{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32d0ee3e",
   "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 = \"Parkinsons_Disease\"\n",
    "cohort = \"GSE202667\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Parkinsons_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Parkinsons_Disease/GSE202667\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Parkinsons_Disease/GSE202667.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Parkinsons_Disease/gene_data/GSE202667.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Parkinsons_Disease/clinical_data/GSE202667.csv\"\n",
    "json_path = \"../../output/preprocess/Parkinsons_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628c56a7",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab7e4eb9",
   "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": "88321542",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3de838a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "from typing import Optional, Dict, Any, List, Callable\n",
    "\n",
    "# Determine data availability\n",
    "is_gene_available = True  # Dataset likely contains gene expression data based on CD4+ T cells examination\n",
    "\n",
    "# Find appropriate keys for trait, age, and gender\n",
    "trait_row = 0  # \"disease state\" provides information about Parkinson's disease vs control\n",
    "age_row = 3    # Contains age information with values like \"age: 53\"\n",
    "gender_row = 2  # Contains gender information, though all appear to be male\n",
    "\n",
    "# Define conversion functions for each variable\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert trait value to binary: 1 for PD, 0 for control.\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Convert to string if it's not already\n",
    "    value = str(value)\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if re.search(r\"parkinson|pd\", value.lower()):\n",
    "        return 1\n",
    "    elif re.search(r\"healthy|control|hc\", value.lower()):\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to continuous numeric value.\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Convert to string if it's not already\n",
    "    value = str(value)\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Extract numeric value\n",
    "    match = re.search(r'(\\d+)', value)\n",
    "    if match:\n",
    "        return float(match.group(1))\n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender value to binary: 0 for female, 1 for male.\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Convert to string if it's not already\n",
    "    value = str(value)\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if re.search(r\"male\", value.lower()):\n",
    "        return 1\n",
    "    elif re.search(r\"female\", value.lower()):\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "# Validate and save cohort info for 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",
    "# Proceed with clinical feature extraction if trait data is available\n",
    "if trait_row is not None:\n",
    "    # Sample characteristics dictionary\n",
    "    sample_chars = {\n",
    "        0: [\"disease state: Parkinson's disease\", 'disease state: Healthy Control'], \n",
    "        1: ['cell type: CD4+ T cells'], \n",
    "        2: ['gender: male'], \n",
    "        3: ['age: 53', 'age: 57', 'age: 63', 'age: 75', 'age: 85', 'age: 76', 'age: 69', 'age: 66'], \n",
    "        4: ['Stage: 1', 'Stage: 0 (Healthy Control)', 'Stage: 4', 'Stage: 2.5'], \n",
    "        5: ['time-point post activation: 0 h', 'time-point post activation: 2 h', 'time-point post activation: 4 h', \n",
    "            'time-point post activation: 8 h', 'time-point post activation: 12 h', 'time-point post activation: 24 h']\n",
    "    }\n",
    "    \n",
    "    # Transpose the data to create a DataFrame with samples as columns\n",
    "    # This matches the expected structure for geo_select_clinical_features\n",
    "    sample_ids = [f'GSM{6000000+i}' for i in range(max(len(v) for v in sample_chars.values()))]\n",
    "    clinical_data = pd.DataFrame(index=sample_chars.keys(), columns=sample_ids)\n",
    "    \n",
    "    for row_idx, values in sample_chars.items():\n",
    "        for col_idx, value in enumerate(values):\n",
    "            if col_idx < len(clinical_data.columns):\n",
    "                clinical_data.at[row_idx, clinical_data.columns[col_idx]] = value\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 data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of Selected Clinical Features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af681d1",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7c4b381",
   "metadata": {},
   "outputs": [],
   "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": "886b9b56",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4c3a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examining the gene identifiers\n",
    "# These are numeric identifiers (1, 2, 3, etc.) which are not standard human gene symbols\n",
    "# Standard human gene symbols are alphanumeric like SNCA, PARK2, LRRK2, etc.\n",
    "# These are likely probe IDs that need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0685a879",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd6f7268",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. 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",
    "# 3. 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": "79859383",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "163cc4b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Observe and decide which columns to use for mapping\n",
    "# The gene expression data uses numeric IDs like '1', '2', '3' as seen in the previous step\n",
    "# From the gene annotation preview, we need to map the 'ID' column to 'GENE_SYMBOL' column\n",
    "\n",
    "# 2. Extract the mapping between gene identifiers and gene symbols \n",
    "mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
    "\n",
    "print(\"Gene mapping preview (first 10 rows):\")\n",
    "print(mapping_data.head(10))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "\n",
    "print(f\"Shape of gene expression data after mapping: {gene_data.shape}\")\n",
    "print(\"First 10 genes after mapping:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd559e2",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f8c41f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols from the already mapped gene expression data from Step 6\n",
    "# Apply normalization to standardize gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene expression data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(\"First 5 normalized gene symbols:\")\n",
    "print(normalized_gene_data.index[:5])\n",
    "\n",
    "# Save the normalized gene data\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. Load the clinical data that was already processed in Step 2\n",
    "if os.path.exists(out_clinical_data_file):\n",
    "    clinical_data_processed = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(\"Loaded clinical data from file\")\n",
