{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87a07464",
   "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 = \"Thymoma\"\n",
    "cohort = \"GSE29695\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Thymoma\"\n",
    "in_cohort_dir = \"../../input/GEO/Thymoma/GSE29695\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Thymoma/GSE29695.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Thymoma/gene_data/GSE29695.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Thymoma/clinical_data/GSE29695.csv\"\n",
    "json_path = \"../../output/preprocess/Thymoma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5247850e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73bdd0bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Check what files are actually in the directory\n",
    "import os\n",
    "print(\"Files in the directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# 2. Find appropriate files with more flexible pattern matching\n",
    "soft_file = None\n",
    "matrix_file = None\n",
    "\n",
    "for file in files:\n",
    "    file_path = os.path.join(in_cohort_dir, file)\n",
    "    # Look for files that might contain SOFT or matrix data with various possible extensions\n",
    "    if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
    "        soft_file = file_path\n",
    "    if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
    "        matrix_file = file_path\n",
    "\n",
    "if not soft_file:\n",
    "    print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if gz_files:\n",
    "        soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "if not matrix_file:\n",
    "    print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
    "    elif len(gz_files) == 1 and not soft_file:\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# 3. Read files if found\n",
    "if soft_file and matrix_file:\n",
    "    # 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",
    "    \n",
    "    try:\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "        \n",
    "        # Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "        sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "        \n",
    "        # 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",
    "    except Exception as e:\n",
    "        print(f\"Error processing files: {e}\")\n",
    "        # Try swapping files if first attempt fails\n",
    "        print(\"Trying to swap SOFT and matrix files...\")\n",
    "        temp = soft_file\n",
    "        soft_file = matrix_file\n",
    "        matrix_file = temp\n",
    "        try:\n",
    "            background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "            sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "            print(\"Background Information:\")\n",
    "            print(background_info)\n",
    "            print(\"Sample Characteristics Dictionary:\")\n",
    "            print(sample_characteristics_dict)\n",
    "        except Exception as e:\n",
    "            print(f\"Still error after swapping: {e}\")\n",
    "else:\n",
    "    print(\"Could not find necessary files for processing.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6a30c5",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba957582",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "is_gene_available = True  # Based on the series title and summary, this dataset contains gene expression data\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "trait_row = 1  # \"type\" field indicates thymic tumor type\n",
    "age_row = None  # Age data is not available\n",
    "gender_row = None  # Gender data is not available\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for non-thymoma, 1 for thymoma)\n",
    "    # Based on the values in the sample characteristics\n",
    "    # \"CL\" refers to Cell Line, not a tumor sample\n",
    "    if value == \"CL\":\n",
    "        return 0\n",
    "    else:\n",
    "        return 1  # All other types (A, AB, B1, B2, B3, Mixed) are thymic tumors\n",
    "\n",
    "def convert_age(value):\n",
    "    # Age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\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:\n",
    "    try:\n",
    "        # Create a DataFrame from the sample characteristics dictionary\n",
    "        # The output from previous step provided a sample characteristics dictionary\n",
    "        sample_chars_dict = {0: ['tissue: Fresh Frozen Human Tumors', 'tissue: Cell Line'], \n",
    "                            1: ['type: B1', 'type: Mixed AB', 'type: CL', 'type: B2', 'type: B3', \n",
    "                                'type: AB', 'type: A/B', 'type: B1/B2', 'type: A'], \n",
    "                            2: ['category: GII', 'category: GI', 'category: CL', 'category: GIII'], \n",
    "                            3: ['batch group: BATCH 1', 'batch group: BATCH 2', 'batch group: BATCH 3'], \n",
    "                            4: ['stage i/ii, iii/iv, or na = not applicable/unknown: III_IV', \n",
    "                                'stage i/ii, iii/iv, or na = not applicable/unknown: NA', \n",
    "                                'stage i/ii, iii/iv, or na = not applicable/unknown: I_II'], \n",
    "                            5: ['relapse no, yes, or na = not applicable/unknown: NA', \n",
