{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6332184",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:58.899890Z",
     "iopub.status.busy": "2025-03-25T06:23:58.899783Z",
     "iopub.status.idle": "2025-03-25T06:23:59.065214Z",
     "shell.execute_reply": "2025-03-25T06:23:59.064871Z"
    }
   },
   "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 = \"Allergies\"\n",
    "cohort = \"GSE205151\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Allergies\"\n",
    "in_cohort_dir = \"../../input/GEO/Allergies/GSE205151\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Allergies/GSE205151.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Allergies/gene_data/GSE205151.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Allergies/clinical_data/GSE205151.csv\"\n",
    "json_path = \"../../output/preprocess/Allergies/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80178eff",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "698ac1fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:59.066656Z",
     "iopub.status.busy": "2025-03-25T06:23:59.066514Z",
     "iopub.status.idle": "2025-03-25T06:23:59.094086Z",
     "shell.execute_reply": "2025-03-25T06:23:59.093793Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Functional Immunophenotyping of Children with Critical Status Asthmaticus Identifies Differential Gene Expression Responses in Neutrophils Exposed to a Poly(I:C) Stimulus\"\n",
      "!Series_summary\t\"We determined whether we could identify clusters of children with critical asthma by functional immunophenotyping using an intracellular viral analog stimulus.\"\n",
      "!Series_summary\t\"We performed a single-center, prospective, observational cohort study of 43 children ages 6 – 17 years admitted to a pediatric intensive care unit for an asthma attack between July 2019 to February 2021.\"\n",
      "!Series_overall_design\t\"Neutrophils were isolated from children, stimulated overnight with LyoVec poly(I:C), and mRNA was analyzed using a targeted Nanostring immunology array. Network analysis of the differentially expressed transcripts for the paired LyoVec poly(I:C) samples was performed.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['polyic_stimulation: Unstimulated', 'polyic_stimulation: Stimulated', 'polyic_stimulation: No'], 1: ['cluster: 1', 'cluster: 2', nan]}\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": "c453aba9",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59a13bc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:59.095086Z",
     "iopub.status.busy": "2025-03-25T06:23:59.094980Z",
     "iopub.status.idle": "2025-03-25T06:23:59.099737Z",
     "shell.execute_reply": "2025-03-25T06:23:59.099466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data file not found. Unable to extract clinical features.\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data (mRNA analyzed using Nanostring immunology array)\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Looking at the Sample Characteristics Dictionary, we have:\n",
    "# - Key 0: 'polyic_stimulation' (Unstimulated, Stimulated, No)\n",
    "# - Key 1: 'cluster' (1, 2, nan)\n",
    "\n",
    "# For the allergy trait (asthma in this case), we can use the 'cluster' field\n",
    "# The study mentions clusters of children with critical asthma\n",
    "trait_row = 1\n",
    "\n",
    "# Age and gender are not available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait (cluster) to binary value (0 or 1)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon and strip whitespace\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert cluster values to binary (0 for cluster 1, 1 for cluster 2)\n",
    "    try:\n",
    "        cluster = int(value)\n",
    "        if cluster == 1:\n",
    "            return 0\n",
    "        elif cluster == 2:\n",
    "            return 1\n",
    "        else:\n",
