{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b0c75c7",
   "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 = \"Anxiety_disorder\"\n",
    "cohort = \"GSE60190\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Anxiety_disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Anxiety_disorder/GSE60190\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Anxiety_disorder/GSE60190.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Anxiety_disorder/gene_data/GSE60190.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Anxiety_disorder/clinical_data/GSE60190.csv\"\n",
    "json_path = \"../../output/preprocess/Anxiety_disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00fb8de",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "071404aa",
   "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": "67f8ed7f",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d746daa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "from typing import Dict, Any, Callable, Optional, List, Tuple\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data from DLPFC\n",
    "# using Illumina HumanHT-12 v3 microarray, which is suitable for our analysis\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Examining the sample characteristics dictionary to identify relevant rows\n",
    "\n",
    "# For trait, we can use row 3 which has 'dx' (diagnosis) with values including 'Control', 'ED', and 'OCD'\n",
    "trait_row = 3\n",
    "\n",
    "# For age, we can use row 5 which has 'age' values\n",
    "age_row = 5\n",
    "\n",
    "# For gender, we can use row 7 which has 'Sex' values\n",
    "gender_row = 7\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert anxiety disorder trait information to binary format.\n",
    "    For Anxiety_disorder as the trait of interest, we consider OCD as 1 (case) and Control as 0 (control).\n",
    "    Exclude other conditions like ED, MDD, etc.\n",
    "    \n",
    "    Args:\n",
    "        value: The raw trait value from the dataset\n",
    "    \n",
    "    Returns:\n",
    "        int: 1 for anxiety disorder (OCD), 0 for control, None for other conditions or missing values\n",
    "    \"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    diagnosis = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # For anxiety disorder, we consider OCD patients as cases\n",
    "    if diagnosis == 'OCD' or diagnosis == 'Tics':  # Tics can be related to anxiety disorders\n",
    "        return 1\n",
    "    elif diagnosis == 'Control':\n",
    "        return 0\n",
    "    else:\n",
    "        return None  # Exclude other diagnoses like ED, Bipolar, MDD\n",
    "\n",
    "def convert_age(value: str) -> float:\n",
    "    \"\"\"\n",
    "    Convert age information to continuous format.\n",
    "    \n",
    "    Args:\n",
    "        value: The raw age value from the dataset\n",
    "    \n",
    "    Returns:\n",
    "        float: Age in years, or None if missing\n",
    "    \"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        age_str = value.split(':', 1)[1].strip()\n",
    "        return float(age_str)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert gender information to binary format (0 for female, 1 for male).\n",
    "    \n",
    "    Args:\n",
    "        value: The raw gender value from the dataset\n",
    "    \n",
    "    Returns:\n",
    "        int: 0 for female, 1 for male, or None if missing\n",
    "    \"\"\"\n",
    "    if not value or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    gender = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if gender == 'F':\n",
    "        return 0\n",
    "    elif gender == 'M':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata - Perform initial filtering\n",
    "# Trait data is available since trait_row is not None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Use the validate_and_save_cohort_info function to save metadata\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",
    "# Skip this step as clinical_data from previous step is required but not available\n",
    "# This will be executed in a subsequent step when clinical_data is available\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207f157d",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40d230c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll provide a properly formatted solution for this step:\n",
    "\n",
    "```python\n",
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# Check what files are available in the directory\n",
    "print(f\"Checking files in: {in_cohort_dir}\")\n",
    "available_files = os.listdir(in_cohort_dir) if os.path.exists(in_cohort_dir) else []\n",
    "print(f\"Available files: {available_files}\")\n",
    "\n",
    "# For GEO datasets, we typically have matrix files and series_matrix files\n",
    "matrix_files = [f for f in available_files if 'matrix' in f.lower()]\n",
    "print(f\"Matrix files found: {matrix_files}\")\n",
    "\n",
    "# Check if the gene expression data is available\n",
    "is_gene_available = False\n",
    "for file in available_files:\n",
    "    if file.endswith('.soft') or file.endswith('.txt') or 'matrix' in file.lower():\n",
    "        try:\n",
    "            with open(os.path.join(in_cohort_dir, file), 'r') as f:\n",
    "                content = f.read(10000)  # Read first 10000 characters\n",
    "                # Look for indicators of gene expression data\n",
    "                if any(term in content.lower() for term in [\"gene_expression\", \"platform_id\", \"platform =\"]):\n",
    "                    is_gene_available = True\n",
    "                    break\n",
    "                # Filter out pure miRNA or methylation datasets\n",
    "                if all(term in content.lower() for term in [\"mirna\", \"microrna\"]) and \"gene expression\" not in content.lower():\n",
    "                    is_gene_available = False\n",
    "                if \"methylation\" in content.lower() and \"gene expression\" not in content.lower():\n",
    "                    is_gene_available = False\n",
    "        except Exception as e:\n",
    "            print(f\"Error checking file {file}: {e}\")\n",
