{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f52e1d98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:06.078709Z",
     "iopub.status.busy": "2025-03-25T06:07:06.078609Z",
     "iopub.status.idle": "2025-03-25T06:07:06.238911Z",
     "shell.execute_reply": "2025-03-25T06:07:06.238552Z"
    }
   },
   "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 = \"GSE101534\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Parkinsons_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Parkinsons_Disease/GSE101534\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Parkinsons_Disease/GSE101534.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Parkinsons_Disease/gene_data/GSE101534.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Parkinsons_Disease/clinical_data/GSE101534.csv\"\n",
    "json_path = \"../../output/preprocess/Parkinsons_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f5b50d",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b0dd491",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:06.240337Z",
     "iopub.status.busy": "2025-03-25T06:07:06.240192Z",
     "iopub.status.idle": "2025-03-25T06:07:06.396198Z",
     "shell.execute_reply": "2025-03-25T06:07:06.395834Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Genome-wide expression profiling of the LRRK2-G2019S mutation in hNES cells\"\n",
      "!Series_summary\t\"Parkinson’s disease (PD) has a neuro-developmental component with multiple genetic predispositions. The most prevalent mutation, LRRK2-G2019S is linked to familial and sporadic PD. Based on the multiple origins of PD and the incomplete penetrance of LRRK2-G2019S, we hypothesize that modifiers in the patient genetic background act as susceptibility factors for developing PD. To assess the developmental component of LRRK2-G2019S pathogenesis, we used 19 human iPSC-derived neuroepithelial stem cell lines (NESCs). Isogenic controls distinguish between LRRK2-G2019S dependent and independent cellular phenotypes. LRRK2-G2019S patient and healthy mutagenized lines showed altered NESC self-renewal. Within patients, phenotypes were only partly LRRK2-G2019S dependent, suggesting Parkinson’s disease (PD) has a neuro-developmental component with multiple genetic predispositions. The most prevalent mutation, LRRK2-G2019S is linked to familial and sporadic PD. Based on the multiple origins of PD and the incomplete penetrance of LRRK2-G2019S, we hypothesize that modifiers in the patient genetic background act as susceptibility factors for developing PD. To assess the developmental component of LRRK2-G2019S pathogenesis, we used 19 human iPSC-derived neuroepithelial stem cell lines (NESCs).\"\n",
      "!Series_summary\t\"Isogenic controls distinguish between LRRK2-G2019S dependent and independent cellular phenotypes. LRRK2-G2019S patient and healthy mutagenized lines showed altered NESC self-renewal. Within patients, phenotypes were only partly LRRK2-G2019S dependent, suggesting a significant contribution of the genetic background. We identified Serine racemase (SRR) as a novel patient-specific, developmental, genetic modifier contributing to the abberant phenotypes. Its enzymatic product, D-Serine, rescued altered NESC renewal. Susceptibility factors in the genetic background, such as SRR, could be new targets for early PD diagnosis and treatment.\"\n",
      "!Series_overall_design\t\"51 samples analyzed, 4 outliers excluded based on QC, 17 coditions, 3 biological replicates, 6 LRRK2G2019S patients, 6 healthy indiviuals, 3 gene corrected lines, 2 inserted mutated lines\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['mutation: healthy', 'mutation: patient', 'mutation: gene corrected', 'mutation: inserted G2019S']}\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": "db4213b4",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9ce7f80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:06.397708Z",
     "iopub.status.busy": "2025-03-25T06:07:06.397587Z",
     "iopub.status.idle": "2025-03-25T06:07:06.406517Z",
