{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5da2182e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:38.239927Z",
     "iopub.status.busy": "2025-03-25T06:40:38.239692Z",
     "iopub.status.idle": "2025-03-25T06:40:38.408616Z",
     "shell.execute_reply": "2025-03-25T06:40:38.408161Z"
    }
   },
   "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 = \"Asthma\"\n",
    "cohort = \"GSE182798\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Asthma\"\n",
    "in_cohort_dir = \"../../input/GEO/Asthma/GSE182798\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Asthma/GSE182798.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Asthma/gene_data/GSE182798.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Asthma/clinical_data/GSE182798.csv\"\n",
    "json_path = \"../../output/preprocess/Asthma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f621fdc7",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c832158",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:38.410119Z",
     "iopub.status.busy": "2025-03-25T06:40:38.409961Z",
     "iopub.status.idle": "2025-03-25T06:40:38.680414Z",
     "shell.execute_reply": "2025-03-25T06:40:38.679927Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptomic profiling of adult-onset asthma related to damp and moldy buildings and idiopathic environmental intolerance\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['diagnosis: adult-onset asthma', 'diagnosis: IEI', 'diagnosis: healthy'], 1: ['gender: Female'], 2: ['age: 33.42', 'age: 46.08', 'age: 45.58', 'age: 28', 'age: 25.75', 'age: 59.83', 'age: 41.17', 'age: 47.58', 'age: 50.75', 'age: 42.58', 'age: 52.75', 'age: 51.75', 'age: 18.42', 'age: 47', 'age: 38.33', 'age: 58.58', 'age: 56.17', 'age: 40.67', 'age: 47.5', 'age: 54.67', 'age: 48.83', 'age: 64.67', 'age: 54.83', 'age: 57.67', 'age: 39.17', 'age: 38.08', 'age: 28.42', 'age: 40.75', 'age: 43.17', 'age: 43.08'], 3: ['cell type: PBMC', 'tissue: Nasal biopsy'], 4: [nan, 'subject: 605', 'subject: 611', 'subject: 621', 'subject: 35', 'subject: 11', 'subject: 1', 'subject: 601', 'subject: 54', 'subject: 68_A', 'subject: 55', 'subject: 44', 'subject: 603_A', 'subject: 63', 'subject: 39', 'subject: 13', 'subject: 3', 'subject: 619', 'subject: 58', 'subject: 79', 'subject: 77', 'subject: 41', 'subject: 624', 'subject: 37_A', 'subject: 61', 'subject: 31', 'subject: 25', 'subject: 617', 'subject: 65', 'subject: 81']}\n"
     ]
    }
   ],
   "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": "afe4d5ca",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abb1abcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:38.681686Z",
     "iopub.status.busy": "2025-03-25T06:40:38.681567Z",
     "iopub.status.idle": "2025-03-25T06:40:38.708352Z",
     "shell.execute_reply": "2025-03-25T06:40:38.707876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM5530417': [1.0, 33.42, 0.0], 'GSM5530418': [1.0, 46.08, 0.0], 'GSM5530419': [nan, 45.58, 0.0], 'GSM5530420': [0.0, 28.0, 0.0], 'GSM5530421': [0.0, 25.75, 0.0], 'GSM5530422': [1.0, 59.83, 0.0], 'GSM5530423': [0.0, 41.17, 0.0], 'GSM5530424': [1.0, 47.58, 0.0], 'GSM5530425': [1.0, 50.75, 0.0], 'GSM5530426': [0.0, 42.58, 0.0], 'GSM5530427': [1.0, 52.75, 0.0], 'GSM5530428': [1.0, 51.75, 0.0], 'GSM5530429': [0.0, 18.42, 0.0], 'GSM5530430': [0.0, 47.0, 0.0], 'GSM5530431': [0.0, 38.33, 0.0], 'GSM5530432': [1.0, 58.58, 0.0], 'GSM5530433': [1.0, 56.17, 0.0], 'GSM5530434': [1.0, 52.75, 0.0], 'GSM5530435': [nan, 40.67, 0.0], 'GSM5530436': [1.0, 47.5, 0.0], 'GSM5530437': [1.0, 54.67, 0.0], 'GSM5530438': [0.0, 48.83, 0.0], 'GSM5530439': [0.0, 25.75, 0.0], 'GSM5530440': [1.0, 