{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "809cf3ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:13.627321Z",
     "iopub.status.busy": "2025-03-25T03:48:13.626781Z",
     "iopub.status.idle": "2025-03-25T03:48:13.791610Z",
     "shell.execute_reply": "2025-03-25T03:48:13.791266Z"
    }
   },
   "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 = \"Red_Hair\"\n",
    "cohort = \"GSE207744\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Red_Hair\"\n",
    "in_cohort_dir = \"../../input/GEO/Red_Hair/GSE207744\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Red_Hair/GSE207744.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Red_Hair/gene_data/GSE207744.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Red_Hair/clinical_data/GSE207744.csv\"\n",
    "json_path = \"../../output/preprocess/Red_Hair/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18c2367",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21821550",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:13.793039Z",
     "iopub.status.busy": "2025-03-25T03:48:13.792892Z",
     "iopub.status.idle": "2025-03-25T03:48:14.044591Z",
     "shell.execute_reply": "2025-03-25T03:48:14.044251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptomic study on human skin samples: identification of actinic keratoses two risk classes.\"\n",
      "!Series_summary\t\"Gene expression profile analysis allowed to identify 2 classes of AK.\"\n",
      "!Series_overall_design\t\"A total of 72 tissue samples (24 NL, 23 L, 4 PL and 21 AK) were isolated from 24 patients. For each patient, samples were acquired on the lesion (L or AK), on the perilesional (PL) i.e. safety surgical margin area (often containing AK) and/or on the non-lesional (NL) parts of the elliptical surgical excision.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['patient number: 001', 'patient number: 006', 'patient number: 016', 'patient number: 017', 'patient number: 018=026=045', 'patient number: 028', 'patient number: 029', 'patient number: 035=041', 'patient number: 048', 'patient number: 056', 'patient number: 057', 'patient number: 074', 'patient number: 075', 'patient number: 077', 'patient number: 082', 'patient number: 090', 'patient number: 091', 'patient number: 109', 'patient number: 110', 'patient number: 115', 'patient number: 119', 'patient number: 122', 'patient number: 123', 'patient number: 125'], 1: ['sample localisation: Temple', 'sample localisation: Vertex', 'sample localisation: Forehead', 'sample localisation: Ear', 'sample localisation: Cheek', 'sample localisation: Neck anterior surface', 'sample localisation: Hand dorsum', 'sample localisation: Leg anterior surface', 'sample localisation: Shoulder'], 2: ['lesion type: Actinic Keratosis', 'lesion type: Lesion', 'lesion type: Non Lesion', 'lesion type: Peri Lesion'], 3: [nan, 'lesion number (if applicable): 1', 'lesion number (if applicable): 2', 'lesion number (if applicable): 3']}\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": "244319f9",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c930cf20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:14.045875Z",
     "iopub.status.busy": "2025-03-25T03:48:14.045765Z",
     "iopub.status.idle": "2025-03-25T03:48:14.050292Z",
     "shell.execute_reply": "2025-03-25T03:48:14.050007Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A new JSON file was created at: ../../output/preprocess/Red_Hair/cohort_info.json\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 1. Determine if gene expression data is available\n",
    "# Based on the background information, this is a transcriptomic study on human skin samples\n",
    "# Therefore gene expression data should be available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability for trait, age, and gender\n",
    "# Looking at the sample characteristics dictionary:\n",
    "# The dataset is about actinic keratosis skin lesions, not red hair.\n",
    "# No information about red hair is available in this dataset.\n",
    "# No age information is available in the sample characteristics\n",
    "# No gender information is available in the sample characteristics\n",
    "\n",
    "trait_row = None  # No red hair information in this dataset\n",
    "age_row = None  # No age information available\n",
    "gender_row = None  # No gender information available\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait information to binary values.\"\"\"\n",
    "    # Since there's no red hair data, this function is defined for completeness\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age information to continuous values.\"\"\"\n",
    "    # No age information in this dataset, function defined for completeness\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender information to binary values.\"\"\"\n",
    "    # No gender information in this dataset, function defined for completeness\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None  # Will be False\n",
    "\n",
    "# Save the cohort information\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 (if trait_row is not None)\n",
