{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a554fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:43.848744Z",
     "iopub.status.busy": "2025-03-25T08:01:43.848628Z",
     "iopub.status.idle": "2025-03-25T08:01:44.009886Z",
     "shell.execute_reply": "2025-03-25T08:01:44.009535Z"
    }
   },
   "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 = \"Celiac_Disease\"\n",
    "cohort = \"GSE72625\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Celiac_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Celiac_Disease/GSE72625\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Celiac_Disease/GSE72625.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Celiac_Disease/gene_data/GSE72625.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Celiac_Disease/clinical_data/GSE72625.csv\"\n",
    "json_path = \"../../output/preprocess/Celiac_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3ef66d",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5dc29f26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:44.011240Z",
     "iopub.status.busy": "2025-03-25T08:01:44.011099Z",
     "iopub.status.idle": "2025-03-25T08:01:44.180687Z",
     "shell.execute_reply": "2025-03-25T08:01:44.180332Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gastrointestinal symptoms and pathology in patients with Common variable immunodeficiency\"\n",
      "!Series_summary\t\"Based on the findings of increased IEL in duodenal biopsies in CVID, an overlap with celiac disease has been suggested. In the present study, increased IEL, in particular in the pars descendens of the duodenum, was one of the most frequent histopathological finding. We therefore examined the gene expression profile in pars descendens of duodenum in CVID patients with increased IEL (n=12, IEL mean 34 [range 22-56] IEL/100 EC), CVID with normal levels of IEL (n=8), celiac disease (n=10, Marsh grade 3a or above) and healthy controls (n=17) by gene expression microarray\"\n",
      "!Series_overall_design\t\"GI histopathological findings in 53 CVID patients that underwent upper and lower endoscopic examination were addressed. For the microarray analysis 20 CVID (8 CVID_normal and 12 CVID with incresed IEL), 10 patients with celiac diseases and 17 healthy controls were included.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['disease state: CVID with increased intraepithelial lymphocytes', 'disease state: CVID without increased intraepithelial lymphocytes', 'disease state: celiac disease', 'disease state: healthy controls'], 1: ['tissue: duodenal biopsy']}\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": "935b4909",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e348ca09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:44.182108Z",
     "iopub.status.busy": "2025-03-25T08:01:44.181992Z",
     "iopub.status.idle": "2025-03-25T08:01:44.189196Z",
     "shell.execute_reply": "2025-03-25T08:01:44.188905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical Features Preview: {0: [0.0], 1: [nan]}\n",
      "Clinical data saved to ../../output/preprocess/Celiac_Disease/clinical_data/GSE72625.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "import json\n",
    "\n",
    "# Check if the cohort has gene expression data based on background information\n",
    "# This seems to contain gene expression microarray data from duodenal biopsies\n",
    "is_gene_available = True\n",
    "\n",
    "# Define the trait, age, and gender rows from the sample characteristics dictionary\n",
    "# Trait (disease state) is available at key 0\n",
    "trait_row = 0\n",
    "# Age is not available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "# Gender is not available in the sample characteristics dictionary\n",
    "gender_row = None\n",
    "\n",
    "# Define conversion functions for the clinical variables\n",
    "def convert_trait(value_str):\n",
    "    \"\"\"Convert trait (disease state) string to binary value (Celiac disease = 1, others = 0)\"\"\"\n",
    "    if value_str is None or pd.isna(value_str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if ':' in value_str:\n",
    "        value = value_str.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value_str.strip().lower()\n",
    "    \n",
    "    # Convert to binary based on Celiac Disease presence\n",
    "    if 'celiac disease' in value:\n",
    "        return 1\n",
    "    elif 'cvid' in value or 'healthy control' in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value_str):\n",
    "    \"\"\"Convert age string to continuous value (not used as age is not available)\"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value_str):\n",
    "    \"\"\"Convert gender string to binary value (not used as gender is not available)\"\"\"\n",
    "    return None\n",
    "\n",
    "# Check trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save metadata for initial filtering\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",
    "# If trait data is available, extract clinical features\n",
    "if is_trait_available:\n",
    "    # Load the clinical data\n",
    "    clinical_data = pd.DataFrame(\n",
    "        {0: ['disease state: CVID with increased intraepithelial lymphocytes', \n",
    "             'disease state: CVID without increased intraepithelial lymphocytes', \n",
    "             'disease state: celiac disease', \n",
    "             'disease state: healthy controls'],\n",
    "         1: ['tissue: duodenal biopsy'] * 4}\n",
    "    )\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",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Clinical Features Preview:\", 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 the clinical data 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3757cfed",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cb8f8b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:44.190568Z",
     "iopub.status.busy": "2025-03-25T08:01:44.190461Z",
     "iopub.status.idle": "2025-03-25T08:01:44.451405Z",
     "shell.execute_reply": "2025-03-25T08:01:44.451014Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Celiac_Disease/GSE72625/GSE72625_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (47323, 47)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
      "       'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
      "       'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
      "       'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
      "       'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\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": "c1063e1e",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1321fdcb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:44.453182Z",
     "iopub.status.busy": "2025-03-25T08:01:44.453058Z",
     "iopub.status.idle": "2025-03-25T08:01:44.454965Z",