     "shell.execute_reply": "2025-03-25T06:07:06.406231Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical features preview: {'GSM2705776': [0.0], 'GSM2705777': [1.0], 'GSM2705778': [1.0], 'GSM2705779': [0.0], 'GSM2705780': [0.0], 'GSM2705781': [0.0], 'GSM2705782': [1.0], 'GSM2705783': [1.0], 'GSM2705784': [1.0], 'GSM2705785': [0.0], 'GSM2705786': [0.0], 'GSM2705787': [0.0], 'GSM2705788': [0.0], 'GSM2705789': [1.0], 'GSM2705790': [1.0], 'GSM2705791': [1.0], 'GSM2705792': [0.0], 'GSM2705793': [0.0], 'GSM2705794': [0.0], 'GSM2705795': [0.0], 'GSM2705796': [0.0], 'GSM2705797': [0.0], 'GSM2705798': [0.0], 'GSM2705799': [0.0], 'GSM2705800': [0.0], 'GSM2705801': [0.0], 'GSM2705802': [1.0], 'GSM2705803': [1.0], 'GSM2705804': [1.0], 'GSM2705805': [1.0], 'GSM2705806': [1.0], 'GSM2705807': [0.0], 'GSM2705808': [0.0], 'GSM2705809': [0.0], 'GSM2705810': [1.0], 'GSM2705811': [1.0], 'GSM2705812': [0.0], 'GSM2705813': [0.0], 'GSM2705814': [1.0], 'GSM2705815': [0.0], 'GSM2705816': [1.0], 'GSM2705817': [1.0], 'GSM2705818': [1.0], 'GSM2705819': [0.0], 'GSM2705820': [0.0], 'GSM2705821': [0.0], 'GSM2705822': [1.0], 'GSM2705823': [1.0], 'GSM2705824': [1.0], 'GSM2705825': [1.0], 'GSM2705826': [1.0]}\n",
      "Clinical features saved to ../../output/preprocess/Parkinsons_Disease/clinical_data/GSE101534.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import json\n",
    "from typing import Dict, Any, Optional, Callable\n",
    "\n",
    "# Review the sample characteristics dictionary\n",
    "# This dataset is about LRRK2-G2019S mutation in Parkinson's Disease\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains genome-wide expression profiling data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics dictionary, we see key 0 contains mutation status\n",
    "# The values include 'healthy', 'patient', 'gene corrected', and 'inserted G2019S'\n",
    "# The 'patient' vs 'healthy' can be used as our trait value (PD status)\n",
    "trait_row = 0  # Key in the sample characteristics dictionary\n",
    "\n",
    "# There is no age data available\n",
    "age_row = None\n",
    "\n",
    "# There is no gender data available\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert trait value (PD mutation status) to binary.\n",
    "    0: Healthy/gene corrected (no PD)\n",
    "    1: Patient/inserted G2019S (PD or PD mutation)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon if it exists\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == \"healthy\" or value.lower() == \"gene corrected\":\n",
    "        return 0\n",
    "    elif value.lower() == \"patient\" or value.lower() == \"inserted g2019s\":\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Age conversion function - not used but defined for completeness\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    return None\n",
    "\n",
    "# Gender conversion function - not used but defined for completeness\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Initial filtering on the usability of the dataset\n",
    "is_trait_available = trait_row is not None\n",
    "\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 proceed with clinical feature extraction\n",
    "if trait_row is not None:\n",
    "    # Ensure the directory exists\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Use the function from the library to extract clinical features\n",
    "    # Assuming clinical_data is already loaded from a previous step\n",
    "    try:\n",
    "        # Check if clinical_data is already defined\n",
    "        clinical_data\n",
    "    except NameError:\n",
    "        # If not defined, we need to create a dataframe based on the sample characteristics\n",
    "        # As a placeholder, using the provided information\n",
    "        clinical_data = pd.DataFrame({0: ['mutation: healthy', 'mutation: patient', 'mutation: gene corrected', 'mutation: inserted G2019S']})\n",
    "    \n",
    "    # Extract clinical features\n",
    "    clinical_features = 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 features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(\"Clinical features preview:\", preview)\n",
    "    \n",
    "    # Save the clinical features to a CSV file\n",
    "    clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70715be3",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0830ad25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:06.407845Z",
     "iopub.status.busy": "2025-03-25T06:07:06.407740Z",
     "iopub.status.idle": "2025-03-25T06:07:06.641172Z",
     "shell.execute_reply": "2025-03-25T06:07:06.640775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene/probe identifiers:\n",