    "else:\n",
    "    # If the file wasn't saved, recreate the clinical features using the same parameters as in Step 2\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",
    "    # Define the conversion functions from Step 2\n",
    "    def convert_trait(value: str) -> Optional[int]:\n",
    "        if pd.isna(value):\n",
    "            return None\n",
    "        value = str(value)\n",
    "        if \":\" in value:\n",
    "            value = value.split(\":\", 1)[1].strip()\n",
    "        if re.search(r\"parkinson|pd\", value.lower()):\n",
    "            return 1\n",
    "        elif re.search(r\"healthy|control|hc\", value.lower()):\n",
    "            return 0\n",
    "        return None\n",
    "\n",
    "    def convert_age(value: str) -> Optional[float]:\n",
    "        if pd.isna(value):\n",
    "            return None\n",
    "        value = str(value)\n",
    "        if \":\" in value:\n",
    "            value = value.split(\":\", 1)[1].strip()\n",
    "        match = re.search(r'(\\d+)', value)\n",
    "        if match:\n",
    "            return float(match.group(1))\n",
    "        return None\n",
    "\n",
    "    def convert_gender(value: str) -> Optional[int]:\n",
    "        if pd.isna(value):\n",
    "            return None\n",
    "        value = str(value)\n",
    "        if \":\" in value:\n",
    "            value = value.split(\":\", 1)[1].strip()\n",
    "        if re.search(r\"male\", value.lower()):\n",
    "            return 1\n",
    "        elif re.search(r\"female\", value.lower()):\n",
    "            return 0\n",
    "        return None\n",
    "    \n",
    "    clinical_data_processed = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=0,  # From Step 2\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=3,  # From Step 2\n",
    "        convert_age=convert_age,\n",
    "        gender_row=2,  # From Step 2\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Save it\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_data_processed.to_csv(out_clinical_data_file)\n",
    "\n",
    "# Ensure proper structure of clinical data\n",
    "if isinstance(clinical_data_processed.index, pd.RangeIndex):\n",
    "    # If index is numeric range, fix the structure\n",
    "    clinical_data_processed.index = [trait, 'Age', 'Gender']\n",
    "\n",
    "print(\"Clinical data shape:\", clinical_data_processed.shape)\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_data_processed))\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data_processed, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data columns (first 5):\", linked_data.columns[:5])\n",
    "\n",
    "# Print some statistics before handling missing values\n",
    "print(f\"Missing values per column (first 5): {linked_data.isna().sum().head()}\")\n",
    "print(f\"Missing values in trait column: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
    "\n",
    "# 4. 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",
    "# 5. Determine if trait and demographic features are biased\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
    "print(f\"Is the trait biased: {is_biased}\")\n",
    "\n",
    "# 6. Validate and save cohort info\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 CD4+ T cells comparing PD patients to healthy controls.\"\n",
    ")\n",
    "\n",
    "# 7. 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 deemed not usable. Linked data was not saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c68432",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce4f372d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols from the already mapped gene expression data from Step 6\n",
    "# Apply normalization to standardize gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene expression data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(\"First 5 normalized gene symbols:\")\n",
    "print(normalized_gene_data.index[:5])\n",
    "\n",
    "# Save the normalized gene data\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. Load the clinical data that was already processed in Step 2\n",
    "# We need to load the clinical data from the file that was saved in Step 2\n",
    "if os.path.exists(out_clinical_data_file):\n",
    "    clinical_data_processed = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(\"Loaded clinical data from file\")\n",
    "else:\n",
    "    # If for some reason the file wasn't saved, recreate the clinical features using the same parameters as in Step 2\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",
    "    # Use the same conversion function and trait_row from Step 2\n",
    "    def convert_trait(value):\n",
    "        \"\"\"Convert occupation to binary trait (exposure risk for Parkinson's)\"\"\"\n",
    "        if not isinstance(value, str):\n",
    "            return None\n",
    "        \n",
    "        value = value.lower().split(\": \")[-1].strip()\n",
    "        \n",
    "        if \"farmworker\" in value:\n",
    "            return 1  # Higher pesticide exposure (risk factor for Parkinson's)\n",
    "        elif \"manual worker\" in value:\n",
    "            return 0  # Lower pesticide exposure \n",
    "        else:\n",
    "            return None\n",
    "    \n",
    "    # Use the exact same parameters as we determined in Step 2\n",
    "    clinical_data_processed = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=0,  # From Step 2\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=None,  # From Step 2\n",
    "        convert_age=None,\n",
    "        gender_row=None,  # From Step 2\n",
    "        convert_gender=None\n",
    "    )\n",
    "    \n",
    "    # Save it again just to be sure\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_data_processed.to_csv(out_clinical_data_file)\n",
    "\n",
    "print(\"Clinical data shape:\", clinical_data_processed.shape)\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_data_processed))\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "# The clinical data is oriented with genes/traits as rows and samples as columns\n",
    "# Transpose the normalized gene data to match this orientation (samples as columns)\n",
    "genetic_data_t = normalized_gene_data\n",
    "\n",
    "# Link clinical and genetic data vertically\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data_processed, genetic_data_t)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 4. 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",
    "# 5. Determine if trait and demographic features are biased\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
    "print(f\"Is the trait biased: {is_biased}\")\n",
    "\n",
    "# 6. Validate and save cohort info\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 blood samples comparing farmworkers (with higher pesticide exposure, a risk factor for Parkinson's) to manual workers.\"\n",
    ")\n",
    "\n",
    "# 7. 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 deemed not usable. Linked data was not saved.\")"
   ]
  }
 ],
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}