     "shell.execute_reply": "2025-03-25T08:01:44.454681Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the identifiers, these are Illumina microarray probe IDs (starting with ILMN_)\n",
    "# These are not human gene symbols and will need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862241cc",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48da686a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:44.456660Z",
     "iopub.status.busy": "2025-03-25T08:01:44.456538Z",
     "iopub.status.idle": "2025-03-25T08:01:49.709282Z",
     "shell.execute_reply": "2025-03-25T08:01:49.708887Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan]}\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": "a926a255",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8802a84b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:49.711132Z",
     "iopub.status.busy": "2025-03-25T08:01:49.710964Z",
     "iopub.status.idle": "2025-03-25T08:01:50.608938Z",
     "shell.execute_reply": "2025-03-25T08:01:50.608553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping shape: (44837, 2)\n",
      "First 5 rows of gene mapping:\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Gene': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape after mapping: (21464, 47)\n",
      "First 10 gene symbols after mapping:\n",
      "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n",
      "       'A4GALT', 'A4GNT'],\n",
      "      dtype='object', name='Gene')\n",
      "Gene expression data shape after normalization: (20259, 47)\n",
      "First 10 normalized gene symbols:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT',\n",
      "       'A4GNT', 'AAA1', 'AAAS'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Celiac_Disease/gene_data/GSE72625.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify columns for gene mapping\n",
    "# From the previous outputs, we can see that:\n",
    "# - Gene expression data has index 'ID' with identifiers like ILMN_1343291\n",
    "# - Gene annotation data has 'ID' column with same ILMN_ format identifiers \n",
    "# - Gene annotation data has 'Symbol' column which contains gene symbols\n",
    "\n",
    "# 2. Get gene mapping dataframe using the get_gene_mapping function\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
    "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
    "print(\"First 5 rows of gene mapping:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene 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 10 gene symbols after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Normalize gene symbols to ensure consistency across the dataset\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n",
    "print(\"First 10 normalized gene symbols:\")\n",
    "print(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": "2717b10b",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f21a2fda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:01:50.610797Z",
     "iopub.status.busy": "2025-03-25T08:01:50.610654Z",
     "iopub.status.idle": "2025-03-25T08:02:00.764795Z",
     "shell.execute_reply": "2025-03-25T08:02:00.764369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data shape: (1, 47)\n",
      "Clinical data preview:\n",
      "{'GSM1866896': [0.0], 'GSM1866897': [0.0], 'GSM1866898': [0.0], 'GSM1866899': [0.0], 'GSM1866900': [0.0], 'GSM1866901': [0.0], 'GSM1866902': [0.0], 'GSM1866903': [0.0], 'GSM1866904': [0.0], 'GSM1866905': [0.0], 'GSM1866906': [0.0], 'GSM1866907': [0.0], 'GSM1866908': [0.0], 'GSM1866909': [0.0], 'GSM1866910': [0.0], 'GSM1866911': [0.0], 'GSM1866912': [0.0], 'GSM1866913': [0.0], 'GSM1866914': [0.0], 'GSM1866915': [0.0], 'GSM1866916': [1.0], 'GSM1866917': [1.0], 'GSM1866918': [1.0], 'GSM1866919': [1.0], 'GSM1866920': [1.0], 'GSM1866921': [1.0], 'GSM1866922': [1.0], 'GSM1866923': [1.0], 'GSM1866924': [1.0], 'GSM1866925': [1.0], 'GSM1866926': [0.0], 'GSM1866927': [0.0], 'GSM1866928': [0.0], 'GSM1866929': [0.0], 'GSM1866930': [0.0], 'GSM1866931': [0.0], 'GSM1866932': [0.0], 'GSM1866933': [0.0], 'GSM1866934': [0.0], 'GSM1866935': [0.0], 'GSM1866936': [0.0], 'GSM1866937': [0.0], 'GSM1866938': [0.0], 'GSM1866939': [0.0], 'GSM1866940': [0.0], 'GSM1866941': [0.0], 'GSM1866942': [0.0]}\n",
      "Linked data shape before handling missing values: (47, 20260)\n",
      "Linked data first few columns:\n",
      "Index(['Celiac_Disease', 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2',\n",
      "       'A4GALT', 'A4GNT', 'AAA1'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (47, 20260)\n",
      "For the feature 'Celiac_Disease', the least common label is '1.0' with 10 occurrences. This represents 21.28% of the dataset.\n",
      "The distribution of the feature 'Celiac_Disease' in this dataset is fine.\n",
      "\n",
      "Data is usable. Saving to ../../output/preprocess/Celiac_Disease/GSE72625.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Re-load clinical data from matrix file to ensure we have the correct data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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",
    "# Re-extract clinical features with the properly loaded clinical data\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(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function\n",
    "try:\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
    "    print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
    "    print(\"Linked data first few columns:\")\n",
    "    print(linked_data.columns[:10])\n",
    "    \n",
    "    # 3. Handle missing values in the linked data\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. Determine whether the trait and demographic features are severely biased\n",
    "    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "    \n",
    "    # 5. Conduct quality check and save the 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_trait_biased, \n",
    "        df=unbiased_linked_data,\n",
    "        note=\"Dataset contains gene expression from duodenal biopsies of Celiac Disease patients, CVID patients, and healthy controls\"\n",
    "    )\n",
    "    \n",
    "    # 6. If the linked data is usable, save it as a CSV file\n",
    "    if is_usable:\n",
    "        print(f\"Data is usable. Saving to {out_data_file}\")\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        unbiased_linked_data.to_csv(out_data_file)\n",
    "    else:\n",
    "        print(\"Data is not usable. Not saving linked data file.\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error in data linking or processing: {e}\")\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=True, \n",
    "        df=pd.DataFrame(),\n",
    "        note=f\"Error processing data: {e}\"\n",
    "    )\n",
    "    print(\"Data is not usable due to processing error.\")"
   ]
  }
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