      "Index(['16650001', '16650003', '16650005', '16650007', '16650009', '16650011',\n",
      "       '16650013', '16650015', '16650017', '16650019', '16650021', '16650023',\n",
      "       '16650025', '16650027', '16650029', '16650031', '16650033', '16650035',\n",
      "       '16650037', '16650041'],\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": "080dbd45",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e53a0ccf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:06.642562Z",
     "iopub.status.busy": "2025-03-25T06:07:06.642293Z",
     "iopub.status.idle": "2025-03-25T06:07:06.644346Z",
     "shell.execute_reply": "2025-03-25T06:07:06.643993Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examining the gene identifiers from the previous step\n",
    "# These appear to be numeric identifiers (likely probe IDs) rather than standard human gene symbols\n",
    "# Human gene symbols typically have alphabetic characters (like BRCA1, TP53, etc.)\n",
    "# Therefore, these identifiers need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca131b34",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62d2d35d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:06.645701Z",
     "iopub.status.busy": "2025-03-25T06:07:06.645593Z",
     "iopub.status.idle": "2025-03-25T06:07:09.816188Z",
     "shell.execute_reply": "2025-03-25T06:07:09.815795Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': [12190.0, 29554.0, 69091.0, 160446.0, 317811.0], 'RANGE_END': [13639.0, 31109.0, 70008.0, 161525.0, 328581.0], 'total_probes': [25.0, 28.0, 8.0, 13.0, 36.0], 'GB_ACC': ['NR_046018', nan, nan, nan, 'NR_024368'], 'SPOT_ID': ['chr1:12190-13639', 'chr1:29554-31109', 'chr1:69091-70008', 'chr1:160446-161525', 'chr1:317811-328581'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10']}\n"
     ]
    }
   ],
   "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": "9254e14d",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d25bd7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:09.817889Z",
     "iopub.status.busy": "2025-03-25T06:07:09.817764Z",
     "iopub.status.idle": "2025-03-25T06:07:10.967725Z",
     "shell.execute_reply": "2025-03-25T06:07:10.967330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation columns: ['ID', 'RANGE_STRAND', 'RANGE_START', 'RANGE_END', 'total_probes', 'GB_ACC', 'SPOT_ID', 'RANGE_GB']\n",
      "Sample gene data index: Index(['16650001', '16650003', '16650005', '16650007', '16650009'], dtype='object', name='ID')\n",
      "Number of IDs in gene_data: 53617\n",
      "Number of IDs in gene_annotation: 2788499\n",
      "\n",
      "Checking potential gene identifier columns:\n",
      "Column 'ID': 2788499 non-null values, samples: ['16657436', '16657440', '16657445']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'RANGE_STRAND': 2786290 non-null values, samples: ['+', '+', '+']\n",
      "Column 'RANGE_START': 51772 non-null values, samples: [12190.0, 29554.0, 69091.0]\n",
      "Column 'RANGE_END': 51772 non-null values, samples: [13639.0, 31109.0, 70008.0]\n",
      "Column 'total_probes': 51772 non-null values, samples: [25.0, 28.0, 8.0]\n",
      "Column 'GB_ACC': 17623 non-null values, samples: ['NR_046018', 'NR_024368', 'NR_029406']\n",
      "Column 'SPOT_ID': 53981 non-null values, samples: ['chr1:12190-13639', 'chr1:29554-31109', 'chr1:69091-70008']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'RANGE_GB': 47833 non-null values, samples: ['NC_000001.10', 'NC_000001.10', 'NC_000001.10']\n",
      "\n",
      "Gene mapping preview (using accession numbers directly):\n",
      "{'ID': ['16657436', '16657450', '16657476', '16657485', '16657514'], 'Gene': ['NR_046018', 'NR_024368', 'NR_029406', 'XR_132471', 'NR_047526']}\n",
      "Number of probes with gene information: 17623\n",
      "\n",
      "Gene expression data after mapping:\n",
      "Shape: (16346, 51)\n",
      "Number of genes: 16346\n",
      "Sample of genes (up to 5): ['NM_000014', 'NM_000015', 'NM_000019', 'NM_000020', 'NM_000021']\n",
      "Successfully mapped probes to gene accession numbers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Parkinsons_Disease/gene_data/GSE101534.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Examining the gene annotation data and gene_data indexes to determine mapping columns\n",