    "# Since trait_row is None, we'll skip this step\n",
    "if trait_row is not None:\n",
    "    # Load the actual clinical data that should be available from previous steps\n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,  # This would be the actual data from previous steps\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 selected clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\", preview)\n",
    "    \n",
    "    # Save the selected clinical features to CSV\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f8d75c",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc1d4ce2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:14.051466Z",
     "iopub.status.busy": "2025-03-25T03:48:14.051218Z",
     "iopub.status.idle": "2025-03-25T03:48:14.512884Z",
     "shell.execute_reply": "2025-03-25T03:48:14.512407Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107',\n",
      "       '(+)E1A_r60_a135', '(+)E1A_r60_a20', '(+)E1A_r60_a22', '(+)E1A_r60_a97',\n",
      "       '(+)E1A_r60_n11', '(+)E1A_r60_n9', '3xSLv1', 'A_19_P00315452',\n",
      "       'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315502', 'A_19_P00315506',\n",
      "       'A_19_P00315518', 'A_19_P00315519', 'A_19_P00315529', 'A_19_P00315541'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths\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\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(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aec9301",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c85e0aac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:14.514526Z",
     "iopub.status.busy": "2025-03-25T03:48:14.514414Z",
     "iopub.status.idle": "2025-03-25T03:48:14.516264Z",
     "shell.execute_reply": "2025-03-25T03:48:14.515987Z"
    }
   },
   "outputs": [],
   "source": [
    "# Based on my biomedical knowledge, these identifiers don't appear to be standard human gene symbols\n",
    "# The identifiers that start with \"A_19_P\" look like Agilent microarray probe IDs\n",
    "# Others like \"(+)E1A_r60_1\" and \"3xSLv1\" are not standard gene symbols either\n",
    "# These will need to be mapped to standard gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76b08c0",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a316740b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:14.517195Z",
     "iopub.status.busy": "2025-03-25T03:48:14.517097Z",
     "iopub.status.idle": "2025-03-25T03:48:22.125271Z",
     "shell.execute_reply": "2025-03-25T03:48:22.124944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_001105533', nan], 'GB_ACC': [nan, nan, nan, 'NM_001105533', nan], 'LOCUSLINK_ID': [nan, nan, nan, 79974.0, 54880.0], 'GENE_SYMBOL': [nan, nan, nan, 'CPED1', 'BCOR'], 'GENE_NAME': [nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1', 'BCL6 corepressor'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.189652', nan], 'ENSEMBL_ID': [nan, nan, nan, nan, 'ENST00000378463'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673', 'ens|ENST00000378463'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'unmapped', 'chr7:120901888-120901947', 'chrX:39909128-39909069'], 'CYTOBAND': [nan, nan, nan, 'hs|7q31.31', 'hs|Xp11.4'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]', 'BCL6 corepressor [Source:HGNC Symbol;Acc:HGNC:20893] [ENST00000378463]'], 'GO_ID': [nan, nan, nan, 'GO:0005783(endoplasmic reticulum)', 'GO:0000122(negative regulation of transcription from RNA polymerase II promoter)|GO:0000415(negative regulation of histone H3-K36 methylation)|GO:0003714(transcription corepressor activity)|GO:0004842(ubiquitin-protein ligase activity)|GO:0005515(protein binding)|GO:0005634(nucleus)|GO:0006351(transcription, DNA-dependent)|GO:0007507(heart development)|GO:0008134(transcription factor binding)|GO:0030502(negative regulation of bone mineralization)|GO:0031072(heat shock protein binding)|GO:0031519(PcG protein complex)|GO:0035518(histone H2A monoubiquitination)|GO:0042476(odontogenesis)|GO:0042826(histone deacetylase binding)|GO:0044212(transcription regulatory region DNA binding)|GO:0045892(negative regulation of transcription, DNA-dependent)|GO:0051572(negative regulation of histone H3-K4 methylation)|GO:0060021(palate development)|GO:0065001(specification of axis polarity)|GO:0070171(negative regulation of tooth mineralization)'], 'SEQUENCE': [nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA', 'CATCAAAGCTACGAGAGATCCTACACACCCAGATTTAAAAAATAATAAAAACTTAAGGGC'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7671a9a7",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bad0d998",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:22.126668Z",
     "iopub.status.busy": "2025-03-25T03:48:22.126544Z",
     "iopub.status.idle": "2025-03-25T03:48:22.500078Z",
     "shell.execute_reply": "2025-03-25T03:48:22.499699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping dataframe preview:\n",
      "{'ID': ['A_33_P3396872', 'A_33_P3267760', 'A_32_P194264', 'A_23_P153745', 'A_21_P0014180'], 'Gene': ['CPED1', 'BCOR', 'CHAC2', 'IFI30', 'GPR146']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 20 gene symbols after mapping:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A1CF-2', 'A1CF-3', 'A2M', 'A2M-1',\n",
