{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e66ff380",
   "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 = \"Height\"\n",
    "cohort = \"GSE181339\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Height\"\n",
    "in_cohort_dir = \"../../input/GEO/Height/GSE181339\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Height/GSE181339.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE181339.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE181339.csv\"\n",
    "json_path = \"../../output/preprocess/Height/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26737df",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aa8ff0d",
   "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": "f981b5e2",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6cf6496",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the series description, this appears to be a gene expression study\n",
    "# \"For the microarray experiment...\" suggests gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "trait_row = 1  # 'group' contains weight status information (NW, OW/OB, MONW)\n",
    "age_row = 2    # 'age' is available\n",
    "gender_row = 0  # 'gender' is available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert the group value to binary form (0 for normal weight, 1 for overweight/obese or MONW)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value.upper() == \"NW\":\n",
    "        return 0  # Normal weight\n",
    "    elif value.upper() in [\"OW/OB\", \"MONW\"]:\n",
    "        return 1  # Overweight/obese or Metabolically Obese Normal-Weight\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age string to numeric value\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender string to binary (0 for female, 1 for male)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip().lower()\n",
    "    \n",
    "    if value.lower() == \"woman\" or value.lower() == \"female\":\n",
    "        return 0\n",
    "    elif value.lower() == \"man\" or value.lower() == \"male\":\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Since trait_row is not None, trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering on the usability\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "# Since trait_row is not None, we proceed with clinical data extraction\n",
    "# Create a DataFrame from the sample characteristics dictionary provided in the task\n",
    "sample_characteristics = {\n",
    "    0: ['gender: Man', 'gender: Woman'],\n",
    "    1: ['group: NW', 'group: OW/OB', 'group: MONW'],\n",
    "    2: ['age: 21', 'age: 23', 'age: 10', 'age: 17', 'age: 11', 'age: 1', 'age: 18', 'age: 12', 'age: 8', 'age: 14', 'age: 26', 'age: 4', 'age: 2', 'age: 3', 'age: 7', 'age: 13', 'age: 15', 'age: 9', 'age: 30', 'age: 19'],\n",
    "    3: ['fasting time: 6hr', 'fasting time: 4hr'],\n",
    "    4: ['timepoint: 0months', 'timepoint: 6months']\n",
    "}\n",
    "\n",
    "# Convert to a format that geo_select_clinical_features can work with\n",
    "# Create a dataframe with the sample characteristics\n",
    "clinical_data = pd.DataFrame.from_dict(sample_characteristics, orient='index')\n",
    "clinical_data.index.name = 'row_id'\n",
    "clinical_data = clinical_data.reset_index()\n",
    "\n",
    "# Extract clinical features\n",
    "clinical_features = 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",
    "preview = preview_df(clinical_features)\n",
    "print(\"Preview of clinical features:\", preview)\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8f3056",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39ac46bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll debug the code to properly handle GEO dataset files and extract clinical features.\n",
    "\n",
    "```python\n",
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import glob\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# First, let's explore what files are available in the directory\n",
    "print(f\"Exploring directory: {in_cohort_dir}\")\n",
    "available_files = glob.glob(os.path.join(in_cohort_dir, \"*\"))\n",
    "print(f\"Available files: {available_files}\")\n",
    "\n",
    "# GEO data is typically stored in _series_matrix.txt files\n",
    "series_matrix_files = glob.glob(os.path.join(in_cohort_dir, \"*_series_matrix.txt*\"))\n",
    "if series_matrix_files:\n",
    "    matrix_file = series_matrix_files[0]\n",
    "    print(f\"Found series matrix file: {matrix_file}\")\n",
    "    \n",
    "    # Read the file line by line to extract sample characteristics\n",
    "    sample_char_dict = {}\n",
    "    current_line_idx = 0\n",
    "    with open(matrix_file, 'r') as f:\n",
    "        for line in f:\n",
    "            if line.startswith('!Sample_characteristics_ch1'):\n",
    "                parts = line.strip().split('\\t')\n",
    "                if len(parts) > 1:  # Ensure there's at least one sample\n",
    "                    # Remove the prefix to get just the values\n",
    "                    values = [p.replace('!Sample_characteristics_ch1 = ', '') for p in parts]\n",
    "                    sample_char_dict[current_line_idx] = values\n",
    "                    current_line_idx += 1\n",