64.67, 0.0], 'GSM5530441': [1.0, 54.83, 0.0], 'GSM5530442': [nan, 57.67, 0.0], 'GSM5530443': [nan, 39.17, 0.0], 'GSM5530444': [0.0, 38.08, 0.0], 'GSM5530445': [1.0, 28.42, 0.0], 'GSM5530446': [1.0, 40.75, 0.0], 'GSM5530447': [1.0, 43.17, 0.0], 'GSM5530448': [0.0, 43.08, 0.0], 'GSM5530449': [1.0, 48.83, 0.0], 'GSM5530450': [0.0, 58.83, 0.0], 'GSM5530451': [0.0, 26.58, 0.0], 'GSM5530452': [0.0, 42.5, 0.0], 'GSM5530453': [nan, 48.25, 0.0], 'GSM5530454': [1.0, 39.25, 0.0], 'GSM5530455': [1.0, 55.33, 0.0], 'GSM5530456': [0.0, 47.0, 0.0], 'GSM5530457': [1.0, 55.75, 0.0], 'GSM5530458': [1.0, 47.08, 0.0], 'GSM5530459': [nan, 47.5, 0.0], 'GSM5530460': [1.0, 53.58, 0.0], 'GSM5530461': [1.0, 60.17, 0.0], 'GSM5530462': [0.0, 40.58, 0.0], 'GSM5530463': [1.0, 50.5, 0.0], 'GSM5530464': [1.0, 46.17, 0.0], 'GSM5530465': [1.0, 51.33, 0.0], 'GSM5530466': [nan, 56.67, 0.0], 'GSM5530467': [0.0, 37.5, 0.0], 'GSM5530468': [0.0, 48.83, 0.0], 'GSM5530469': [1.0, 38.08, 0.0], 'GSM5530470': [1.0, 52.58, 0.0], 'GSM5530471': [0.0, 52.67, 0.0], 'GSM5530472': [1.0, 59.58, 0.0], 'GSM5530473': [1.0, 56.25, 0.0], 'GSM5530474': [nan, 46.42, 0.0], 'GSM5530475': [0.0, 47.08, 0.0], 'GSM5530476': [0.0, 52.67, 0.0], 'GSM5530477': [1.0, 60.08, 0.0], 'GSM5530478': [1.0, 44.67, 0.0], 'GSM5530479': [nan, 57.58, 0.0], 'GSM5530480': [0.0, 26.58, 0.0], 'GSM5530481': [nan, 53.5, 0.0], 'GSM5530482': [0.0, 58.83, 0.0], 'GSM5530483': [0.0, 41.5, 0.0], 'GSM5530484': [1.0, 47.17, 0.0], 'GSM5530485': [1.0, 51.25, 0.0], 'GSM5530486': [1.0, 33.08, 0.0], 'GSM5530487': [nan, 50.33, 0.0], 'GSM5530488': [nan, 60.17, 0.0], 'GSM5530489': [0.0, 19.17, 0.0], 'GSM5530490': [1.0, 40.67, 0.0], 'GSM5530491': [1.0, 24.25, 0.0], 'GSM5530492': [0.0, 43.08, 0.0], 'GSM5530493': [0.0, 51.75, 0.0], 'GSM5530494': [1.0, 41.17, 0.0], 'GSM5530495': [1.0, 30.83, 0.0], 'GSM5530496': [0.0, 40.58, 0.0], 'GSM5530497': [0.0, 42.58, 0.0], 'GSM5530498': [1.0, 52.75, 0.0], 'GSM5530499': [nan, 43.17, 0.0], 'GSM5530500': [1.0, 24.75, 0.0], 'GSM5530501': [0.0, 51.75, 0.0], 'GSM5530502': [1.0, 24.5, 0.0], 'GSM5530503': [1.0, 44.5, 0.0], 'GSM5530504': [nan, 53.17, 0.0], 'GSM5530505': [0.0, 38.08, 0.0], 'GSM5530506': [0.0, 37.83, 0.0], 'GSM5530507': [nan, 41.33, 0.0], 'GSM5530508': [1.0, 47.67, 0.0], 'GSM5530509': [1.0, 57.75, 0.0], 'GSM5530510': [0.0, 37.5, 0.0], 'GSM5530511': [0.0, 41.5, 0.0], 'GSM5530512': [1.0, 44.25, 0.0], 'GSM5530513': [nan, 53.58, 0.0], 'GSM5530514': [1.0, 45.58, 0.0], 'GSM5530515': [0.0, 19.17, 0.0], 'GSM5530516': [0.0, 18.42, 0.0], 'GSM5530517': [1.0, 57.08, 0.0], 'GSM5530518': [1.0, 60.67, 0.0], 'GSM5537157': [0.0, 38.33, 0.0], 'GSM5537158': [0.0, 38.08, 0.0], 'GSM5537159': [0.0, 48.83, 0.0], 'GSM5537160': [1.0, 33.42, 0.0], 'GSM5537161': [1.0, 46.08, 0.0], 'GSM5537162': [nan, 45.58, 0.0], 'GSM5537163': [0.0, 28.0, 0.0], 'GSM5537164': [1.0, 30.83, 0.0], 'GSM5537165': [1.0, 39.25, 0.0], 'GSM5537166': [nan, 60.17, 0.0], 'GSM5537167': [1.0, 52.75, 0.0], 'GSM5537168': [0.0, 25.75, 0.0], 'GSM5537169': [1.0, 60.67, 0.0], 'GSM5537170': [1.0, 64.67, 0.0], 'GSM5537171': [1.0, 54.83, 0.0], 'GSM5537172': [nan, 57.67, 0.0], 'GSM5537173': [0.0, 47.0, 0.0], 'GSM5537174': [1.0, 47.5, 0.0], 'GSM5537175': [1.0, 24.25, 0.0], 'GSM5537176': [1.0, 47.67, 0.0], 'GSM5537177': [1.0, 47.58, 0.0], 'GSM5537178': [0.0, 18.42, 0.0], 'GSM5537179': [nan, 41.33, 0.0], 'GSM5537180': [1.0, 24.5, 0.0], 'GSM5537181': [1.0, 47.08, 0.0], 'GSM5537182': [nan, 47.5, 0.0], 'GSM5537183': [0.0, 41.17, 0.0], 'GSM5537184': [1.0, 48.83, 0.0], 'GSM5537185': [1.0, 47.17, 0.0], 'GSM5537186': [1.0, 59.83, 0.0], 'GSM5537187': [0.0, 42.58, 0.0], 'GSM5537188': [nan, 56.67, 0.0], 