    "            return None\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value (not used in this dataset)\"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary value (not used in this dataset)\"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info\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 need to extract clinical features\n",
    "if trait_row is not None:\n",
    "    try:\n",
    "        # Look for the sample characteristics data which should be available from previous steps\n",
    "        # Each cohort typically has a characteristics.csv file from GEO processing\n",
    "        clinical_data_file = os.path.join(in_cohort_dir, \"characteristics.csv\")\n",
    "        clinical_data = pd.read_csv(clinical_data_file, index_col=0)\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 extracted clinical features\n",
    "        clinical_preview = preview_df(selected_clinical_df)\n",
    "        print(\"Clinical Data Preview:\")\n",
    "        print(clinical_preview)\n",
    "        \n",
    "        # Save the 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",
    "    except FileNotFoundError:\n",
    "        print(f\"Clinical data file not found. Unable to extract clinical features.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98adb4d2",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd22eac2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:59.100733Z",
     "iopub.status.busy": "2025-03-25T06:23:59.100634Z",
     "iopub.status.idle": "2025-03-25T06:23:59.118507Z",
     "shell.execute_reply": "2025-03-25T06:23:59.118228Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['ABCB1', 'ABCF1', 'ABL1', 'ADA', 'AHR', 'AICDA', 'AIRE', 'ALAS1', 'APP',\n",
      "       'ARG1', 'ARG2', 'ARHGDIB', 'ATG10', 'ATG12', 'ATG16L1', 'ATG5', 'ATG7',\n",
      "       'ATM', 'B2M', 'B3GAT1'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d9de18",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3becceb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:59.119487Z",
     "iopub.status.busy": "2025-03-25T06:23:59.119387Z",
     "iopub.status.idle": "2025-03-25T06:23:59.121049Z",
     "shell.execute_reply": "2025-03-25T06:23:59.120785Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers appear to be standard human gene symbols (like ABCB1, ATG5, B2M)\n",
    "# They follow the standard HGNC gene nomenclature and are recognizable as common human genes\n",
    "# No mapping is needed as they are already in the preferred format\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1fe33fb",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "274cb43b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:23:59.122023Z",
     "iopub.status.busy": "2025-03-25T06:23:59.121919Z",
     "iopub.status.idle": "2025-03-25T06:23:59.375666Z",
     "shell.execute_reply": "2025-03-25T06:23:59.375301Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: (576, 144)\n",
      "Normalized gene data saved to ../../output/preprocess/Allergies/gene_data/GSE205151.csv\n",
      "Loading the original clinical data...\n",
      "Extracting clinical features...\n",
      "Clinical data preview:\n",
      "{'GSM6205808': [0.0], 'GSM6205809': [0.0], 'GSM6205810': [1.0], 'GSM6205811': [1.0], 'GSM6205812': [0.0], 'GSM6205813': [0.0], 'GSM6205814': [1.0], 'GSM6205815': [1.0], 'GSM6205816': [1.0], 'GSM6205817': [1.0], 'GSM6205818': [1.0], 'GSM6205819': [1.0], 'GSM6205820': [0.0], 'GSM6205821': [0.0], 'GSM6205822': [1.0], 'GSM6205823': [1.0], 'GSM6205824': [1.0], 'GSM6205825': [1.0], 'GSM6205826': [1.0], 'GSM6205827': [1.0], 'GSM6205828': [0.0], 'GSM6205829': [0.0], 'GSM6205830': [1.0], 'GSM6205831': [1.0], 'GSM6205832': [1.0], 'GSM6205833': [1.0], 'GSM6205834': [0.0], 'GSM6205835': [0.0], 'GSM6205836': [0.0], 'GSM6205837': [0.0], 'GSM6205838': [0.0], 'GSM6205839': [0.0], 'GSM6205840': [1.0], 'GSM6205841': [1.0], 'GSM6205842': [0.0], 'GSM6205843': [0.0], 'GSM6205844': [1.0], 'GSM6205845': [1.0], 'GSM6205846': [1.0], 'GSM6205847': [1.0], 'GSM6205848': [0.0], 'GSM6205849': [0.0], 'GSM6205850': [0.0], 'GSM6205851': [0.0], 'GSM6205852': [0.0], 'GSM6205853': [0.0], 'GSM6205854': [0.0], 'GSM6205855': [0.0], 'GSM6205856': [0.0], 'GSM6205857': [0.0], 'GSM6205858': [1.0], 'GSM6205859': [1.0], 