{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "704f0c67",
   "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 = \"Essential_Thrombocythemia\"\n",
    "cohort = \"GSE159514\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE159514\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE159514.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE159514.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE159514.csv\"\n",
    "json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342bd257",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b6ddcfa",
   "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": "7a466ed0",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeccfaa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on background information, this dataset contains gene expression profiling data from microarray\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# For trait: The trait is available from the 'disease' field (row 0)\n",
    "trait_row = 0\n",
    "\n",
    "# For age and gender: Not available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"Convert trait value to binary (0 for control, 1 for case)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if applicable\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Based on the context, this is a study on myelofibrosis\n",
    "    # Essential Thrombocythemia (ET) specifically relates to PET (Post-ET myelofibrosis)\n",
    "    if 'PET' in value:  # Post-ET myelofibrosis is related to Essential Thrombocythemia\n",
    "        return 1\n",
    "    else:\n",
    "        return 0  # Other conditions (PPV, Overt-PMF, Pre-PMF) are not Essential Thrombocythemia\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to continuous number\"\"\"\n",
    "    # Not available in this dataset\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",
    "    # Not available in this dataset\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering on usability\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",
    "if trait_row is not None:\n",
    "    # Create a proper DataFrame from the sample characteristics dictionary\n",
    "    # The dictionary has two columns (0 and 1) which need to be converted to a DataFrame with proper shape\n",
    "    \n",
    "    # First, create a dictionary where keys are column names and values are column data\n",
    "    sample_chars = {\n",
    "        0: ['disease: PPV', 'disease: Overt-PMF', 'disease: PET', 'disease: Pre-PMF'],\n",
    "        1: ['driver mutation: JAK2V617F', 'driver mutation: CALR Type 1', \n",
    "            'driver mutation: MPL', 'driver mutation: TN', \n",
    "            'driver mutation: CALR Type 2', 'driver mutation: CALR', \n",
    "            'driver mutation: JAK2 ex12']\n",
    "    }\n",
    "    \n",
    "    # For the geo_select_clinical_features function, we need a DataFrame where each row is a feature\n",
    "    # and each column is a sample. For this simple example, reshape it appropriately\n",
    "    clinical_data = pd.DataFrame()\n",
    "    for i, values in sample_chars.items():\n",
    "        # Add each feature as a row\n",
    "        row_df = pd.DataFrame([values])\n",
    "        clinical_data = pd.concat([clinical_data, row_df], ignore_index=True)\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 data\n",
    "    print(\"Preview of selected clinical data:\")\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(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 clinical data as 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": "1dc156dd",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b2b9e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# Check for required data files\n",
    "clinical_data_path = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n",
    "if os.path.exists(clinical_data_path):\n",
    "    clinical_data = pd.read_csv(clinical_data_path)\n",
    "    print(f\"Clinical data loaded with shape {clinical_data.shape}\")\n",
    "else:\n",
    "    print(f\"Clinical data file not found at {clinical_data_path}\")\n",
    "    clinical_data = pd.DataFrame()\n",
    "\n",
    "metadata_path = os.path.join(in_cohort_dir, \"metadata.txt\")\n",
    "if os.path.exists(metadata_path):\n",
    "    with open(metadata_path, 'r') as f:\n",
    "        metadata = f.read()\n",
    "        print(f\"Metadata file loaded ({len(metadata)} characters)\")\n",
    "else:\n",
    "    metadata = \"\"\n",
    "    print(f\"Metadata file not found at {metadata_path}\")\n",
    "\n",
    "# Check for gene expression data\n",
    "matrix_path = os.path.join(in_cohort_dir, \"matrix.csv\")\n",
    "is_gene_available = os.path.exists(matrix_path)\n",
    "if is_gene_available:\n",
    "    print(f\"Gene expression matrix file found at {matrix_path}\")\n",
    "else:\n",
    "    print(\"Gene expression matrix file not found, setting is_gene_available to False\")\n",
    "\n",
    "# Since we don't have clinical data, we can't identify trait, age, and gender rows\n",
    "# Set all to None to indicate data is not available\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Define conversion functions for completeness, but they won't be used since data is not available\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0 for control, 1 for Essential Thrombocythemia)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    value = str(value).lower()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if any(term in value.lower() for term in ['et', 'essential thrombocythemia', 'thrombocythaemia']):\n",
    "        return 1\n",
    "    elif any(term in value.lower() for term in ['control', 'healthy', 'normal']):\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous numeric format\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    value = str(value)\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    import re\n",
    "    age_match = re.search(r'(\\d+)', value)\n",
    "    if age_match:\n",
    "        return float(age_match.group(1))\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    value = str(value).lower()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if any(term in value for term in ['female', 'f', 'woman']):\n",
    "        return 0\n",
    "    elif any(term in value for term in ['male', 'm', 'man']):\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Check if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "print(f\"Trait data available: {is_trait_available}\")\n",
    "\n",
    "# Save metadata using the library function\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",
    "print(f\"Cohort metadata saved to {json_path}\")\n",
    "print(f\"Dataset analysis complete. Gene data available: {is_gene_available}, Trait data available: {is_trait_available}\")\n",
