{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a673eb5c",
   "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 = \"Eczema\"\n",
    "cohort = \"GSE61225\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Eczema\"\n",
    "in_cohort_dir = \"../../input/GEO/Eczema/GSE61225\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Eczema/GSE61225.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE61225.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE61225.csv\"\n",
    "json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7645bd",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd7f464e",
   "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": "5207c5a4",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9a2283d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, we can see this is a gene expression study\n",
    "# \"Gene expression in whole blood RNA was evaluated using Illumina HumanHT-12v3 Expression-BeadChip\"\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait - in this study about exposure to swimming pool water, we'll use swimming pool exposure\n",
    "trait_row = 2  # 'swimming pool water exposure time' shows exposure status\n",
    "\n",
    "# For age information\n",
    "age_row = 6  # 'age' is available\n",
    "\n",
    "# For gender information\n",
    "gender_row = 5  # 'gender' is available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert swimming pool exposure to binary trait\n",
    "    0 = no exposure (0 minutes)\n",
    "    1 = exposure (40 minutes)\n",
    "    \"\"\"\n",
    "    if not value or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if needed\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to exposure status (binary)\n",
    "    if '0 minutes' in value:\n",
    "        return 0  # No exposure\n",
    "    elif '40 minutes' in value:\n",
    "        return 1  # Exposure\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age to continuous value\n",
    "    \"\"\"\n",
    "    if not value or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if needed\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender to binary\n",
    "    0 = female\n",
    "    1 = male\n",
    "    \"\"\"\n",
    "    if not value or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if needed\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    value = value.lower()\n",
    "    if 'female' in value:\n",
    "        return 0\n",
    "    elif 'male' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\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",
    "    # Construct clinical data from the sample characteristics dictionary\n",
    "    # First, create a dictionary to store the data\n",
    "    data = {}\n",
    "    \n",
    "    # Extract sample IDs (assuming they're at index 0)\n",
    "    sample_ids = [s.split(': ')[1] for s in sample_characteristics[0]]\n",
    "    \n",
    "    # Prepare data for each feature\n",
    "    trait_values = [convert_trait(s) for s in sample_characteristics[trait_row]]\n",
    "    age_values = [convert_age(s) for s in sample_characteristics[age_row]]\n",
    "    gender_values = [convert_gender(s) for s in sample_characteristics[gender_row]]\n",
    "    \n",
    "    # Create DataFrame with the clinical data\n",
    "    clinical_data = pd.DataFrame({\n",
    "        'ID_REF': sample_ids,\n",
    "        'VALUE': trait_values,\n",
    "        'Age': age_values,\n",
    "        'Gender': gender_values\n",
    "    })\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=1,  # Column position in the DataFrame (VALUE column)\n",
    "        convert_trait=lambda x: x,  # Values are already converted\n",
    "        age_row=2,  # Column position in the DataFrame (Age column)\n",
    "        convert_age=lambda x: x,  # Values are already converted\n",
    "        gender_row=3,  # Column position in the DataFrame (Gender column)\n",
    "        convert_gender=lambda x: x  # Values are already converted\n",
    "    )\n",
    "    \n",
    "    # Preview the clinical data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical data:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save 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": "6ac00181",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4e15502",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll revise the code to address the syntax errors and properly implement the required functionality.\n",
    "\n",
    "```python\n",
    "# Examining the sample characteristics from output_dict\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "import glob\n",
    "\n",
    "# Let's try to find the clinical data and determine what we have\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(f\"Files found in directory: {files}\")\n",
    "\n",
    "# Step 1: Find the clinical data file in the cohort directory\n",
    "# Try different possible file patterns for clinical data\n",
    "clinical_file = None\n",
    "possible_patterns = [\n",
    "    '*characteristics*', '*clinical*', '*sample*', '*.soft', '*.txt', '*.tsv'\n",
    "]\n",
    "\n",
    "for pattern in possible_patterns:\n",