    "\n",
    "# Load sample characteristics if available\n",
    "sample_characteristics = {}\n",
    "clinical_data = None\n",
    "\n",
    "# Try different file patterns for clinical data\n",
    "possible_clinical_files = [\n",
    "    os.path.join(in_cohort_dir, \"clinical_data.csv\"),\n",
    "    os.path.join(in_cohort_dir, \"GSE60190_series_matrix.txt\"),\n",
    "    os.path.join(in_cohort_dir, \"series_matrix.txt\")\n",
    "]\n",
    "\n",
    "for file_path in possible_clinical_files:\n",
    "    if os.path.exists(file_path):\n",
    "        print(f\"Found clinical data file: {file_path}\")\n",
    "        if file_path.endswith('.csv'):\n",
    "            clinical_data = pd.read_csv(file_path)\n",
    "        else:\n",
    "            # For series_matrix files, we need to parse the !Sample_characteristics lines\n",
    "            try:\n",
    "                with open(file_path, 'r') as f:\n",
    "                    lines = f.readlines()\n",
    "                \n",
    "                # Extract sample characteristics lines\n",
    "                char_lines = [line.strip() for line in lines if line.startswith(\"!Sample_characteristics\")]\n",
    "                \n",
    "                # Parse sample characteristics\n",
    "                for i, line in enumerate(char_lines):\n",
    "                    # Extract values after the equals sign\n",
    "                    values = [part.split(\"=\")[1].strip() if \"=\" in part else part.strip() \n",
    "                             for part in line.split(\"\\t\")[1:]]\n",
    "                    if values:\n",
    "                        sample_characteristics[i] = values\n",
    "                \n",
    "                # Also create a dataframe from the characteristics\n",
    "                if sample_characteristics:\n",
    "                    # Convert to a format suitable for a dataframe\n",
    "                    samples = list(set([val for sublist in sample_characteristics.values() for val in sublist]))\n",
    "                    clinical_data = pd.DataFrame(index=range(len(sample_characteristics)), \n",
    "                                               columns=['characteristic'] + samples)\n",
    "                    for i, values in sample_characteristics.items():\n",
    "                        clinical_data.iloc[i, 0] = f\"characteristic_{i}\"\n",
    "                        for val in values:\n",
    "                            clinical_data.loc[i, val] = val\n",
    "            except Exception as e:\n",
    "                print(f\"Error parsing series matrix file: {e}\")\n",
    "        break\n",
    "\n",
    "if clinical_data is None and sample_characteristics:\n",
    "    # If we have sample characteristics but no dataframe, create one\n",
    "    clinical_data = pd.DataFrame()\n",
    "    for i, values in sample_characteristics.items():\n",
    "        clinical_data.loc[i, 'characteristic'] = f\"characteristic_{i}\"\n",
    "        for val in values:\n",
    "            clinical_data.loc[i, val] = val\n",
    "\n",
    "# Also check for a background info file\n",
    "background_info = \"\"\n",
    "background_path = os.path.join(in_cohort_dir, \"background_info.txt\")\n",
    "if os.path.exists(background_path):\n",
    "    with open(background_path, 'r') as f:\n",
    "        background_info = f.read()\n",
    "    print(\"\\nBackground Info:\")\n",
    "    print(background_info)\n",
    "\n",
    "# Print sample characteristics for analysis\n",
    "print(\"\\nSample Characteristics:\")\n",
    "for key, values in sample_characteristics.items():\n",
    "    print(f\"Row {key}: {values}\")\n",
    "\n",
    "# Based on available information, determine trait, age, and gender data\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Check each row in sample characteristics to identify relevant data\n",
    "for key, values in sample_characteristics.items():\n",
    "    # Convert values to strings for easier analysis\n",
    "    str_values = [str(v).lower() if v is not None else \"\" for v in values]\n",
    "    joined_values = \" \".join(str_values).lower()\n",
    "    \n",
    "    # Look for anxiety-related terms\n",
    "    if any(term in joined_values for term in [\"anxiety\", \"anxious\", \"anx\", \"gad\", \"panic\", \"diagnosis\", \"condition\", \"disorder\"]):\n",
    "        trait_row = key\n",
    "    \n",
    "    # Look for age-related terms\n",
    "    if any(term in joined_values for term in [\"age\", \"years\", \"yr\", \"yrs\"]):\n",
    "        age_row = key\n",
    "    \n",
    "    # Look for gender-related terms\n",
    "    if any(term in joined_values for term in [\"gender\", \"sex\", \"male\", \"female\"]):\n",
    "        gender_row = key\n",
    "\n",
    "# Define conversion functions\n",
    "def convert_trait(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    value_lower = str(value).lower()\n",
    "    \n",
    "    # Look for anxiety indicators\n",
    "    if any(term in value_lower for term in [\"anxiety\", \"anxious\", \"anxiety disorder\", \"gad\", \"panic\"]):\n",
    "        return 1\n",
    "    elif any(term in value_lower for term in [\"control\", \"healthy\", \"normal\", \"none\"]):\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric age\n",
    "    import re\n",
    "    age_match = re.search(r'(\\d+\\.?\\d*)', str(value))\n",
    "    if age_match:\n",
    "        return float(age_match.group(1))\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    value_lower = str(value).lower()\n",
    "    \n",
    "    if any(term in value_lower for term in [\"female\", \"f\", \"woman\", \"women\"]):\n",
    "        return 0\n",
    "    elif any(term in value_lower for term in [\"male\", \"m\", \"man\", \"men\"]):\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata - Initial Filtering\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction (if trait data is available and clinical data exists)\n",
    "if trait_row is not None and clinical_data is not None:\n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features("
   ]
  }
 ],
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}