{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83617e40",
   "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 = \"Chronic_obstructive_pulmonary_disease_(COPD)\"\n",
    "cohort = \"GSE162635\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)\"\n",
    "in_cohort_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv\"\n",
    "json_path = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b1a472",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af79c746",
   "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": "532a609d",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30b03bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data from COPD patients\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (COPD status):\n",
    "# The key 'gold.1' in sample characteristics dictionary indicates GOLD staging for COPD\n",
    "# GOLD staging is a classification of COPD severity (0 is chronic bronchitis without obstruction,\n",
    "# I-IV are COPD stages, and healthy indicates control)\n",
    "trait_row = 2  # corresponds to 'gold.1' values\n",
    "\n",
    "# For age:\n",
    "# Age information is not available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender:\n",
    "# Gender information is not available in the sample characteristics dictionary\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "# Convert trait (COPD) function:\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"\n",
    "    Convert GOLD staging to binary COPD status\n",
    "    0 = No COPD (healthy controls)\n",
    "    1 = COPD (including chronic bronchitis without obstruction and GOLD stages I-IV)\n",
    "    \"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon and strip whitespace\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary COPD status\n",
    "    if value == \"healthy\":\n",
    "        return 0\n",
    "    elif value in [\"O\", \"I\", \"II\", \"III\", \"IV\"]:  # All GOLD stages considered COPD\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# No conversion functions needed for age and gender as they are not available\n",
    "convert_age = None\n",
    "convert_gender = None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Initial filtering on 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. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data\n",
    "    # The data is expected to be in a specific format for geo_select_clinical_features\n",
    "    # We need to construct a DataFrame where rows are features (indexed by numbers)\n",
    "    # and columns are samples\n",
    "    \n",
    "    # Sample characteristics dictionary from previous output\n",
    "    sample_chars_dict = {\n",
    "        0: ['tissue: transbronchial biopsy'], \n",
    "        1: ['visit: 1', 'visit: 2', 'visit: 3'], \n",
    "        2: ['gold.1: O', 'gold.1: II', 'gold.1: III', 'gold.1: I', 'gold.1: healthy', 'gold.1: IV'], \n",
    "        3: ['gold.2: O', 'gold.2: II', 'gold.2: III', 'gold.2: I', 'phl.1: Productive', 'gold.2: healthy', 'gold.2: IV'], \n",
    "        4: ['gold.3: O', 'gold.3: II', 'gold.3: III', 'gold.3: I', 'gold.3: IV', 'phl.1: Severe', 'phl.1: No_Dry', 'patid: pat019', 'gold.3: healthy', 'phl.1: Productive', 'patid: pat055'], \n",
    "        5: ['phl.1: Severe', 'phl.1: Productive', 'phl.1: No_Dry', 'phl.2: Productive', 'phl.2: No_Dry', np.nan, 'phl.1: Healthy', 'phl.2: Severe'], \n",
    "        6: ['phl.2: Severe', 'phl.2: Productive', 'phl.2: No_Dry', 'patid: pat013', 'patid: pat014', np.nan, 'phl.2: Healthy', 'patid: pat031', 'patid: pat039', 'patid: pat042', 'patid: pat052', 'patid: pat053', 'patid: pat060', 'phl.3: No_Dry'], \n",
    "        7: ['phl.3: Severe', 'phl.3: No_Dry', 'phl.3: Productive', 'patid: pat010', np.nan, 'phl.3: Healthy', 'patid: pat071'], \n",
    "        8: ['patid: pat001', 'patid: pat002', 'patid: pat003', 'patid: pat004', 'patid: pat005', 'patid: pat006', 'patid: pat007', 'patid: pat008', 'patid: pat009', np.nan, 'patid: pat011', 'patid: pat012', 'patid: pat015', 'patid: pat016', 'patid: pat017', 'patid: pat018', 'patid: pat020', 'patid: pat021', 'patid: pat022', 'patid: pat023', 'patid: pat024', 'patid: pat025', 'patid: pat026', 'patid: pat027', 'patid: pat028', 'patid: pat029', 'patid: pat030', 'patid: pat032', 'patid: pat033', 'patid: pat034']\n",
    "    }\n",
    "    \n",
    "    # Find the maximum number of samples\n",
    "    max_samples = max(len(values) for values in sample_chars_dict.values())\n",
    "    \n",
    "    # Create sample names\n",
    "    sample_names = [f\"GSM{i+1}\" for i in range(max_samples)]\n",
    "    \n",
    "    # Create a DataFrame with the right structure\n",
    "    clinical_data = pd.DataFrame(index=list(range(max(sample_chars_dict.keys()) + 1)),\n",
    "                                columns=sample_names)\n",
    "    \n",
    "    # Fill in the data\n",
    "    for row, values in sample_chars_dict.items():\n",
    "        for i, value in enumerate(values):\n",
    "            if i < len(sample_names):  # Make sure we don't go out of bounds\n",
    "                clinical_data.iloc[row, i] = value\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_features = 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",