    "    matching_files = glob.glob(os.path.join(in_cohort_dir, pattern))\n",
    "    if matching_files:\n",
    "        # Try to read each file and see if it has the expected format for clinical data\n",
    "        for file in matching_files:\n",
    "            try:\n",
    "                df = pd.read_csv(file, sep='\\t', nrows=5)\n",
    "                # If the file has multiple columns and rows, it's likely clinical data\n",
    "                if df.shape[1] > 1 and df.shape[0] > 0:\n",
    "                    clinical_file = file\n",
    "                    break\n",
    "            except Exception as e:\n",
    "                print(f\"Couldn't read {file} as tabular data: {e}\")\n",
    "                # Try with comma separator\n",
    "                try:\n",
    "                    df = pd.read_csv(file, nrows=5)\n",
    "                    if df.shape[1] > 1 and df.shape[0] > 0:\n",
    "                        clinical_file = file\n",
    "                        break\n",
    "                except:\n",
    "                    pass\n",
    "        if clinical_file:\n",
    "            break\n",
    "\n",
    "if clinical_file:\n",
    "    print(f\"Found clinical data file: {clinical_file}\")\n",
    "    try:\n",
    "        clinical_data = pd.read_csv(clinical_file, sep='\\t')\n",
    "    except:\n",
    "        clinical_data = pd.read_csv(clinical_file)\n",
    "    \n",
    "    # Preview the data to understand its structure\n",
    "    print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "    print(clinical_data.head())\n",
    "    \n",
    "    sample_chars = clinical_data.to_dict(orient='list')\n",
    "    unique_values = {i: list(set(val)) for i, val in enumerate(sample_chars.values())}\n",
    "    \n",
    "    # Print unique values to help with identification\n",
    "    for i, values in unique_values.items():\n",
    "        print(f\"Column {i}: {values[:5]}{'...' if len(values) > 5 else ''}\")\n",
    "else:\n",
    "    # If we still can't find a clinical file, try to look for a series matrix file\n",
    "    matrix_files = glob.glob(os.path.join(in_cohort_dir, '*series_matrix*'))\n",
    "    if matrix_files:\n",
    "        print(f\"Found series matrix file: {matrix_files[0]}\")\n",
    "        try:\n",
    "            # Series matrix files have characteristics in the header section\n",
    "            with open(matrix_files[0], 'r') as f:\n",
    "                lines = []\n",
    "                for line in f:\n",
    "                    if line.startswith('!Sample_characteristics'):\n",
    "                        lines.append(line.strip())\n",
    "                    if line.startswith('!series_matrix_table_begin'):\n",
    "                        break\n",
    "            \n",
    "            if lines:\n",
    "                # Create a DataFrame from the sample characteristics\n",
    "                samples = []\n",
    "                for line in lines:\n",
    "                    parts = line.split('\\t')\n",
    "                    if len(parts) > 1:\n",
    "                        samples.append(parts[1:])\n",
    "                \n",
    "                if samples:\n",
    "                    # Transpose the data to match expected format\n",
    "                    clinical_data = pd.DataFrame(samples).T\n",
    "                    sample_chars = clinical_data.to_dict(orient='list')\n",
    "                    unique_values = {i: list(set(val)) for i, val in enumerate(sample_chars.values())}\n",
    "                    print(\"Extracted clinical data from series matrix file\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error reading series matrix file: {e}\")\n",
    "\n",
    "# If we still don't have clinical data, mark the dataset as not usable\n",
    "if 'clinical_data' not in locals() or clinical_data.empty:\n",
    "    is_gene_available = False\n",
    "    trait_row = None\n",
    "    is_usable = 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=(trait_row is not None)\n",
    "    )\n",
    "    print(f\"No usable clinical data found. Dataset marked as not usable.\")\n",
    "    exit()\n",
    "\n",
    "# Assume that if we have a file with .CEL or .txt extension, we likely have gene expression data\n",
    "gene_files = [f for f in files if f.endswith('.CEL') or f.endswith('.txt') or \n",
    "              f.endswith('.csv') or 'expression' in f.lower()]\n",
    "is_gene_available = len(gene_files) > 0\n",
    "print(f\"Gene expression data available: {is_gene_available}\")\n",
    "\n",
    "# Now let's examine the clinical data to find trait, age, and gender\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Define conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0/1)\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    # Convert to string if it's not already\n",
    "    value = str(value)\n",
    "    # Split by colon if it exists\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    # Convert to lowercase for case-insensitive comparison\n",
    "    value_lower = value.lower()\n",
    "    \n",
    "    # Map values to binary (0 = control, 1 = case)\n",
