{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afc6ca4e",
   "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 = \"Allergies\"\n",
    "cohort = \"GSE203409\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Allergies\"\n",
    "in_cohort_dir = \"../../input/GEO/Allergies/GSE203409\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Allergies/GSE203409.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Allergies/gene_data/GSE203409.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Allergies/clinical_data/GSE203409.csv\"\n",
    "json_path = \"../../output/preprocess/Allergies/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b558c7",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa35a43c",
   "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": "11adeaa3",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a565bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Dict, Any, Callable, Optional\n",
    "\n",
    "# 1. Determine gene expression data availability\n",
    "# From the background information, this appears to be a gene expression study\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Determine variable availability and create conversion functions\n",
    "# Looking at the sample characteristics dictionary:\n",
    "# - This is an in vitro cell line study (HaCaT cells)\n",
    "# - There are different knockdowns (shC and shFLG) and treatments\n",
    "# - No human age or gender data is present as this is a cell line study\n",
    "\n",
    "# For trait, we can use the knockdown status (shC vs shFLG)\n",
    "# shFLG represents filaggrin-insufficient cells which is relevant to allergies\n",
    "trait_row = 1  # knockdown data is in row 1\n",
    "\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"Convert knockdown status to binary trait.\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract value after colon and strip whitespace\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: shFLG (filaggrin-insufficient) = 1, shC (control) = 0\n",
    "    if \"shFLG\" in value:\n",
    "        return 1  # Filaggrin-insufficient (associated with allergies)\n",
    "    elif \"shC\" in value:\n",
    "        return 0  # Control\n",
    "    return None\n",
    "\n",
    "# Age and gender are not applicable as this is a cell line study\n",
    "age_row = None\n",
    "gender_row = None\n",
    "convert_age = None\n",
    "convert_gender = None\n",
    "\n",
    "# 3. Save metadata about dataset usability\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Extract clinical features if trait is available\n",
    "if trait_row is not None:\n",
    "    # Create sample characteristics dictionary from the provided output\n",
    "    sample_characteristics_dict = {\n",
    "        0: ['cell line: HaCaT'], \n",
    "        1: ['knockdown: shC', 'knockdown: shFLG'], \n",
    "        2: ['treatment: Untreated', 'treatment: Histamine', 'treatment: Amphiregulin', 'treatment: IFNy', 'treatment: IL-4/IL-13', 'treatment: Cysteine', 'treatment: Derp1/cysteine', 'treatment: Derp2'], \n",
    "        3: ['treatment compound concentration: N/A', 'treatment compound concentration: 1 ug/ml', 'treatment compound concentration: 50 ng/ml', 'treatment compound concentration: 50 ng/ml / 50 ng/ml', 'treatment compound concentration: 10 uM', 'treatment compound concentration: 100 nM / 10 uM', 'treatment compound concentration: 100 nM']\n",
    "    }\n",
    "    \n",
    "    # Create clinical_data from this dictionary - using proper transposition\n",
    "    clinical_data = pd.DataFrame(sample_characteristics_dict).T\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 processed clinical data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of processed clinical data:\")\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 the processed clinical data\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": "85dc8694",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a764ac3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# Assuming clinical_data is already available from previous steps\n",
    "# Let's examine what we have in the clinical_data DataFrame\n",
    "try:\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(clinical_data.head())\n",
    "    print(\"\\nClinical data shape:\", clinical_data.shape)\n",
    "    print(\"\\nClinical data columns:\", clinical_data.columns.tolist())\n",
    "    \n",
    "    # Print unique values for each row to analyze the content\n",
    "    print(\"\\nUnique values in clinical data:\")\n",
    "    for i in range(len(clinical_data)):\n",
    "        unique_vals = clinical_data.iloc[i].unique()\n",
    "        if len(unique_vals) < 10:  # Only print if there aren't too many unique values\n",
    "            print(f\"Row {i}: {unique_vals}\")\n",
    "        else:\n",
    "            print(f\"Row {i}: {len(unique_vals)} unique values\")\n",
    "except NameError:\n",
    "    print(\"Clinical data not available from previous steps\")\n",
    "    clinical_data = pd.DataFrame()  # Create empty DataFrame if not available\n",
    "\n",
    "# 1. Determine if gene expression data is available\n",
    "# Look for indicators in the data structure and content\n",
    "is_gene_available = True\n",
    "# We'll assume gene expression data is available unless we find evidence to the contrary\n",
    "# In a real scenario, we'd analyze clinical_data or other data to determine this\n",
    "\n",
    "# 2. Variable availability and data type conversion\n",
    "# Initialize as None, will be updated if found\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Examine clinical data to find rows containing trait, age, and gender information\n",
    "if not clinical_data.empty:\n",
    "    for i in range(len(clinical_data)):\n",
    "        row_values = ' '.join(clinical_data.iloc[i].astype(str).tolist()).lower()\n",
    "        \n",
    "        # Look for allergy-related information\n",
    "        if any(term in row_values for term in ['allergy', 'allergic', 'atopic', 'asthma', 'rhinitis']):\n",
    "            trait_row = i\n",
    "        \n",
    "        # Look for age information\n",
    "        if any(term in row_values for term in ['age', 'years old']):\n",
    "            age_row = i\n",
    "        \n",
    "        # Look for gender/sex information\n",
    "        if any(term in row_values for term in ['gender', 'sex', 'male', 'female']):\n",
    "            gender_row = i\n",
    "\n",
    "    # Check if the identified rows have varying values (not constant)\n",
    "    if trait_row is not None:\n",
    "        unique_values = clinical_data.iloc[trait_row].astype(str).unique()\n",
    "        if len(unique_values) <= 1:\n",
    "            trait_row = None  # Consider as not available if only one unique value\n",
    "\n",
    "    if age_row is not None:\n",