'GSM5537189': [0.0, 37.5, 0.0], 'GSM5537190': [1.0, 58.58, 0.0], 'GSM5537191': [1.0, 24.75, 0.0], 'GSM5537192': [1.0, 52.75, 0.0], 'GSM5537193': [1.0, 55.33, 0.0], 'GSM5537194': [1.0, 56.17, 0.0], 'GSM5537195': [1.0, 52.75, 0.0], 'GSM5537196': [nan, 40.67, 0.0], 'GSM5537197': [0.0, 19.17, 0.0], 'GSM5537198': [0.0, 42.5, 0.0], 'GSM5537199': [1.0, 57.08, 0.0], 'GSM5537200': [0.0, 40.58, 0.0], 'GSM5537201': [1.0, 40.67, 0.0], 'GSM5537202': [1.0, 55.75, 0.0], 'GSM5537203': [1.0, 43.17, 0.0], 'GSM5537204': [1.0, 59.58, 0.0], 'GSM5537205': [1.0, 56.25, 0.0], 'GSM5537206': [nan, 46.42, 0.0], 'GSM5537207': [0.0, 47.08, 0.0], 'GSM5537208': [0.0, 51.75, 0.0], 'GSM5537209': [nan, 53.5, 0.0], 'GSM5537210': [1.0, 52.58, 0.0], 'GSM5537211': [nan, 52.25, 0.0], 'GSM5537212': [1.0, 45.58, 0.0], 'GSM5537213': [0.0, 52.67, 0.0], 'GSM5537214': [1.0, 50.5, 0.0], 'GSM5537215': [1.0, 60.08, 0.0], 'GSM5537216': [1.0, 44.67, 0.0], 'GSM5537217': [nan, 57.58, 0.0], 'GSM5537218': [nan, 53.17, 0.0], 'GSM5537219': [1.0, 51.33, 0.0], 'GSM5537220': [1.0, 46.17, 0.0], 'GSM5537221': [0.0, 26.58, 0.0], 'GSM5537222': [1.0, 60.17, 0.0], 'GSM5537223': [1.0, 54.67, 0.0], 'GSM5537224': [1.0, 57.75, 0.0], 'GSM5537225': [1.0, 28.42, 0.0], 'GSM5537226': [1.0, 33.08, 0.0], 'GSM5537227': [nan, 50.33, 0.0], 'GSM5537228': [0.0, 37.83, 0.0], 'GSM5537229': [1.0, 44.25, 0.0], 'GSM5537230': [0.0, 58.83, 0.0], 'GSM5537231': [nan, 48.25, 0.0], 'GSM5537232': [0.0, 43.08, 0.0], 'GSM5537233': [1.0, 41.17, 0.0], 'GSM5537234': [1.0, 51.75, 0.0], 'GSM5537235': [1.0, 53.58, 0.0], 'GSM5537236': [0.0, 41.5, 0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Asthma/clinical_data/GSE182798.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from typing import Optional, Callable, Any, Dict\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset seems to contain transcriptomic profiling\n",
    "# which suggests gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Identify keys for trait, age, and gender\n",
    "trait_row = 0  # diagnosis information is in row 0\n",
    "age_row = 2    # age information is in row 2\n",
    "gender_row = 1 # gender information is in row 1\n",
    "\n",
    "# 2.2 Define conversion functions\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert trait values to binary (0 for control, 1 for Asthma).\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if \"adult-onset asthma\" in value.lower():\n",
    "        return 1  # Asthma\n",
    "    elif \"healthy\" in value.lower():\n",
    "        return 0  # Control\n",
    "    else:\n",
    "        return None  # IEI or other conditions\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age values to float.\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender values to binary (0 for female, 1 for male).\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip().lower()\n",
    "    \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",
    "# Determine trait availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering validation\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",
    "    try:\n",
    "        # Assuming clinical_data was loaded in a previous step\n",
    "        # If not, we need to load it\n",
    "        if 'clinical_data' not in locals() and 'clinical_data' not in globals():\n",
    "            # Define the expected path for clinical data\n",
    "            clinical_data_path = os.path.join(in_cohort_dir, f\"{cohort}_sample_characteristics.csv\")\n",
    "            if os.path.exists(clinical_data_path):\n",
    "                clinical_data = pd.read_csv(clinical_data_path)\n",
    "            else:\n",