      "       'A2M-AS1', 'A2ML1', 'A2MP1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AA06',\n",
      "       'AAAS', 'AAAS-1', 'AACS', 'AACS-2', 'AACS-3', 'AACSP1'],\n",
      "      dtype='object', name='Gene')\n",
      "Gene data shape after mapping: (29222, 72)\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns in gene_annotation contain gene identifiers and gene symbols\n",
    "# Based on the preview, the column 'ID' appears to match the gene identifiers in gene_expression data\n",
    "# The column 'GENE_SYMBOL' contains the gene symbols\n",
    "\n",
    "# 2. Extract the gene mapping using the get_gene_mapping function from the library\n",
    "gene_mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
    "\n",
    "# Examine the mapping dataframe\n",
    "print(\"Gene mapping dataframe preview:\")\n",
    "print(preview_df(gene_mapping_df))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping_df)\n",
    "\n",
    "# Print the first 20 gene symbols after mapping to verify the process\n",
    "print(\"First 20 gene symbols after mapping:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# Print the shape of the gene data after mapping\n",
    "print(f\"Gene data shape after mapping: {gene_data.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec6db3f",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11be82cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T03:48:22.501513Z",
     "iopub.status.busy": "2025-03-25T03:48:22.501401Z",
     "iopub.status.idle": "2025-03-25T03:48:23.475623Z",
     "shell.execute_reply": "2025-03-25T03:48:23.475215Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data shape: (20778, 72)\n",
      "First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A3GALT2', 'A4GALT', 'A4GNT']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Red_Hair/gene_data/GSE207744.csv\n",
      "No Red_Hair trait data available for cohort GSE207744. Cannot link clinical and genetic data.\n",
      "Abnormality detected in the cohort: GSE207744. Preprocessing failed.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the obtained gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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",
    "# Check if trait data is available by reading the JSON metadata\n",
    "import json\n",
    "with open(json_path, \"r\") as file:\n",
    "    metadata = json.load(file)\n",
    "    \n",
    "is_trait_available = False\n",
    "if cohort in metadata:\n",
    "    is_trait_available = metadata[cohort].get(\"is_trait_available\", False)\n",
    "\n",
    "# Only proceed with clinical data processing if trait is available\n",
    "if is_trait_available:\n",
    "    # Load the clinical features from the saved file\n",
    "    clinical_file_path = out_clinical_data_file\n",
    "    if os.path.exists(clinical_file_path):\n",
    "        clinical_features = pd.read_csv(clinical_file_path)\n",
    "        print(f\"Clinical features loaded from {clinical_file_path}\")\n",
    "        print(f\"Clinical features shape: {clinical_features.shape}\")\n",
    "    \n",
    "    # 2. Link the clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features.T, normalized_gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    print(f\"First few columns: {list(linked_data.columns[:5])}\")\n",
    "    \n",
    "    # 3. Handle missing values in the linked data\n",
    "    trait_column = linked_data.columns[0]  # First column should be the trait\n",
    "    print(f\"Using trait column: {trait_column}\")\n",
    "    \n",
    "    linked_data_processed = handle_missing_values(linked_data, trait_column)\n",
    "    print(f\"Shape after handling missing values: {linked_data_processed.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_processed, trait_column)\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 data but was processed and found unsuitable for Red_Hair analysis.\"\n",
    "    )\n",
    "    \n",
    "    # 6. Save the data if it's usable\n",
    "    if is_usable:\n",
    "        # Create directory if it doesn't exist\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        # Save the data\n",
    "        unbiased_linked_data.to_csv(out_data_file)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n",
    "else:\n",
    "    print(f\"No Red_Hair trait data available for cohort {cohort}. Cannot link clinical and genetic data.\")\n",
    "    # Create empty DataFrame with appropriate structure for validation\n",
    "    empty_df = pd.DataFrame(columns=[trait])\n",
    "    \n",
    "    # Mark as unusable in final validation - using False for is_biased instead of None\n",
    "    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=False,\n",
    "        is_biased=False,  # Using False instead of None to satisfy function requirements\n",
    "        df=empty_df,\n",
    "        note=\"No Red_Hair trait information available in this cohort.\"\n",
    "    )"
   ]
  }
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