    "        unique_values = clinical_data.iloc[age_row].astype(str).unique()\n",
    "        if len(unique_values) <= 1:\n",
    "            age_row = None  # Consider as not available if only one unique value\n",
    "\n",
    "    if gender_row is not None:\n",
    "        unique_values = clinical_data.iloc[gender_row].astype(str).unique()\n",
    "        if len(unique_values) <= 1:\n",
    "            gender_row = None  # Consider as not available if only one unique value\n",
    "\n",
    "# Define conversion functions\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert trait (allergy) value to binary format: 1 for present, 0 for absent.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Positive indicators\n",
    "    if any(term in value for term in ['yes', 'positive', 'present', 'allergy', 'allergic', 'diagnosed', 'asthma', 'rhinitis', 'atopic']):\n",
    "        return 1\n",
    "    # Negative indicators\n",
    "    elif any(term in value for term in ['no', 'negative', 'absent', 'control', 'healthy', 'normal']):\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to continuous format.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract age as a number\n",
    "    try:\n",
    "        # Extract digits from the string\n",
    "        import re\n",
    "        numbers = re.findall(r'\\d+\\.?\\d*', value)\n",
    "        if numbers:\n",
    "            return float(numbers[0])\n",
    "        else:\n",
    "            return None\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender to binary format: 0 for female, 1 for male.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if any(term in value for term in ['female', 'f', 'woman', 'girl']):\n",
    "        return 0\n",
    "    elif any(term in value for term in ['male', 'm', 'man', 'boy']):\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save metadata\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None and not clinical_data.empty:\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 features\n",
    "    print(\"\\nSelected clinical features preview:\")\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(preview)\n",
    "    \n",
    "    # Save 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",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "else:\n",
    "    print(\"Clinical data not available or trait information not found. Skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c2e319",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82f52659",
   "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": "350acbdc",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03af5791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the gene identifiers shown, these are Illumina microarray probe IDs (ILMN_xxxxxxx format)\n",
    "# They 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": "a3346495",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc95de8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 3. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fb95e6",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4ba85dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the columns containing probe IDs and gene symbols in the gene annotation dataframe\n",
    "# From the preview, we can see 'ID' contains probe identifiers matching the gene expression data\n",
    "# The column 'Symbol' appears to contain gene symbols\n",
    "probe_col = 'ID'\n",
    "gene_col = 'Symbol'\n",
    "\n",
    "# 2. Get the gene mapping dataframe by extracting the ID and Symbol columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# Verify the mapping by printing a few rows\n",
    "print(\"Gene mapping preview:\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print the first few rows of the resulting gene expression data to verify the result\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(gene_data.head())\n",
    "print(\"\\nShape of gene expression data:\", gene_data.shape)\n",
    "\n",
    "# Save the processed gene expression 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": "db1a2295",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "473a8dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(\"Normalizing gene symbols...\")\n",
    "# First reload the gene data from the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Extract gene mapping from annotation\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "probe_col = 'ID'\n",
    "gene_col = 'Symbol'\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# Apply mapping to convert probe-level data to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Now normalize the 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",
    "# 2. Evaluate if we can proceed with linking clinical and genetic data\n",
    "# From our analysis in previous steps, we know this is a cell line study with knockdown information\n",
    "trait_row = 1  # knockdown status (shC vs shFLG)\n",
    "\n",
    "# Define the trait conversion function since we need it\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"Convert knockdown status to binary trait.\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract value after colon and strip whitespace\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: shFLG (filaggrin-insufficient) = 1, shC (control) = 0\n",
    "    if \"shFLG\" in value:\n",
    "        return 1  # Filaggrin-insufficient (associated with allergies)\n",
    "    elif \"shC\" in value:\n",
    "        return 0  # Control\n",
    "    return None\n",
    "\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "if is_trait_available:\n",
    "    print(\"Extracting clinical features...\")\n",
    "    # Use the clinical_data obtained directly from the matrix file\n",
    "    background_info, clinical_data = get_background_and_clinical_data(matrix_file)\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=None,  # Cell line study has no age\n",
    "        convert_age=None,\n",
    "        gender_row=None,  # Cell line study has no gender\n",
    "        convert_gender=None\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",
    "    \n",
    "else:\n",
    "    print(\"No trait information available - this dataset cannot be used for trait-gene association analysis.\")\n",
    "    is_biased = True  # Set to True since we can't use this dataset without trait information\n",
    "    linked_data = pd.DataFrame()  # Empty dataframe as placeholder\n",
    "\n",
    "# 5. Final validation\n",
    "note = \"Dataset contains gene expression from HaCaT keratinocyte cell line with filaggrin knockdown (shFLG) vs control (shC). This represents an in vitro model relevant to allergies rather than direct human subject data.\"\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 trait information or other issues.\")"
   ]
  }
 ],
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}