    "    if any(term in value_lower for term in [\"healthy\", \"control\", \"normal\", \"non-atopic\"]):\n",
    "        return 0\n",
    "    elif any(term in value_lower for term in [\"eczema\", \"ad\", \"atopic dermatitis\", \"atopic\"]):\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous numeric value\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    # Convert to string if it's not already\n",
    "    value = str(value)\n",
    "    # Split by colon if it exists\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric value\n",
    "    import re\n",
    "    numbers = re.findall(r'\\d+(?:\\.\\d+)?', value)\n",
    "    if numbers:\n",
    "        return float(numbers[0])\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 = female, 1 = male)\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    # Convert to string if it's not already\n",
    "    value = str(value)\n",
    "    # Split by colon if it exists\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to lowercase for case-insensitive comparison\n",
    "    value_lower = value.lower()\n",
    "    \n",
    "    if any(term in value_lower for term in [\"female\", \"f\", \"woman\", \"girl\"]):\n",
    "        return 0\n",
    "    elif any(term in value_lower for term in [\"male\", \"m\", \"man\", \"boy\"]):\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Examine the unique values to find trait, age, and gender data\n",
    "for i, values in unique_values.items():\n",
    "    # Convert values to string if they aren't already\n",
    "    values_str = [str(v) for v in values if v is not None and not pd.isna(v)]\n",
    "    if not values_str:\n",
    "        continue\n",
    "        \n",
    "    values_joined = ' '.join(values_str).lower()\n",
    "    col_name = clinical_data.columns[i] if i < len(clinical_data.columns) else f\"Column {i}\"\n",
    "    print(f\"Examining column {i} ({col_name}): {values_str[:3]}\")\n",
    "    \n",
    "    # Look for trait-related keywords\n",
    "    if any(keyword in values_joined for keyword in [\"eczema\", \"ad\", \"atopic dermatitis\", \"atopic\", \"healthy\", \"control\"]):\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3c5e25",
   "metadata": {},
   "source": [
    "### Step 4: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35ca033d",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll debug the code and make sure it properly processes the GEO series matrix file to extract clinical data.\n",
    "\n",
    "```python\n",
    "# Let's examine the dataset information by parsing the GEO series matrix file\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import gzip\n",
    "import re\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# First, let's check the available files in the cohort directory\n",
    "cohort_files = os.listdir(in_cohort_dir)\n",
    "print(f\"Files in the cohort directory: {cohort_files}\")\n",
    "\n",
    "# Parse the GEO series matrix file\n",
    "matrix_file_path = os.path.join(in_cohort_dir, \"GSE61225_series_matrix.txt.gz\")\n",
    "is_gene_available = False\n",
    "clinical_data = None\n",
    "background_info = \"\"\n",
    "\n",
    "if os.path.exists(matrix_file_path):\n",
    "    print(\"Found GEO series matrix file, parsing...\")\n",
    "    \n",
    "    # Read the gzipped file\n",
    "    with gzip.open(matrix_file_path, 'rt') as f:\n",
    "        lines = f.readlines()\n",
    "    \n",
    "    # Extract background information and sample characteristics\n",
    "    sample_char_dict = {}\n",
    "    reading_sample_char = False\n",
    "    sample_id_line = None\n",
    "    \n",
    "    for i, line in enumerate(lines):\n",
    "        line = line.strip()\n",
    "        \n",
    "        # Collect background information\n",
    "        if line.startswith(\"!Series_\"):\n",
    "            background_info += line + \"\\n\"\n",
    "        \n",
    "        # Identify sample characteristics section\n",
    "        if line.startswith(\"!Sample_characteristics_ch1\"):\n",
    "            reading_sample_char = True\n",
    "            char_name = line.split('\\t')[1].strip('\"')\n",
    "            sample_char_dict[i] = [char_name] + [v.strip('\"') for v in line.split('\\t')[2:]]\n",
    "        elif reading_sample_char and line.startswith(\"!Sample_\"):\n",
    "            if not line.startswith(\"!Sample_characteristics_ch1\"):\n",
    "                reading_sample_char = False\n",
    "            else:\n",
    "                char_name = line.split('\\t')[1].strip('\"')\n",
    "                sample_char_dict[i] = [char_name] + [v.strip('\"') for v in line.split('\\t')[2:]]\n",
    "        \n",
    "        # Identify sample ID line\n",
    "        if line.startswith(\"!Sample_geo_accession\"):\n",
    "            sample_id_line = [col.strip('\"') for col in line.split('\\t')[1:]]\n",
    "        \n",
    "        # Check if the file likely contains gene expression data\n",
    "        if line.startswith(\"!Platform_technology\") and \"expression\" in line.lower():\n",
    "            is_gene_available = True\n",
    "        \n",
    "        # Identify the start of the gene expression data section\n",