'GSM6205860': [0.0], 'GSM6205861': [0.0], 'GSM6205862': [0.0], 'GSM6205863': [0.0], 'GSM6205864': [0.0], 'GSM6205865': [0.0], 'GSM6205866': [0.0], 'GSM6205867': [0.0], 'GSM6205868': [0.0], 'GSM6205869': [0.0], 'GSM6205870': [0.0], 'GSM6205871': [0.0], 'GSM6205872': [1.0], 'GSM6205873': [1.0], 'GSM6205874': [1.0], 'GSM6205875': [1.0], 'GSM6205876': [1.0], 'GSM6205877': [1.0], 'GSM6205878': [1.0], 'GSM6205879': [1.0], 'GSM6205880': [1.0], 'GSM6205881': [1.0], 'GSM6205882': [0.0], 'GSM6205883': [0.0], 'GSM6205884': [1.0], 'GSM6205885': [1.0], 'GSM6205886': [1.0], 'GSM6205887': [1.0], 'GSM6205888': [1.0], 'GSM6205889': [1.0], 'GSM6205890': [1.0], 'GSM6205891': [1.0], 'GSM6205892': [0.0], 'GSM6205893': [0.0], 'GSM6205894': [1.0], 'GSM6205895': [1.0], 'GSM6205896': [0.0], 'GSM6205897': [0.0], 'GSM6205898': [1.0], 'GSM6205899': [1.0], 'GSM6205900': [0.0], 'GSM6205901': [0.0], 'GSM6205902': [1.0], 'GSM6205903': [1.0], 'GSM6205904': [0.0], 'GSM6205905': [1.0], 'GSM6205906': [0.0], 'GSM6205907': [1.0], 'GSM6205908': [0.0], 'GSM6205909': [1.0], 'GSM6205910': [1.0], 'GSM6205911': [1.0], 'GSM6205912': [1.0], 'GSM6205913': [1.0], 'GSM6205914': [0.0], 'GSM6205915': [1.0], 'GSM6205916': [1.0], 'GSM6205917': [1.0], 'GSM6205918': [0.0], 'GSM6205919': [0.0], 'GSM6205920': [0.0], 'GSM6205921': [1.0], 'GSM6205922': [0.0], 'GSM6205923': [0.0], 'GSM6205924': [0.0], 'GSM6205925': [nan], 'GSM6205926': [0.0], 'GSM6205927': [0.0], 'GSM6205928': [0.0], 'GSM6205929': [0.0], 'GSM6205930': [1.0], 'GSM6205931': [0.0], 'GSM6205932': [0.0], 'GSM6205933': [0.0], 'GSM6205934': [0.0], 'GSM6205935': [1.0], 'GSM6205936': [0.0], 'GSM6205937': [1.0], 'GSM6205938': [1.0], 'GSM6205939': [1.0], 'GSM6205940': [1.0], 'GSM6205941': [1.0], 'GSM6205942': [1.0], 'GSM6205943': [1.0], 'GSM6205944': [1.0], 'GSM6205945': [0.0], 'GSM6205946': [0.0], 'GSM6205947': [1.0], 'GSM6205948': [0.0], 'GSM6205949': [1.0], 'GSM6205950': [0.0], 'GSM6205951': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Allergies/clinical_data/GSE205151.csv\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (144, 577)\n",
      "Handling missing values...\n",
      "Linked data shape after handling missing values: (143, 577)\n",
      "Checking for bias in trait distribution...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the feature 'Allergies', the least common label is '0.0' with 69 occurrences. This represents 48.25% of the dataset.\n",
      "The distribution of the feature 'Allergies' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset usability: True\n",
      "Linked data saved to ../../output/preprocess/Allergies/GSE205151.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\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 a CSV 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 the clinical and genetic data\n",
    "print(\"Loading the original clinical data...\")\n",
    "# Get the matrix file again to ensure we have the proper data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "print(\"Extracting clinical features...\")\n",
    "# Use the clinical_data obtained directly from the matrix file\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",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Save the clinical data to a CSV file\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",
    "# Link clinical and genetic data using the normalized gene data\n",
    "print(\"Linking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"Handling missing values...\")\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check if trait is biased\n",
    "print(\"Checking for bias in trait distribution...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression data from patients with Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (PMF).\"\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",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "\n",
    "# 6. Save 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 trait-gene association studies due to bias or other issues.\")"
   ]
  }
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