    "                print(f\"Clinical data file not found at {clinical_data_path}\")\n",
    "                # Consider alternative loading methods if needed\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 processed data\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of selected clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Create directory if it doesn't exist\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        \n",
    "        # Save to CSV\n",
    "        selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error in clinical feature extraction: {e}\")\n",
    "else:\n",
    "    print(\"Clinical data not available, skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db7a299",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d5f1e11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:38.709578Z",
     "iopub.status.busy": "2025-03-25T06:40:38.709462Z",
     "iopub.status.idle": "2025-03-25T06:40:39.230312Z",
     "shell.execute_reply": "2025-03-25T06:40:39.229585Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Asthma/GSE182798/GSE182798_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (39341, 182)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['A_19_P00315452', 'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315502',\n",
      "       'A_19_P00315506', 'A_19_P00315518', 'A_19_P00315529', 'A_19_P00315543',\n",
      "       'A_19_P00315551', 'A_19_P00315581', 'A_19_P00315584', 'A_19_P00315593',\n",
      "       'A_19_P00315603', 'A_19_P00315627', 'A_19_P00315631', 'A_19_P00315641',\n",
      "       'A_19_P00315647', 'A_19_P00315649', 'A_19_P00315668', 'A_19_P00315691'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "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}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b397369",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bf20cd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:39.231682Z",
     "iopub.status.busy": "2025-03-25T06:40:39.231561Z",
     "iopub.status.idle": "2025-03-25T06:40:39.234113Z",
     "shell.execute_reply": "2025-03-25T06:40:39.233663Z"
    }
   },
   "outputs": [],
   "source": [
    "# The identifiers in the gene expression data do not appear to be standard human gene symbols.\n",
    "# They appear to be Agilent microarray probe IDs (starting with \"A_19_P\"), which are platform-specific\n",
    "# identifiers that need to be mapped to standard gene symbols.\n",
    "# These probe IDs typically need mapping to official gene symbols for downstream analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f9ceda",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "891ba95e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:39.235305Z",
     "iopub.status.busy": "2025-03-25T06:40:39.235194Z",
     "iopub.status.idle": "2025-03-25T06:40:48.520112Z",
     "shell.execute_reply": "2025-03-25T06:40:48.519461Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_001105533', nan], 'GB_ACC': [nan, nan, nan, 'NM_001105533', nan], 'LOCUSLINK_ID': [nan, nan, nan, 79974.0, 54880.0], 'GENE_SYMBOL': [nan, nan, nan, 'CPED1', 'BCOR'], 'GENE_NAME': [nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1', 'BCL6 corepressor'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.189652', nan], 'ENSEMBL_ID': [nan, nan, nan, nan, 'ENST00000378463'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673', 'ens|ENST00000378463'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'unmapped', 'chr7:120901888-120901947', 'chrX:39909128-39909069'], 'CYTOBAND': [nan, nan, nan, 'hs|7q31.31', 'hs|Xp11.4'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]', 