    "    preview = preview_df(selected_features)\n",
    "    print(\"Preview of extracted clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Create the output directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the extracted features to CSV\n",
    "    selected_features.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3e8a71",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38ee48e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a667009",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9629c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Review the gene identifiers\n",
    "# The identifiers (121_at, 1316_at, etc.) appear to be Affymetrix probe IDs from an Affymetrix microarray platform\n",
    "# These are not human gene symbols but probe identifiers that need to be mapped to gene symbols\n",
    "# The \"_at\" suffix is characteristic of Affymetrix probe IDs\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb3207b",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f189a6d",
   "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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Let's look for platform information in the SOFT file to understand the annotation better\n",
    "print(\"\\nSearching for platform information in SOFT file:\")\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for i, line in enumerate(f):\n",
    "        if '!Series_platform_id' in line:\n",
    "            print(line.strip())\n",
    "            break\n",
    "        if i > 100:  # Limit search to first 100 lines\n",
    "            print(\"Platform ID not found in first 100 lines\")\n",
    "            break\n",
    "\n",
    "# Check if the SOFT file includes any reference to gene symbols\n",
    "print(\"\\nSearching for gene symbol information in SOFT file:\")\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    gene_symbol_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n",
    "            gene_symbol_lines.append(line.strip())\n",
    "        if i > 1000 and len(gene_symbol_lines) > 0:  # Limit search but ensure we found something\n",
    "            break\n",
    "    \n",
    "    if gene_symbol_lines:\n",
    "        print(\"Found references to gene symbols:\")\n",
    "        for line in gene_symbol_lines[:5]:  # Show just first 5 matches\n",
    "            print(line)\n",
    "    else:\n",
    "        print(\"No explicit gene symbol references found in first 1000 lines\")\n",
    "\n",
    "# Look for alternative annotation files or references in the directory\n",
    "print(\"\\nChecking for additional annotation files in the directory:\")\n",
    "all_files = os.listdir(in_cohort_dir)\n",
    "print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f16b0e",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c65ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the columns for gene identifier and gene symbol in the annotation\n",
    "# From the preview, we can see:\n",
    "# - 'ID' column contains the probe IDs (like \"1007_s_at\") which match the gene expression data row indices\n",
    "# - 'Gene Symbol' column contains the gene symbols we want to map to\n",
    "\n",
    "# 2. Get a gene mapping dataframe\n",
    "# Use get_gene_mapping to extract the gene identifier and gene symbol columns\n",
    "# Notice that \"Gene Symbol\" includes multiple genes per probe (e.g., \"DDR1 /// MIR4640\")\n",
    "prob_col = 'ID'\n",
    "gene_col = 'Gene Symbol'\n",
    "mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "\n",
    "print(f\"Gene mapping shape: {mapping_data.shape}\")\n",
    "print(\"Gene mapping sample:\")\n",
    "print(mapping_data.head())\n",
    "\n",
    "# 3. Apply the gene mapping to the gene expression data\n",
    "# This function will handle probes mapped to multiple genes\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "print(f\"Gene data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the mapped 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": "a6355813",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c93839ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene expression 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 expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Try to load the clinical data if it exists\n",
    "try:\n",
    "    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(f\"Loaded clinical features shape: {clinical_features.shape}\")\n",
    "    has_clinical_data = True\n",
    "except FileNotFoundError:\n",
    "    print(f\"Clinical data file not found at {out_clinical_data_file}\")\n",
    "    has_clinical_data = False\n",
    "except pd.errors.EmptyDataError:\n",
    "    print(f\"Clinical data file is empty at {out_clinical_data_file}\")\n",
    "    has_clinical_data = False\n",
    "\n",
    "# Link clinical and genetic data if clinical data exists\n",
    "if has_clinical_data:\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "    print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
    "    \n",
    "    # Check if trait name is in the dataframe index\n",
    "    if trait in linked_data.index:\n",
    "        # 3. Handle missing values\n",