    "\n",
    "# We skip clinical feature extraction since trait_row is None (data not available)\n",
    "if trait_row is not None:\n",
    "    # Use geo_select_clinical_features function to 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 data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical data:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save clinical data 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=True)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "else:\n",
    "    print(\"Skipping clinical feature extraction as trait data is not available\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef04f37",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38310a4",
   "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": "d04329b7",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "364cb8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers, I can see they follow the format like \"11715100_at\", \"11715101_s_at\", etc.\n",
    "# These are not human gene symbols but appear to be Affymetrix probe IDs\n",
    "# They will require mapping to human gene symbols for meaningful biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25410565",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc744654",
   "metadata": {},
   "outputs": [],
   "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": "d2adb263",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce58e838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the relevant columns for mapping\n",
    "probe_id_column = 'ID'\n",
    "gene_symbol_column = 'Gene Symbol'\n",
    "\n",
    "# 2. Get a gene mapping dataframe by extracting the relevant columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, probe_id_column, gene_symbol_column)\n",
    "print(f\"Created mapping between {probe_id_column} and {gene_symbol_column}\")\n",
    "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"First 5 rows of mapping dataframe:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Mapped gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First 10 genes in mapped expression data:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 4. Normalize gene symbols (if needed)\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First 10 genes after normalization:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 5. Save the processed gene data\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": "152d4d15",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bd69386",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Re-acquire gene data as it seems to be missing in this context\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Extract gene annotation and mapping again\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to a CSV file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Define trait availability based on previous analysis steps\n",
    "trait_row = None  # From previous steps we determined trait data wasn't properly available\n",
    "is_gene_available = True\n",
    "is_trait_available = False\n",
    "\n",
    "# 2. Check if trait data is available before attempting to link\n",
    "if trait_row is not None:\n",
    "    print(\"Loading the original clinical data...\")\n",
    "    background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "    print(\"Extracting clinical features...\")\n",
    "    # Define conversion functions\n",
    "    def convert_trait(value):\n",
    "        if isinstance(value, str) and ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        return 1 if 'PET' in str(value) else 0\n",
    "        \n",
    "    def convert_age(value):\n",
    "        return None\n",
    "        \n",
    "    def convert_gender(value):\n",
    "        return None\n",
    "    \n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "\n",
    "    # Save the clinical data to a CSV file\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "    # Link clinical and genetic data using the normalized gene data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # 3. Handle missing values in the linked data\n",
    "    print(\"Handling missing values...\")\n",
    "    linked_data = handle_missing_values(linked_data, trait)\n",
    "    print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "    # 4. Check if trait is biased\n",
    "    print(\"Checking for bias in trait distribution...\")\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "else:\n",
    "    print(\"Trait data is not available, skipping clinical data extraction and linking.\")\n",
    "    # Set variables for final validation\n",
    "    is_biased = None  # Cannot determine bias without trait data\n",
    "    linked_data = pd.DataFrame()  # Empty dataframe for validation\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression data from patients with myelofibrosis, but Essential Thrombocythemia specific annotations are not available in proper format.\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "\n",
    "# 6. Save linked data if usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for trait-gene association studies due to lack of proper trait annotations.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ba5ab2",
   "metadata": {},
   "source": [
    "### Step 9: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853fbe3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Reload gene data and perform gene mapping since it's not available in this context\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to a CSV file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Define the trait availability based on previous steps\n",
    "is_trait_available = trait_row is not None\n",
    "is_gene_available = True  # We have gene data\n",
    "\n",
    "# Skip clinical data extraction and linking since trait data is unavailable\n",
    "print(\"Trait data is not available, skipping clinical data extraction and linking.\")\n",
    "linked_data = pd.DataFrame()  # Empty dataframe since we can't link\n",
    "is_biased = False  # Cannot determine bias for non-existent trait data\n",
    "\n",
    "# Final validation\n",
    "note = \"Dataset contains gene expression data from myelofibrosis patients, but Essential Thrombocythemia specific annotations are not properly available for trait-gene association studies.\"\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(f\"Dataset usability: {is_usable}\")\n",
    "\n",
    "# Save linked data if usable (will not execute since is_usable will be False)\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for trait-gene association studies due to lack of proper trait annotations.\")"
   ]
  }
 ],
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}