    "                                'relapse no, yes, or na = not applicable/unknown: NO', \n",
    "                                'relapse no, yes, or na = not applicable/unknown: YES'], \n",
    "                            6: ['metastasis no, yes, or na = not applicable/unknown: NA', \n",
    "                                'metastasis no, yes, or na = not applicable/unknown: YES', \n",
    "                                'metastasis no, yes, or na = not applicable/unknown: NO']}\n",
    "        \n",
    "        # Transform the dictionary into a DataFrame\n",
    "        clinical_data = pd.DataFrame.from_dict(sample_chars_dict, orient='index')\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 dataframe\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of selected clinical features:\")\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)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing clinical data: {e}\")\n",
    "        # If there was an error, we still want to record that the trait is available\n",
    "        # but we couldn't process it\n",
    "        is_trait_available = False\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e835e6b0",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6bcc1a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the path to the soft and matrix files\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Looking more carefully at the background information\n",
    "# This is a SuperSeries which doesn't contain direct gene expression data\n",
    "# Need to investigate the soft file to find the subseries\n",
    "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
    "\n",
    "# Open the SOFT file to try to identify subseries\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    subseries_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'Series_relation' in line and 'SuperSeries of' in line:\n",
    "            subseries_lines.append(line.strip())\n",
    "        if i > 1000:  # Limit search to first 1000 lines\n",
    "            break\n",
    "\n",
    "# Display the subseries found\n",
    "if subseries_lines:\n",
    "    print(\"Found potential subseries references:\")\n",
    "    for line in subseries_lines:\n",
    "        print(line)\n",
    "else:\n",
    "    print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
    "\n",
    "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(\"\\nGene data extraction result:\")\n",
    "    print(\"Number of rows:\", len(gene_data))\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",
    "    print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5361d145",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c51bedac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examining the gene identifiers, I can see these are Illumina probe IDs (starting with \"ILMN_\")\n",
    "# These are not human gene symbols and will need to be mapped to proper gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13305594",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48f077bf",
   "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": "2ff46b59",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fec723a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Observe the gene expression identifiers and annotation data to identify corresponding columns\n",
    "# Based on examination of the gene annotation preview:\n",
    "# - The 'ID' or 'Probe_Id' column in the annotation contains the Illumina probe IDs (ILMN_*)\n",
    "# - The 'Symbol' column contains the human gene symbols\n",
    "\n",
    "# Looking at the gene identifiers in the gene expression data, they match the 'ID' column in the annotation\n",
    "# The ID column is the one we want to use for mapping\n",
    "\n",
    "# 2. Get a gene mapping dataframe by extracting the gene identifier and symbol columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "\n",
    "# Print the first few rows of the mapping to verify\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 information about the gene expression data\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(\"Number of genes:\", len(gene_data))\n",
    "print(\"First 5 gene symbols:\", gene_data.index[:5].tolist())\n",
    "print(\"Number of samples:\", gene_data.shape[1])\n",
    "\n",
    "# Save the 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": "9510e25e",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "051f5f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Shape of gene data after normalization: {gene_data.shape}\")\n",
    "print(f\"Gene data column names (sample IDs): {gene_data.columns.tolist()[:5]}...\")  # Show first 5 sample IDs\n",
    "\n",
    "# 2. Load the clinical data that was saved in step 2\n",
    "clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "print(\"Clinical data loaded from file:\")\n",
    "print(clinical_df)\n",
    "\n",
    "# Examine the matrix file to get the actual sample IDs\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "try:\n",
    "    # Read the first few lines of the matrix file to find sample IDs\n",
    "    with gzip.open(matrix_file, 'rt') as f:\n",
    "        for i, line in enumerate(f):\n",
    "            if '!Sample_geo_accession' in line:\n",
    "                sample_ids = line.strip().split('\\t')[1:]\n",
    "                print(f\"Sample IDs from matrix file: {sample_ids[:5]}...\")\n",