    "            elif line.startswith('!Sample_title'):\n",
    "                # Sample titles can sometimes contain useful information\n",
    "                parts = line.strip().split('\\t')\n",
    "                if len(parts) > 1:\n",
    "                    values = [p.replace('!Sample_title = ', '') for p in parts]\n",
    "                    sample_char_dict[current_line_idx] = values\n",
    "                    current_line_idx += 1\n",
    "                    \n",
    "    # If we've collected any sample characteristics, convert to DataFrame\n",
    "    if sample_char_dict:\n",
    "        clinical_data = pd.DataFrame(sample_char_dict).T\n",
    "        # Print a preview of what we found\n",
    "        print(\"Sample characteristics preview:\")\n",
    "        for idx, row in clinical_data.iterrows():\n",
    "            print(f\"Row {idx}: {row.unique()[:5]}...\")\n",
    "    else:\n",
    "        clinical_data = pd.DataFrame()\n",
    "        print(\"No sample characteristics found in series matrix file.\")\n",
    "else:\n",
    "    # If no series matrix file, try to find a soft file\n",
    "    soft_files = glob.glob(os.path.join(in_cohort_dir, \"*.soft*\"))\n",
    "    if soft_files:\n",
    "        soft_file = soft_files[0]\n",
    "        print(f\"Found SOFT file: {soft_file}\")\n",
    "        \n",
    "        # Read the SOFT file to extract sample characteristics\n",
    "        sample_char_dict = {}\n",
    "        current_line_idx = 0\n",
    "        with open(soft_file, 'r') as f:\n",
    "            in_sample_section = False\n",
    "            current_sample = None\n",
    "            for line in f:\n",
    "                if line.startswith('^SAMPLE'):\n",
    "                    in_sample_section = True\n",
    "                    current_sample = []\n",
    "                elif line.startswith('!Sample_characteristics_ch1'):\n",
    "                    if in_sample_section:\n",
    "                        current_sample.append(line.strip().split(' = ')[1])\n",
    "                elif line.startswith('!sample_table_end'):\n",
    "                    if in_sample_section and current_sample:\n",
    "                        sample_char_dict[current_line_idx] = current_sample\n",
    "                        current_line_idx += 1\n",
    "                        current_sample = None\n",
    "                        in_sample_section = False\n",
    "                        \n",
    "        if sample_char_dict:\n",
    "            clinical_data = pd.DataFrame(sample_char_dict).T\n",
    "            print(\"Sample characteristics preview from SOFT file:\")\n",
    "            for idx, row in clinical_data.iterrows():\n",
    "                print(f\"Row {idx}: {row.unique()[:5]}...\")\n",
    "        else:\n",
    "            clinical_data = pd.DataFrame()\n",
    "            print(\"No sample characteristics found in SOFT file.\")\n",
    "    else:\n",
    "        # As a last resort, try to find any text files\n",
    "        txt_files = glob.glob(os.path.join(in_cohort_dir, \"*.txt\"))\n",
    "        if txt_files:\n",
    "            print(f\"Found text files but no recognized GEO format: {txt_files}\")\n",
    "            clinical_data = pd.DataFrame()\n",
    "        else:\n",
    "            print(\"No recognizable data files found.\")\n",
    "            clinical_data = pd.DataFrame()\n",
    "\n",
    "# Analyze what we have and make decisions about data availability\n",
    "is_gene_available = True  # Assuming gene expression data exists unless determined otherwise\n",
    "\n",
    "# Determine if height-related data is available in the clinical data\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "if not clinical_data.empty:\n",
    "    # Check each row for trait, age, and gender data\n",
    "    for row_idx in range(len(clinical_data)):\n",
    "        row_values = clinical_data.iloc[row_idx].astype(str)\n",
    "        row_text = ' '.join(row_values).lower()\n",
    "        \n",
    "        # Check for trait (Height)\n",
    "        if 'height' in row_text and trait_row is None:\n",
    "            unique_values = row_values.unique()\n",
    "            if len(unique_values) > 1:  # More than one unique value\n",
    "                trait_row = row_idx\n",
    "                print(f\"Found trait data (Height) in row {row_idx}: {unique_values[:5]}\")\n",
    "        \n",
    "        # Check for age\n",
    "        if ('age' in row_text or 'years' in row_text) and age_row is None:\n",
    "            unique_values = row_values.unique()\n",
    "            if len(unique_values) > 1:  # More than one unique value\n",
    "                age_row = row_idx\n",
    "                print(f\"Found age data in row {row_idx}: {unique_values[:5]}\")\n",
    "        \n",
    "        # Check for gender\n",
    "        if ('gender' in row_text or 'sex' in row_text) and gender_row is None:\n",
    "            unique_values = row_values.unique()\n",
    "            if len(unique_values) > 1:  # More than one unique value\n",
    "                gender_row = row_idx\n",
    "                print(f\"Found gender data in row {row_idx}: {unique_values[:5]}\")\n",