    "        linked_data_clean = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "        \n",
    "        # 4. Determine whether the trait and demographic features are severely biased\n",
    "        is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "        trait_available = True\n",
    "    else:\n",
    "        print(f\"Trait '{trait}' not found in linked data. Using first row as placeholder.\")\n",
    "        # Using first row as placeholder for handle_missing_values function\n",
    "        first_row = linked_data.index[0]\n",
    "        linked_data_clean = handle_missing_values(linked_data, first_row)\n",
    "        print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "        is_biased = True  # Mark as biased since no trait information for COPD\n",
    "        trait_available = False\n",
    "else:\n",
    "    # If no clinical features, just use gene expression data\n",
    "    print(\"No clinical features available, proceeding with gene expression data only\")\n",
    "    linked_data = normalized_gene_data.T  # Transpose to match expected format\n",
    "    linked_data_clean = linked_data\n",
    "    is_biased = True  # Mark as biased since no trait information\n",
    "    trait_available = False\n",
    "\n",
    "# 5. Conduct final quality validation\n",
    "note = \"Dataset contains gene expression data from transbronchial biopsies from COPD patients at different GOLD stages.\"\n",
    "if not trait_available:\n",
    "    note += \" No usable COPD trait information for associative analysis.\"\n",
    "\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=trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data_clean,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data_clean.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset deemed not usable due to quality issues - linked data not saved\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c65648e",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76e25d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene expression 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 expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Use the normalized data for the rest of the analysis\n",
    "gene_data = normalized_gene_data\n",
    "\n",
    "# 2. Since we found out in Step 2 that trait_row is not None, clinical data should be available\n",
    "try:\n",
    "    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(f\"Loaded clinical features shape: {clinical_features.shape}\")\n",
    "    has_clinical_data = True\n",
    "except (FileNotFoundError, pd.errors.EmptyDataError):\n",
    "    print(f\"Clinical data file not found or empty at {out_clinical_data_file}\")\n",
    "    has_clinical_data = False\n",
    "\n",
    "# Create a new DataFrame with a dummy trait column for the purpose of data handling\n",
    "if has_clinical_data:\n",
    "    # Check if the rows in clinical_features are actually traits\n",
    "    if trait in clinical_features.index:\n",
    "        # Proper clinical data with trait information\n",
    "        linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
    "        print(f\"Linked data shape: {linked_data.shape}\")\n",
    "        \n",
    "        # Handle missing values using the trait as the key column\n",
    "        linked_data_clean = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "        \n",
    "        # Determine whether trait and demographic features are biased\n",
    "        is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "        trait_available = True\n",
    "    else:\n",
    "        # The clinical data file exists but doesn't have the trait information\n",
    "        print(f\"Trait '{trait}' not found in clinical data.\")\n",
    "        trait_available = False\n",
    "        has_clinical_data = False  # Treat as if no clinical data\n",
    "else:\n",
    "    # No clinical data available\n",
    "    trait_available = False\n",
    "\n",
    "# If no proper clinical data with trait, use gene expression data only\n",
    "if not has_clinical_data:\n",
    "    print(\"No usable clinical features available, proceeding with gene expression data only\")\n",
    "    linked_data = gene_data.T  # Transpose to match expected format\n",
    "    linked_data_clean = linked_data\n",
    "    is_biased = True  # Mark as biased since no trait information\n",
    "\n",
    "# 5. Conduct final quality validation\n",
    "note = \"Dataset contains gene expression data from transbronchial biopsies from COPD patients at different GOLD stages. \"\n",
    "if not trait_available:\n",
    "    note += \"No usable COPD trait information for associative analysis.\"\n",
    "\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=trait_available,\n",
    "    is_biased=is_biased if trait_available else True,\n",
    "    df=linked_data_clean,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if usable\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data_clean.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset deemed not usable due to quality issues - linked data not saved\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}