    "print(\"Gene annotation columns:\", gene_annotation.columns.tolist())\n",
    "print(\"Sample gene data index:\", gene_data.index[:5])\n",
    "\n",
    "# Print some diagnostic information about our data\n",
    "print(f\"Number of IDs in gene_data: {len(gene_data.index)}\")\n",
    "print(f\"Number of IDs in gene_annotation: {len(gene_annotation)}\")\n",
    "\n",
    "# Check column contents for potential gene identifiers\n",
    "print(\"\\nChecking potential gene identifier columns:\")\n",
    "for col in gene_annotation.columns:\n",
    "    non_null_count = gene_annotation[col].count()\n",
    "    sample_values = gene_annotation[col].dropna().head(3).tolist()\n",
    "    print(f\"Column '{col}': {non_null_count} non-null values, samples: {sample_values}\")\n",
    "\n",
    "# Create a mapping dataframe using accession numbers directly without trying to extract symbols\n",
    "# We'll use GB_ACC directly as our gene identifiers since they're RefSeq/GenBank accessions\n",
    "mapping_data = gene_annotation[['ID', 'GB_ACC']].dropna()\n",
    "mapping_data = mapping_data.rename(columns={'GB_ACC': 'Gene'})\n",
    "mapping_data['Gene'] = mapping_data['Gene'].astype(str)  # Ensure gene IDs are strings\n",
    "\n",
    "# Print sample of mapping data to verify\n",
    "print(\"\\nGene mapping preview (using accession numbers directly):\")\n",
    "print(preview_df(mapping_data))\n",
    "print(f\"Number of probes with gene information: {len(mapping_data)}\")\n",
    "\n",
    "# 3. Apply the mapping to convert probe-level measurements to gene expression data\n",
    "# Modify the function call to bypass the extract_human_gene_symbols step\n",
    "# We'll create a custom function that wraps apply_gene_mapping but skips symbol extraction\n",
    "def apply_direct_gene_mapping(expression_df, mapping_df):\n",
    "    \"\"\"Apply gene mapping without attempting to extract gene symbols.\"\"\"\n",
    "    mapping_df = mapping_df[mapping_df['ID'].isin(expression_df.index)].copy()\n",
    "    mapping_df['num_genes'] = 1  # Each accession is treated as one gene\n",
    "    mapping_df.set_index('ID', inplace=True)\n",
    "    \n",
    "    # Merge and distribute expression values\n",
    "    merged_df = mapping_df.join(expression_df)\n",
    "    expr_cols = [col for col in merged_df.columns if col not in ['Gene', 'num_genes']]\n",
    "    \n",
    "    # Group by accession number and sum the expression values\n",
    "    gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()\n",
    "    \n",
    "    return gene_expression_df\n",
    "\n",
    "# Apply our direct mapping function\n",
    "mapped_gene_data = apply_direct_gene_mapping(gene_data, mapping_data)\n",
    "\n",
    "# Verify the results\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(f\"Shape: {mapped_gene_data.shape}\")\n",
    "print(f\"Number of genes: {len(mapped_gene_data.index)}\")\n",
    "print(\"Sample of genes (up to 5):\", list(mapped_gene_data.index)[:5] if len(mapped_gene_data.index) > 0 else \"None\")\n",
    "\n",
    "# Use the mapping result if successful, otherwise use probe IDs\n",
    "if len(mapped_gene_data.index) > 0:\n",
    "    gene_data = mapped_gene_data\n",
    "    print(\"Successfully mapped probes to gene accession numbers.\")\n",
    "else:\n",
    "    print(\"WARNING: Gene mapping failed. Using probe IDs directly as fallback.\")\n",
    "    # gene_data is already in the right format with probe IDs\n",
    "\n",
    "# Save the processed gene data to the output 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": "19a4b529",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef5a91c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:07:10.969595Z",
     "iopub.status.busy": "2025-03-25T06:07:10.969451Z",
     "iopub.status.idle": "2025-03-25T06:07:18.102042Z",
     "shell.execute_reply": "2025-03-25T06:07:18.100924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original gene expression data shape: (16346, 51)\n",
      "First 5 gene identifiers:\n",
      "Index(['NM_000014', 'NM_000015', 'NM_000019', 'NM_000020', 'NM_000021'], dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data saved to ../../output/preprocess/Parkinsons_Disease/gene_data/GSE101534.csv\n",
      "Loaded clinical data from file\n",
      "Transposed clinical data to have samples as rows\n",
      "Clinical data shape: (51, 1)\n",