    "\n",
    "# Define conversion functions based on the identified data structure\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert height value to a continuous numeric value.\"\"\"\n",
    "    try:\n",
    "        # Try to extract a numeric value from the string\n",
    "        # Height may be in format like \"height: 180cm\" or similar\n",
    "        if value is None:\n",
    "            return None\n",
    "        \n",
    "        value = str(value).lower()\n",
    "        # Look for height patterns\n",
    "        if 'height' in value:\n",
    "            # Extract numeric part - look for digits\n",
    "            import re\n",
    "            height_match = re.search(r'(\\d+\\.?\\d*)', value)\n",
    "            if height_match:\n",
    "                return float(height_match.group(1))\n",
    "        # If it's just a number, try to convert directly\n",
    "        elif value.replace('.', '', 1).isdigit():\n",
    "            return float(value)\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"Error converting trait: {e}\")\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to a continuous numeric value.\"\"\"\n",
    "    try:\n",
    "        if value is None:\n",
    "            return None\n",
    "        \n",
    "        value = str(value).lower()\n",
    "        # Look for age patterns\n",
    "        if 'age' in value or 'years' in value:\n",
    "            # Extract numeric part\n",
    "            import re\n",
    "            age_match = re.search(r'(\\d+\\.?\\d*)', value)\n",
    "            if age_match:\n",
    "                return float(age_match.group(1))\n",
    "        # If it's just a number, try to convert directly\n",
    "        elif value.replace('.', '', 1).isdigit():\n",
    "            return float(value)\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"Error converting age: {e}\")\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male).\"\"\"\n",
    "    try:\n",
    "        if value is None:\n",
    "            return None\n",
    "        \n",
    "        value = str(value).lower()\n",
    "        # Check for gender/sex indicators\n",
    "        if 'female' in value or 'f' == value.strip() or 'f:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbad901",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "452bb99a",
   "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": "28b890b3",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89de7d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on reviewing the gene identifiers in the gene expression data, I can see they are numeric \n",
    "# identifiers (like 7, 8, 15, 18, etc.) rather than human gene symbols (which would be something \n",
    "# like BRCA1, TP53, etc.)\n",
    "# \n",
    "# These appear to be probe IDs from a microarray platform, which need to be mapped to human gene symbols\n",
    "# for proper biological interpretation. The numeric format is typical of Affymetrix or similar microarray \n",
    "# platforms where probes are identified by numbers rather than gene names.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc845dcc",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870ed6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
    "import gzip\n",
    "\n",
    "# Look at the first few lines of the SOFT file to understand its structure\n",
    "print(\"Examining SOFT file structure:\")\n",
    "try:\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        # Read first 20 lines to understand the file structure\n",
    "        for i, line in enumerate(file):\n",
    "            if i < 20:\n",
    "                print(f\"Line {i}: {line.strip()}\")\n",
    "            else:\n",
    "                break\n",
    "except Exception as e:\n",
    "    print(f\"Error reading SOFT file: {e}\")\n",
    "\n",
    "# 2. Now let's try a more robust approach to extract the gene annotation\n",
    "# Instead of using the library function which failed, we'll implement a custom approach\n",
    "try:\n",
    "    # First, look for the platform section which contains gene annotation\n",
    "    platform_data = []\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        in_platform_section = False\n",
    "        for line in file:\n",
    "            if line.startswith('^PLATFORM'):\n",
    "                in_platform_section = True\n",
    "                continue\n",
    "            if in_platform_section and line.startswith('!platform_table_begin'):\n",
    "                # Next line should be the header\n",
    "                header = next(file).strip()\n",
    "                platform_data.append(header)\n",
    "                # Read until the end of the platform table\n",
    "                for table_line in file:\n",
    "                    if table_line.startswith('!platform_table_end'):\n",
    "                        break\n",
    "                    platform_data.append(table_line.strip())\n",
    "                break\n",
    "    \n",
    "    # If we found platform data, convert it to a DataFrame\n",
    "    if platform_data:\n",
    "        import pandas as pd\n",
    "        import io\n",
    "        platform_text = '\\n'.join(platform_data)\n",