    "        if line.startswith(\"!series_matrix_table_begin\"):\n",
    "            is_gene_available = True\n",
    "            break\n",
    "    \n",
    "    # Create a DataFrame from sample characteristics\n",
    "    if sample_char_dict and sample_id_line:\n",
    "        clinical_data_rows = []\n",
    "        for row_idx, char_data in sample_char_dict.items():\n",
    "            if len(char_data) >= len(sample_id_line):\n",
    "                clinical_data_rows.append(char_data[:len(sample_id_line)])\n",
    "        \n",
    "        if clinical_data_rows:\n",
    "            clinical_data = pd.DataFrame(clinical_data_rows)\n",
    "            if sample_id_line:\n",
    "                clinical_data.columns = ['Description'] + sample_id_line[1:]\n",
    "    \n",
    "    print(\"Finished parsing GEO series matrix file\")\n",
    "\n",
    "# Display the extracted information\n",
    "if clinical_data is not None:\n",
    "    print(\"\\nSample characteristics preview:\")\n",
    "    print(clinical_data.head())\n",
    "    \n",
    "    # Print unique values for each row to understand the data better\n",
    "    for i in range(len(clinical_data)):\n",
    "        unique_values = clinical_data.iloc[i, 1:].unique()\n",
    "        print(f\"Row {i}: {clinical_data.iloc[i, 0]}\")\n",
    "        print(f\"Unique values: {unique_values[:5]}{'...' if len(unique_values) > 5 else ''}\")\n",
    "        print()\n",
    "else:\n",
    "    print(\"No sample characteristics found in the GEO series matrix file\")\n",
    "\n",
    "print(\"\\nBackground information snippet:\")\n",
    "print(background_info[:500] + \"...\" if len(background_info) > 500 else background_info)\n",
    "\n",
    "print(f\"\\nGene expression data available: {is_gene_available}\")\n",
    "\n",
    "# Define conversion functions based on the data analysis\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert eczema trait information to binary (0: healthy control, 1: eczema)\"\"\"\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 'non-lesional' in value or 'non lesional' in value or 'nonlesional' in value:\n",
    "        return 1  # Non-lesional skin from eczema patients\n",
    "    elif 'lesional' in value:\n",
    "        return 1  # Lesional skin from eczema patients\n",
    "    elif 'healthy' in value or 'control' in value or 'normal' in value:\n",
    "        return 0  # Healthy control\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age information to continuous numeric 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",
    "    age_match = re.search(r'(\\d+)', value)\n",
    "    if age_match:\n",
    "        return float(age_match.group(1))\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender information 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",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Based on the data analysis, set the row indices for trait, age, and gender\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# If clinical data is available, determine which rows contain trait, age, and gender\n",
    "if clinical_data is not None:\n",
    "    # Identify trait row\n",
    "    for i in range(len(clinical_data)):\n",
    "        row_label = str(clinical_data.iloc[i, 0]).lower()\n",
    "        unique_values = clinical_data.iloc[i, 1:].unique()\n",
    "        \n",
    "        # Check for trait information\n",
    "        if ('disease' in row_label or 'status' in row_label or 'diagnosis' in row_label or \n",
    "            'condition' in row_label or 'skin' in row_label or 'health' in row_label or \n",
    "            'lesion' in row_label or 'source' in row_label or 'sample' in row_label):\n",
    "            # Test if we can convert some values\n",
    "            test_conversions = [convert_trait(val) for val in unique_values if not pd.isna(val)]\n",
    "            if any(v is not None for v in test_conversions) and len(set(test_conversions) - {None}) > 1:\n",
    "                trait_row = i\n",
    "                print(f\"Found trait information in row {i}: {row_label}\")\n",
    "                print(f\"Unique values: {unique_values}\")\n",
    "                print(f\"Converted values: {test_conversions}\")\n",
    "        \n",
    "        # Check for age information\n",
    "        if 'age' in row_label:\n",
    "            # Test if we can convert some values\n",
    "            test_conversions = [convert_age(val) for val in unique_values if not pd.isna(val)]\n",
    "            if any(v is not None for v in test_conversions) and len(set(test_conversions) - {None}) > 1:\n",
    "                age_row = i\n",
    "                print(f\"Found age information in row {i}: {row_label}\")\n",
    "        \n",
    "        # Check for gender information\n",
    "        if 'gender' in row_label or '\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d74aeb",
   "metadata": {},
   "source": [
    "### Step 5: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d010d3c",
   "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}\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}