    "                break\n",
    "            if i > 100:  # Limit search to first 100 lines\n",
    "                break\n",
    "    \n",
    "    # Create a new clinical dataframe with the correct sample IDs as columns\n",
    "    new_clinical_df = pd.DataFrame(index=[trait], columns=sample_ids)\n",
    "    \n",
    "    # Fill in the values - assuming all tumor samples are 1 except cell lines (which are 0)\n",
    "    for col in new_clinical_df.columns:\n",
    "        # Check if the sample is a cell line by examining the column name\n",
    "        # Cell lines typically have \"CL\" in their sample identifiers or other metadata\n",
    "        # This is a simplified heuristic - in a real scenario, you'd need more robust identification\n",
    "        if \"GSM\" in col:  # This is a GEO sample ID\n",
    "            new_clinical_df.loc[trait, col] = 1  # Default: 1 for tumor samples\n",
    "            \n",
    "    # Print the new clinical dataframe for verification\n",
    "    print(\"New clinical dataframe with correct sample IDs:\")\n",
    "    print(new_clinical_df)\n",
    "    \n",
    "    # Save the updated clinical data\n",
    "    new_clinical_df.to_csv(out_clinical_data_file)\n",
    "    clinical_df = new_clinical_df\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error accessing sample IDs from matrix file: {e}\")\n",
    "    # If we can't get sample IDs, create a simple dataframe with gene_data columns\n",
    "    clinical_df = pd.DataFrame(index=[trait], columns=gene_data.columns)\n",
    "    clinical_df.loc[trait, :] = 1  # Set all samples as having the trait\n",
    "    clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(\"Created simple clinical dataframe using gene data column names:\")\n",
    "    print(clinical_df)\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = pd.concat([clinical_df, gene_data], axis=0)\n",
    "print(f\"Shape of linked data before cleaning: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
    "\n",
    "# 5. Check if the trait and demographic features are biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
    "\n",
    "# 6. Validate the dataset and save cohort information\n",
    "note = \"Dataset contains gene expression data from thymic tumors. The trait variable represents thymic tumor type (0=Cell line, 1=Thymic tumor).\"\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=note\n",
    ")\n",
    "\n",
    "# 7. Save the linked data if it's usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Saved processed linked data to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset validation failed. Final linked data not saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a9ef4e",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "108ff59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Shape of gene data after normalization: {gene_data.shape}\")\n",
    "print(f\"First few gene symbols: {gene_data.index[:5].tolist()}\")\n",
    "print(f\"Sample IDs in gene data: {gene_data.columns[:5].tolist()}...\")\n",
    "\n",
    "# Save the normalized 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Reload clinical data and properly process it for this Thymoma dataset\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Define conversion functions specifically for the Thymoma dataset\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert to binary: 0 for cell lines, 1 for tumor samples\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Cell lines are encoded as \"CL\"\n",
    "    if value == \"CL\":\n",
    "        return 0\n",
    "    # All other types are tumor samples\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "# Extract the clinical data using the appropriate rows based on our analysis\n",
    "clinical_df = geo_select_clinical_features(\n",
    "    clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=1,  # Row for tumor type (from the sample characteristics dictionary)\n",
    "    convert_trait=convert_trait,\n",
    "    # No age or gender data available in this dataset\n",
    "    age_row=None,\n",
    "    convert_age=None,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_df))\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 3. Handle sample ID format to ensure proper linking\n",
    "# Strip quotes from sample IDs if present\n",
    "gene_data.columns = gene_data.columns.str.strip('\"')\n",
    "clinical_df.columns = clinical_df.columns.str.strip('\"')\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n",
    "print(f\"Shape of linked data: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
    "\n",
    "# 5. Check if the trait and demographic features are biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
    "\n",
    "# 6. Validate the dataset and save cohort information\n",
    "note = \"Dataset contains gene expression data from thymic tumors. The trait variable represents tumor type (0=Cell line, 1=Thymic tumor).\"\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=note\n",
    ")\n",
    "\n",
    "# 7. Save the linked data if it's usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Saved processed linked data to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset validation failed. Final linked data not saved.\")"
   ]
  }
 ],
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}