical_data = pd.read_excel(clinical_files[0])\n",
    "            except:\n",
    "                print(\"Could not read clinical data file.\")\n",
    "                clinical_data = pd.DataFrame()\n",
    "    else:\n",
    "        print(\"No clinical data files found.\")\n",
    "        clinical_data = pd.DataFrame()\n",
    "\n",
    "# Print what we found\n",
    "print(\"\\nClinical Data Preview:\")\n",
    "print(clinical_data.head())\n",
    "\n",
    "# Extract and display unique values to help identify relevant columns/rows\n",
    "print(\"\\nUnique values in clinical data:\")\n",
    "for col in clinical_data.columns:\n",
    "    unique_vals = clinical_data[col].dropna().unique()\n",
    "    if len(unique_vals) < 10:  # Only print if there aren't too many values\n",
    "        print(f\"{col}: {unique_vals}\")\n",
    "\n",
    "# Background knowledge\n",
    "print(\"\\nBackground knowledge:\")\n",
    "bg_files = [f for f in available_files if 'background' in f.lower() or 'readme' in f.lower()]\n",
    "background = \"\"\n",
    "if bg_files:\n",
    "    with open(bg_files[0], 'r') as f:\n",
    "        background = f.read()\n",
    "    print(background)\n",
    "else:\n",
    "    print(\"No background file found.\")\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on available files, determine if gene expression data is likely present\n",
    "is_gene_available = any('expression' in f.lower() for f in available_files) or any('matrix' in f.lower() for f in available_files)\n",
    "print(f\"\\nGene expression data available: {is_gene_available}\")\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability - based on what we found in the data\n",
    "\n",
    "# Initialize to None, we'll update if we find relevant data\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Look through column names and data to find trait, age, and gender information\n",
    "# This is a simplification - in real code we'd do more thorough analysis\n",
    "if not clinical_data.empty:\n",
    "    # Search for trait information (cystic fibrosis status)\n",
    "    cf_related_cols = [col for col in clinical_data.columns \n",
    "                     if any(term in str(col).lower() for term in ['cf', 'fibrosis', 'disease', 'status', 'condition', 'diagnosis'])]\n",
    "    if cf_related_cols:\n",
    "        trait_row = cf_related_cols[0]\n",
    "        print(f\"Found trait information in column: {trait_row}\")\n",
    "    \n",
    "    # Search for age information\n",
    "    age_related_cols = [col for col in clinical_data.columns \n",
    "                      if any(term in str(col).lower() for term in ['age', 'years'])]\n",
    "    if age_related_cols:\n",
    "        age_row = age_related_cols[0]\n",
    "        print(f\"Found age information in column: {age_row}\")\n",
    "    \n",
    "    # Search for gender information\n",
    "    gender_related_cols = [col for col in clinical_data.columns \n",
    "                         if any(term in str(col).lower() for term in ['gender', 'sex'])]\n",
    "    if gender_related_cols:\n",
    "        gender_row = gender_related_cols[0]\n",
    "        print(f\"Found gender information in column: {gender_row}\")\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert CF status to binary value (1 for CF, 0 for non-CF)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value_str = str(value).lower()\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value_str:\n",
    "        value_str = value_str.split(':', 1)[1].strip()\n",
    "    \n",
    "    if any(term in value_str for term in ['cf', 'cystic fibrosis', 'yes', 'true', 'y', 'patient', 'affected']):\n",
    "        return 1\n",
    "    elif any(term in value_str for term in ['non-cf', 'control', 'no', 'false', 'n', 'healthy', 'unaffected']):\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to numeric value\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value_str = str(value)\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value_str:\n",
    "        value_str = value_str.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Extract first number in the string\n",
    "    import re\n",
    "    numbers = re.findall(r'\\d+\\.?\\d*', value_str)\n",
    "    if numbers:\n",
    "        try:\n",
    "            return float(numbers[0])\n",
    "        except (ValueError, TypeError):\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe8b037",
   "metadata": {},
   "source": [
    "### Step 4: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa135e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import re\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# Check the files in the cohort directory to locate data files\n",
    "cohort_files = os.listdir(in_cohort_dir)\n",
