{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b686e511",
   "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 = \"Age-Related_Macular_Degeneration\"\n",
    "cohort = \"GSE38662\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Age-Related_Macular_Degeneration\"\n",
    "in_cohort_dir = \"../../input/GEO/Age-Related_Macular_Degeneration/GSE38662\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/GSE38662.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE38662.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE38662.csv\"\n",
    "json_path = \"../../output/preprocess/Age-Related_Macular_Degeneration/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "109f45fe",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6e32145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This step has already been executed successfully. There's no code to debug.\n",
    "# The output shows we have retrieved:\n",
    "# - Background information about human embryonic stem cells\n",
    "# - Sample characteristics showing cell types, cell lines, passages, and gender\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132c7fca",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2ab62eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "```python\n",
    "import pandas as pd\n",
    "import json\n",
    "import os\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "import glob\n",
    "\n",
    "# Let's first check what files are actually available in the cohort directory\n",
    "print(f\"Looking for files in: {in_cohort_dir}\")\n",
    "available_files = glob.glob(os.path.join(in_cohort_dir, \"*\"))\n",
    "print(\"Available files:\", available_files)\n",
    "\n",
    "# Check for gene expression data (looking for common file patterns)\n",
    "gene_file_patterns = [\"*_series_matrix.txt\", \"*gene*.txt\", \"*expression*.txt\", \"*.CEL\", \"*.gpr\"]\n",
    "gene_files = []\n",
    "for pattern in gene_file_patterns:\n",
    "    gene_files.extend(glob.glob(os.path.join(in_cohort_dir, pattern)))\n",
    "\n",
    "is_gene_available = len(gene_files) > 0\n",
    "print(f\"Gene expression files found: {gene_files}\")\n",
    "print(f\"is_gene_available: {is_gene_available}\")\n",
    "\n",
    "# Look for clinical data files (could be in various formats)\n",
    "clinical_file_patterns = [\"*clinical*.txt\", \"*pheno*.txt\", \"*sample*.txt\", \"*_series_matrix.txt\"]\n",
    "clinical_files = []\n",
    "for pattern in clinical_file_patterns:\n",
    "    clinical_files.extend(glob.glob(os.path.join(in_cohort_dir, pattern)))\n",
    "\n",
    "# Initialize variables\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "clinical_data = None\n",
    "\n",
    "# Check if any clinical files were found\n",
    "if clinical_files:\n",
    "    print(f\"Potential clinical data files: {clinical_files}\")\n",
    "    \n",
    "    # Try to read the first available clinical file\n",
    "    # Start with series matrix file if available as it often contains sample characteristics\n",
    "    series_matrix_files = glob.glob(os.path.join(in_cohort_dir, \"*_series_matrix.txt\"))\n",
    "    \n",
    "    if series_matrix_files:\n",
    "        try:\n",
    "            # For series matrix files, we need to extract the sample characteristics\n",
    "            with open(series_matrix_files[0], 'r') as f:\n",
    "                lines = f.readlines()\n",
    "            \n",
    "            # Extract sample characteristic lines\n",
    "            char_lines = [line for line in lines if line.startswith(\"!Sample_characteristics_ch\")]\n",
    "            \n",
    "            if char_lines:\n",
    "                # Convert to dataframe\n",
    "                data = []\n",
    "                for line in char_lines:\n",
    "                    parts = line.strip().split('\\t')\n",
    "                    if len(parts) > 1:\n",
    "                        data.append(parts[1:])  # Skip the first part which is the header\n",
    "                \n",
    "                if data:\n",
    "                    clinical_data = pd.DataFrame(data)\n",
    "                    print(\"Clinical data shape from series matrix:\", clinical_data.shape)\n",
    "                    print(clinical_data.head())\n",
    "                    \n",
    "                    # Print unique values for each row to identify trait, age, and gender\n",
    "                    for i in range(len(clinical_data.index)):\n",
    "                        unique_values = clinical_data.iloc[i].unique()\n",
    "                        print(f\"Row {i} unique values: {unique_values}\")\n",
    "                        \n",
    "                        # Look for trait-related terms in the unique values\n",
    "                        values_str = ' '.join(str(v).lower() for v in unique_values)\n",
    "                        if any(term in values_str for term in ['amd', 'macular degeneration', 'disease', 'diagnosis', 'status']):\n",
    "                            trait_row = i\n",
    "                            print(f\"Potential trait row found at index {i}\")\n",
    "                        \n",
    "                        # Look for age-related terms\n",
    "                        if any(term in values_str for term in ['age', 'years']):\n",
    "                            age_row = i\n",
    "                            print(f\"Potential age row found at index {i}\")\n",
    "                        \n",
    "                        # Look for gender-related terms\n",
    "                        if any(term in values_str for term in ['gender', 'sex', 'male', 'female']):\n",
    "                            gender_row = i\n",
    "                            print(f\"Potential gender row found at index {i}\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error reading series matrix file: {e}\")\n",
    "\n",
    "# Check the background information file if it exists\n",
    "background_path = os.path.join(in_cohort_dir, \"background.txt\")\n",
    "if os.path.exists(background_path):\n",
    "    with open(background_path, 'r') as f:\n",
    "        background_info = f.read()\n",
    "    print(\"\\nBackground Information:\")\n",
    "    print(background_info)\n",
    "    \n",
    "    # Look for clues in background info about trait, age, and gender\n",
    "    bg_lower = background_info.lower()\n",
    "    \n",
    "    # If we haven't found trait info yet, check background\n",
    "    if trait_row is None and ('amd' in bg_lower or 'macular degeneration' in bg_lower):\n",
    "        print(\"Background information suggests this dataset is related to AMD.\")\n",
    "        \n",
    "    # Check for mentions of age uniformity or restrictions\n",
    "    if 'all patients are' in bg_lower and 'age' in bg_lower:\n",
    "        print(\"Background suggests age might be uniform across samples.\")\n",
    "        \n",
    "    # Check for mentions of gender uniformity or restrictions\n",
    "    if 'all patients are' in bg_lower and ('male' in bg_lower or 'female' in bg_lower):\n",
    "        print(\"Background suggests gender might be uniform across samples.\")\n",
    "\n",
    "# Data type conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0: control, 1: case)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if it exists\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    if 'control' in value or 'normal' in value or 'healthy' in value:\n",
    "        return 0\n",
    "    elif 'amd' in value or 'case' in value or 'patient' in value or 'disease' in value or 'macular degeneration' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if it exists\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to convert to float\n",
    "    try:\n",
    "        # Extract numbers if mixed with text\n",
    "        import re\n",
    "        numbers = re.findall(r'\\d+', str(value))\n",
    "        if numbers:\n",
    "            return float(numbers[0])\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0: female, 1: male)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if it exists\n",
    "    if ':' in str(value):\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    if 'female' in value or 'f' == value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value:\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 for initial filtering\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# Extract clinical features if trait data is available\n",
    "if is_trait_available and clinical_data is not None:\n",
    "    # Use the geo_select_clinical_features function to 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 selected clinical features\n",
    "    print(\"\\nSelected Clinical Features Preview:\")\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(preview)\n",
    "    \n",
    "    #\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74492f5",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fc4d5f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c3ec6f90",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7bec5dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d8715b9f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b68e34c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e6e5380a",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f23df00",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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