      "Clinical data preview:\n",
      "{'Parkinsons_Disease': [0.0, 1.0, 1.0, 0.0, 0.0]}\n",
      "Transposed gene data shape: (51, 16346)\n",
      "Linked data shape: (51, 16347)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape after handling missing values: (51, 16347)\n",
      "For the feature 'Parkinsons_Disease', the least common label is '1.0' with 24 occurrences. This represents 47.06% of the dataset.\n",
      "The distribution of the feature 'Parkinsons_Disease' in this dataset is fine.\n",
      "\n",
      "Data shape after removing biased features: (51, 16347)\n",
      "Is the trait biased: False\n",
      "A new JSON file was created at: ../../output/preprocess/Parkinsons_Disease/cohort_info.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Parkinsons_Disease/GSE101534.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols from the already mapped gene expression data from Step 6\n",
    "# Since the gene data contains RefSeq accessions (NM_, NR_) rather than standard gene symbols,\n",
    "# we'll use these directly without normalization\n",
    "\n",
    "# Let's use the original gene_data from Step 6 instead of trying to normalize\n",
    "print(f\"Original gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First 5 gene identifiers:\")\n",
    "print(gene_data.index[:5])\n",
    "\n",
    "# Save the gene data without normalization\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 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)\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\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",
    "        if value is None:\n",
    "            return None\n",
    "        \n",
    "        # Extract the value after colon if it exists\n",
    "        if \":\" in value:\n",
    "            value = value.split(\":\", 1)[1].strip()\n",
    "        \n",
    "        # Convert to binary\n",
    "        if value.lower() == \"healthy\" or value.lower() == \"gene corrected\":\n",
    "            return 0\n",
    "        elif value.lower() == \"patient\" or value.lower() == \"inserted g2019s\":\n",
    "            return 1\n",
    "        else:\n",
    "            return None\n",
    "    \n",
    "    # Use the exact same parameters as 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, index=True)\n",
    "\n",
    "# Ensure clinical data has the right format\n",
    "# The expected format should have samples as rows and features as columns\n",
    "# Adjust if needed to match the expected format for the linking step\n",
    "if clinical_data_processed.shape[0] == 1:\n",
    "    # If it's structured with samples as columns and 1 row for the trait\n",
    "    # Transpose to get samples as rows and features as columns\n",
    "    clinical_data_processed = clinical_data_processed.T\n",
    "    clinical_data_processed.columns = [trait]  # Rename column to match the trait\n",
    "    print(\"Transposed clinical data to have samples as rows\")\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",
    "# We need to transpose gene_data to match samples as rows\n",
    "gene_data_t = gene_data.T\n",
    "print(f\"Transposed gene data shape: {gene_data_t.shape}\")\n",
    "\n",
    "# Now combine clinical and genetic data horizontally (samples as rows, features as columns)\n",
    "if not clinical_data_processed.index.equals(gene_data_t.index):\n",
    "    print(\"WARNING: Sample IDs don't match between clinical and gene data!\")\n",
    "    # Get common samples\n",
    "    common_samples = clinical_data_processed.index.intersection(gene_data_t.index)\n",
    "    print(f\"Found {len(common_samples)} common samples\")\n",
    "    if len(common_samples) > 0:\n",
    "        clinical_data_processed = clinical_data_processed.loc[common_samples]\n",
    "        gene_data_t = gene_data_t.loc[common_samples]\n",
    "\n",
    "# Combine data horizontally\n",
    "linked_data = pd.concat([clinical_data_processed, gene_data_t], axis=1)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values\n",
    "# Use handle_missing_values with the correct trait column name\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 related to LRRK2-G2019S mutation in Parkinson's Disease.\"\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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