    "        gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
    "                                      low_memory=False, on_bad_lines='skip')\n",
    "        print(\"\\nGene annotation preview:\")\n",
    "        print(preview_df(gene_annotation))\n",
    "    else:\n",
    "        print(\"Could not find platform table in SOFT file\")\n",
    "        \n",
    "        # Try an alternative approach - extract mapping from other sections\n",
    "        with gzip.open(soft_file, 'rt') as file:\n",
    "            for line in file:\n",
    "                if 'ANNOTATION information' in line or 'annotation information' in line:\n",
    "                    print(f\"Found annotation information: {line.strip()}\")\n",
    "                if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
    "                    print(f\"Platform title: {line.strip()}\")\n",
    "            \n",
    "except Exception as e:\n",
    "    print(f\"Error processing gene annotation: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06038669",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f15b5960",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspect the gene identifier columns in the annotation dataframe\n",
    "print(\"Column names in gene annotation dataframe:\")\n",
    "print(gene_annotation.columns)\n",
    "\n",
    "# Based on previous output, I can see that:\n",
    "# - The gene expression data uses numeric IDs (like 7, 8, 15...)\n",
    "# - The gene annotation dataframe has an 'ID' column that appears to contain similar numeric identifiers\n",
    "# - The 'GENE_SYMBOL' column contains the human gene symbols we need\n",
    "\n",
    "# 1. Identify the mapping columns\n",
    "probe_id_col = 'ID'\n",
    "gene_symbol_col = 'GENE_SYMBOL'\n",
    "\n",
    "# 2. Get the gene mapping dataframe by extracting the two relevant columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n",
    "print(f\"Number of probes with gene mapping: {len(mapping_df)}\")\n",
    "print(f\"Sample of the 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(f\"Dimensions of gene expression data after mapping: {gene_data.shape}\")\n",
    "print(f\"Sample of gene symbols and expression values:\")\n",
    "print(gene_data.head())\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 expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f477487",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1323b4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 1. Let's load the gene expression data\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",
    "# Normalize gene symbols using the NCBI Gene database information\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to the output file\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 clinical and genetic data\n",
    "# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n",
    "# Create a minimal clinical dataframe with the trait column (containing NaNs)\n",
    "sample_ids = normalized_gene_data.columns\n",
    "minimal_clinical_df = pd.DataFrame(index=sample_ids)\n",
    "minimal_clinical_df[trait] = np.nan  # Add the trait column with NaN values\n",
    "\n",
    "# If we have age and gender data from Step 2, add those columns\n",
    "if age_row is not None:\n",
    "    minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n",
    "\n",
    "if gender_row is not None:\n",
    "    minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n",
    "\n",
    "minimal_clinical_df.index.name = 'Sample'\n",
    "\n",
    "# Save this minimal clinical data for reference\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "minimal_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Create a linked dataset \n",
    "linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n",
    "linked_data.index.name = 'Sample'\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# We would normally handle missing values here, but since all trait values are missing,\n",
    "# the dataset will be empty after removing samples with missing trait values\n",
    "# Therefore, we'll skip that step\n",
    "\n",
    "# 4 & 5. Validate and save cohort information\n",
    "# Since trait_row was None in Step 2, we know Height data is not available\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "is_trait_available = False  # Height data is not available\n",
    "\n",
    "note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n",
    "\n",
    "# For datasets without trait data, we set is_biased to False\n",
    "# This indicates the dataset is not usable due to missing trait data, not due to bias\n",
    "is_biased = False\n",
    "\n",
    "# Final validation\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=is_gene_available, \n",
    "    is_trait_available=is_trait_available, \n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Since there is no trait data, the dataset is not usable for our association study\n",
    "# So we should not save it to out_data_file\n",
    "print(f\"Dataset usability: {is_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 does not contain Height data and cannot be used for association studies.\")"
   ]
  }
 ],
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