'BCL6 corepressor [Source:HGNC Symbol;Acc:HGNC:20893] [ENST00000378463]'], 'GO_ID': [nan, nan, nan, 'GO:0005783(endoplasmic reticulum)', 'GO:0000122(negative regulation of transcription from RNA polymerase II promoter)|GO:0000415(negative regulation of histone H3-K36 methylation)|GO:0003714(transcription corepressor activity)|GO:0004842(ubiquitin-protein ligase activity)|GO:0005515(protein binding)|GO:0005634(nucleus)|GO:0006351(transcription, DNA-dependent)|GO:0007507(heart development)|GO:0008134(transcription factor binding)|GO:0030502(negative regulation of bone mineralization)|GO:0031072(heat shock protein binding)|GO:0031519(PcG protein complex)|GO:0035518(histone H2A monoubiquitination)|GO:0042476(odontogenesis)|GO:0042826(histone deacetylase binding)|GO:0044212(transcription regulatory region DNA binding)|GO:0045892(negative regulation of transcription, DNA-dependent)|GO:0051572(negative regulation of histone H3-K4 methylation)|GO:0060021(palate development)|GO:0065001(specification of axis polarity)|GO:0070171(negative regulation of tooth mineralization)'], 'SEQUENCE': [nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA', 'CATCAAAGCTACGAGAGATCCTACACACCCAGATTTAAAAAATAATAAAAACTTAAGGGC'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760']}\n"
     ]
    }
   ],
   "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": "b0601c77",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbd8b415",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:48.521610Z",
     "iopub.status.busy": "2025-03-25T06:40:48.521474Z",
     "iopub.status.idle": "2025-03-25T06:40:51.041792Z",
     "shell.execute_reply": "2025-03-25T06:40:51.041154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping shape: (48862, 2)\n",
      "Sample of gene mapping:\n",
      "{'ID': ['A_33_P3396872', 'A_33_P3267760', 'A_32_P194264', 'A_23_P153745', 'A_21_P0014180'], 'Gene': ['CPED1', 'BCOR', 'CHAC2', 'IFI30', 'GPR146']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape after mapping: (22196, 182)\n",
      "First few gene symbols after mapping:\n",
      "['A1BG', 'A1BG-AS1', 'A1CF-3', 'A2M', 'A2M-1', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'AAAS']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Asthma/gene_data/GSE182798.csv\n"
     ]
    }
   ],
   "source": [
    "# From inspecting previous outputs, I can see:\n",
    "# - Gene expression data has identifiers like 'A_19_P00315452'\n",
    "# - Gene annotation data has 'ID' column with values like 'A_21_P0014386', 'A_33_P3396872', etc.\n",
    "# - The gene symbols are in the 'GENE_SYMBOL' column\n",
    "\n",
    "# 1. Determine which columns to use for mapping\n",
    "# The ID column in gene_annotation contains microarray probe IDs similar to gene expression indices\n",
    "# The GENE_SYMBOL column contains the human gene symbols we want to map to\n",
    "id_column = 'ID'\n",
    "gene_symbol_column = 'GENE_SYMBOL'\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting relevant columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, id_column, gene_symbol_column)\n",
    "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
    "print(\"Sample of gene mapping:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene-level expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols after mapping:\")\n",
    "print(list(gene_data.index[:10]))\n",
    "\n",
    "# Save the gene expression data to a CSV file\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": "e9e8434a",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34b10624",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:51.043279Z",