    "print(f\"Files in cohort directory: {cohort_files}\")\n",
    "\n",
    "# Look for clinical data file\n",
    "clinical_data_path = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n",
    "if os.path.exists(clinical_data_path):\n",
    "    clinical_data = pd.read_csv(clinical_data_path)\n",
    "    print(\"Sample characteristics loaded.\")\n",
    "    print(clinical_data.head())\n",
    "else:\n",
    "    print(\"Sample characteristics file not found. Looking for alternatives...\")\n",
    "    potential_files = [f for f in cohort_files if \"clinical\" in f.lower() or \"sample\" in f.lower()]\n",
    "    if potential_files:\n",
    "        clinical_data_path = os.path.join(in_cohort_dir, potential_files[0])\n",
    "        clinical_data = pd.read_csv(clinical_data_path)\n",
    "        print(f\"Loaded {potential_files[0]} as clinical data.\")\n",
    "        print(clinical_data.head())\n",
    "    else:\n",
    "        # Try to get information from metadata file\n",
    "        metadata_path = os.path.join(in_cohort_dir, \"metadata.txt\")\n",
    "        if os.path.exists(metadata_path):\n",
    "            with open(metadata_path, 'r') as f:\n",
    "                metadata = f.read()\n",
    "            print(\"Metadata found:\")\n",
    "            print(metadata[:1000] + \"...\")  # Print first 1000 chars\n",
    "        else:\n",
    "            print(\"No clinical data or metadata files found.\")\n",
    "            clinical_data = pd.DataFrame()  # Empty dataframe as fallback\n",
    "\n",
    "# Check for gene expression data\n",
    "gene_files = [f for f in cohort_files if \"gene\" in f.lower() or \"expression\" in f.lower() or \"matrix\" in f.lower()]\n",
    "if gene_files:\n",
    "    print(f\"Potential gene expression files: {gene_files}\")\n",
    "    is_gene_available = True\n",
    "else:\n",
    "    print(\"No obvious gene expression files found.\")\n",
    "    # GSE53543 is a gene expression dataset studying cystic fibrosis\n",
    "    is_gene_available = True  # Based on dataset ID\n",
    "\n",
    "# Define conversion functions\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"Convert CF status to binary (0=control, 1=CF)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"cf\" in value or \"cystic fibrosis\" in value or \"patient\" 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: str) -> float:\n",
    "    \"\"\"Convert age to a float value\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value)\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric age\n",
    "    match = re.search(r'(\\d+(?:\\.\\d+)?)', value)\n",
    "    if match:\n",
    "        return float(match.group(1))\n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> int:\n",
    "    \"\"\"Convert gender to binary (0=female, 1=male)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \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",
    "# Set default row identifiers\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# If clinical data is available, try to identify trait, age, and gender rows\n",
    "if not clinical_data.empty:\n",
    "    # Print unique values in each row to help identify variables\n",
    "    for i in range(min(10, clinical_data.shape[0])):\n",
    "        unique_values = set()\n",
    "        for val in clinical_data.iloc[i, :]:\n",
    "            if isinstance(val, str):\n",
    "                unique_values.add(val)\n",
    "            else:\n",
    "                unique_values.add(str(val))\n",
    "        print(f\"Row {i}: {unique_values}\")\n",
    "        \n",
    "        # Search for indicators in the row values\n",
    "        row_text = ' '.join([str(v).lower() for v in unique_values])\n",
    "        \n",
    "        # Identify trait row (CF status)\n",
    "        if any(x in row_text for x in ['cf', 'cystic fibrosis', 'control', 'patient', 'disease']):\n",
    "            trait_row = i\n",
    "            print(f\"Found trait row at index {i}\")\n",
    "        \n",
    "        # Identify age row\n",
    "        if any(x in row_text for x in ['age', 'year', 'month']):\n",
    "            age_row = i\n",
    "            print(f\"Found age row at index {i}\")\n",
    "        \n",
    "        # Identify gender row\n",
    "        if any(x in row_text for x in ['gender', 'sex', 'male', 'female']):\n",
    "            gender_row = i\n",
    "            print(f\"Found gender row at index {i}\")\n",
    "\n",
    "# Set trait availability based on trait_row\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save the initial cohort information\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# Extract clinical features if trait data is available\n",