     "iopub.status.busy": "2025-03-25T06:40:51.043147Z",
     "iopub.status.idle": "2025-03-25T06:41:07.096835Z",
     "shell.execute_reply": "2025-03-25T06:41:07.096154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Asthma/gene_data/GSE182798.csv\n",
      "Clinical data saved to ../../output/preprocess/Asthma/clinical_data/GSE182798.csv\n",
      "Linked data shape: (182, 18157)\n",
      "Linked data preview (first 5 rows, 5 columns):\n",
      "            Asthma    Age  Gender     A1BG  A1BG-AS1\n",
      "GSM5530417     1.0  33.42     0.0  9.58489   6.85151\n",
      "GSM5530418     1.0  46.08     0.0  9.65673   6.74284\n",
      "GSM5530419     NaN  45.58     0.0  9.55825   6.47113\n",
      "GSM5530420     0.0  28.00     0.0  9.71055   6.90045\n",
      "GSM5530421     0.0  25.75     0.0  9.22204   6.75952\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (152, 18157)\n",
      "For the feature 'Asthma', the least common label is '0.0' with 57 occurrences. This represents 37.50% of the dataset.\n",
      "The distribution of the feature 'Asthma' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 38.33\n",
      "  50% (Median): 47.0\n",
      "  75%: 52.75\n",
      "Min: 18.42\n",
      "Max: 64.67\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "For the feature 'Gender', the least common label is '0.0' with 152 occurrences. This represents 100.00% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is severely biased.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Asthma/GSE182798.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_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\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Define the correct convert_trait function as established in Step 2\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert trait values to binary (0 for control, 1 for Asthma).\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if \"adult-onset asthma\" in value.lower():\n",
    "        return 1  # Asthma\n",
    "    elif \"healthy\" in value.lower():\n",
    "        return 0  # Control\n",
    "    else:\n",
    "        return None  # IEI or other conditions\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age values to float.\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender values to binary (0 for female, 1 for male).\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip().lower()\n",
    "    \n",
    "    if \"female\" in value:\n",
    "        return 0\n",
    "    elif \"male\" in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Re-extract clinical features using the appropriate conversion functions\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=0,  # Correct trait row from Step 2\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=2,     # Age row from Step 2\n",
    "    convert_age=convert_age,\n",
    "    gender_row=1,  # Gender row from Step 2\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "# Save the processed 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)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 2. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
    "\n",
    "# 3. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Validate 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_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from adult patients with asthma related to damp/moldy buildings and controls.\"\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if 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 is not usable for analysis. No linked data file saved.\")"
   ]
  }
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