    "if trait_row is not None and not clinical_data.empty:\n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age 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",
    "    print(\"Preview of extracted clinical features:\")\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(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, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "else:\n",
    "    print(\"Clinical data extraction skipped due to missing trait data or empty clinical dataset.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c179ad9f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8f0c2f6",
   "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": "359ea362",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2b8b887",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers in the gene expression data\n",
    "# Looking at the identifiers such as \"ILMN_1651229\", I can recognize these are Illumina probe IDs,\n",
    "# not standard human gene symbols (which would look like BRCA1, TP53, etc.)\n",
    "# These are microarray probe identifiers specific to Illumina platform and need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b043132",
   "metadata": {},
   "source": [
    "### Step 7: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a30405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba67bc97",
   "metadata": {},
   "source": [
    "### Step 8: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be601595",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the keys in gene annotation for gene identifiers and gene symbols\n",
    "# Looking at the annotation preview, 'ID' looks like the Illumina probe identifiers\n",
    "# And 'Symbol' appears to contain gene symbols\n",
    "probe_id_col = 'ID'\n",
    "gene_symbol_col = 'Symbol'\n",
    "\n",
    "print(f\"Will map from {probe_id_col} to {gene_symbol_col}\")\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the identifier and gene symbol columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)\n",
    "print(\"Gene mapping dataframe shape:\", mapping_df.shape)\n",
    "print(\"First few rows of mapping dataframe:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(\"Gene expression dataframe shape after mapping:\", gene_data.shape)\n",
    "print(\"First few gene symbols in the mapped data:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the gene expression data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4271dbea",
   "metadata": {},
   "source": [
    "### Step 9: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2db579dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(\"First few genes with their expression values after normalization:\")\n",
    "print(normalized_gene_data.head())\n",
    "\n",
    "# Save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "# Use the clinical data that was already processed and saved in previous steps\n",
    "selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are biased\n",
    "# First check if trait is biased\n",
    "trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n",
    "if trait_type == \"binary\":\n",
    "    is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
    "else:\n",
    "    is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
    "\n",
    "# Check for and remove biased demographic features\n",
    "unbiased_linked_data = linked_data.copy()\n",
    "if \"Age\" in unbiased_linked_data.columns:\n",
    "    age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n",
    "    if age_biased:\n",
    "        print(f\"The distribution of the feature \\'Age\\' in this dataset is severely biased.\\n\")\n",
    "        unbiased_linked_data = unbiased_linked_data.drop(columns='Age')\n",
    "    else:\n",
    "        print(f\"The distribution of the feature \\'Age\\' in this dataset is fine.\\n\")\n",
    "\n",
    "if \"Gender\" in unbiased_linked_data.columns:\n",
    "    gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n",
    "    if gender_biased:\n",
    "        print(f\"The distribution of the feature \\'Gender\\' in this dataset is severely biased.\\n\")\n",
    "        unbiased_linked_data = unbiased_linked_data.drop(columns='Gender')\n",
    "    else:\n",
    "        print(f\"The distribution of the feature \\'Gender\\' in this dataset is fine.\\n\")\n",
    "\n",
    "# 5. Conduct final quality validation and save cohort information\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=True, \n",
    "    is_trait_available=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=f\"Dataset contains gene expression data comparing CFBE41o-ΔF508 (CF) cells with CFBE41o−CFTR (control) cells.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Data was determined to be unusable and was not saved\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}