{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6997e14b",
   "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 = \"Endometriosis\"\n",
    "cohort = \"GSE37837\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Endometriosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Endometriosis/GSE37837\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Endometriosis/GSE37837.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Endometriosis/gene_data/GSE37837.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Endometriosis/clinical_data/GSE37837.csv\"\n",
    "json_path = \"../../output/preprocess/Endometriosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b8386e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21e29777",
   "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": "86961c98",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d721f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Check if gene expression data is available\n",
    "# From the background information, we see this dataset contains genome-wide expression analysis\n",
    "# using Agilent whole human genome oligo microarray\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Determine variable availability and create conversion functions\n",
    "\n",
    "# 2.1 Trait Data\n",
    "# Here, endometriosis status can be determined from the 'tissue' field (row 2)\n",
    "# Looking at the unique values, we can see \"Autologous_eutopic\" vs \"Endometrioma_ectopic\"\n",
    "trait_row = 2\n",
    "\n",
    "def convert_trait(value):\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if isinstance(value, str) and \"Endometrioma_ectopic\" in value:\n",
    "        return 1  # Endometriotic tissue\n",
    "    elif isinstance(value, str) and \"Autologous_eutopic\" in value:\n",
    "        return 0  # Normal endometrial tissue\n",
    "    return None\n",
    "\n",
    "# 2.2 Age Data\n",
    "# Age is available in row 0\n",
    "age_row = 0\n",
    "\n",
    "def convert_age(value):\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    try:\n",
    "        if isinstance(value, str):\n",
    "            # Extract numeric age value\n",
    "            age = int(value.split()[0])\n",
    "            return age  # Return as continuous value\n",
    "    except:\n",
    "        pass\n",
    "    return None\n",
    "\n",
    "# 2.3 Gender Data\n",
    "# All samples are from females according to row 1\n",
    "# Since this is a constant feature (only one value), we'll mark it as not available\n",
    "gender_row = None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Not needed since gender is not variable in this dataset, but included for completeness\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if isinstance(value, str) and \"female\" in value.lower():\n",
    "        return 0\n",
    "    elif isinstance(value, str) and \"male\" in value.lower():\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save metadata using the validate_and_save_cohort_info function\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save initial cohort info\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 data is available\n",
    "if trait_row is not None:\n",
    "    # The Sample Characteristics Dictionary was provided in the previous step\n",
    "    sample_chars = {0: ['age (y): 29', 'age (y): 40', 'age (y): 33', 'age (y): 45', 'age (y): 24', 'age (y): 38', 'age (y): 28', 'age (y): 25', 'age (y): 31', 'age (y): 37', 'age (y): 30', 'age (y): 34'], 1: ['gender: female (fertile)'], 2: ['tissue: Autologous_eutopic', 'tissue: Endometrioma_ectopic'], 3: ['subject id: E17', 'subject id: E20', 'subject id: E23', 'subject id: E26', 'subject id: E31', 'subject id: E32', 'subject id: E33', 'subject id: E40', 'subject id: E43', 'subject id: E48', 'subject id: E49', 'subject id: E52', 'subject id: E56', 'subject id: E57', 'subject id: E68', 'subject id: E70', 'subject id: E73', 'subject id: E75'], 4: ['menstrual phase: Proliferative', 'menstrual phase: Secretory'], 5: ['endometrioma severity stage: Severe (stage 4)', 'endometrioma severity stage: Moderate (stage 3)'], 6: ['parity: Pregnancy_1; live offspriing_1', 'parity: Pregnancy_6; live offspriing_6', 'parity: Pregnancy_3; live offspriing_3', 'parity: Pregnancy_3; live offspriing_2', 'parity: Pregnancy_2; live offspriing_1', 'parity: Pregnancy_4; live offspriing_2', 'parity: Pregnancy_2; live offspriing_2', 'parity: Pregnancy_4; live offspriing_4']}\n",
    "    \n",
    "    # First, let's create a dataframe where rows are the feature indices and columns are the sample IDs\n",
    "    # Start with an empty list to hold the sample IDs\n",
    "    sample_ids = []\n",
    "    # Extract subject IDs from row 3\n",
    "    for sample_id_str in sample_chars[3]:\n",
    "        if ':' in sample_id_str:\n",
    "            sample_id = sample_id_str.split(':', 1)[1].strip()\n",
    "            sample_ids.append(sample_id)\n",
    "    \n",
    "    # Create an empty dataframe with rows as feature indices and columns as sample IDs\n",
    "    clinical_data = pd.DataFrame(index=sample_chars.keys(), columns=sample_ids)\n",
    "    \n",
    "    # Now fill the dataframe\n",
    "    # For each feature row and sample, determine the appropriate value\n",
    "    for row_idx, values in sample_chars.items():\n",
    "        for sample_id in sample_ids:\n",
    "            # For each sample, find the most appropriate value\n",
    "            # For now, we'll just use the first value in the list\n",
    "            if values:\n",
    "                clinical_data.loc[row_idx, sample_id] = values[0]\n",
    "    \n",
    "    # Use geo_select_clinical_features 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 extracted clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of clinical data:\")\n",
    "    for key, value in preview.items():\n",
    "        print(f\"{key}: {value}\")\n",
    "    \n",
    "    # Save the extracted clinical features to 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8842678",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb12fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Get the file paths for the SOFT file and matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. First, let's examine the structure of the matrix file to understand its format\n",
    "import gzip\n",
    "\n",
    "# Peek at the first few lines of the file to understand its structure\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    # Read first 100 lines to find the header structure\n",
    "    for i, line in enumerate(file):\n",
    "        if '!series_matrix_table_begin' in line:\n",
    "            print(f\"Found data marker at line {i}\")\n",
    "            # Read the next line which should be the header\n",
    "            header_line = next(file)\n",
    "            print(f\"Header line: {header_line.strip()}\")\n",
    "            # And the first data line\n",
    "            first_data_line = next(file)\n",
    "            print(f\"First data line: {first_data_line.strip()}\")\n",
    "            break\n",
    "        if i > 100:  # Limit search to first 100 lines\n",
    "            print(\"Matrix table marker not found in first 100 lines\")\n",
    "            break\n",
    "\n",
    "# 3. Now try to get the genetic data with better error handling\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(gene_data.index[:20])\n",
    "except KeyError as e:\n",
    "    print(f\"KeyError: {e}\")\n",
    "    \n",
    "    # Alternative approach: manually extract the data\n",
    "    print(\"\\nTrying alternative approach to read the gene data:\")\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        # Find the start of the data\n",
    "        for line in file:\n",
    "            if '!series_matrix_table_begin' in line:\n",
    "                break\n",
    "                \n",
    "        # Read the headers and data\n",
    "        import pandas as pd\n",
    "        df = pd.read_csv(file, sep='\\t', index_col=0)\n",
    "        print(f\"Column names: {df.columns[:5]}\")\n",
    "        print(f\"First 20 row IDs: {df.index[:20]}\")\n",
    "        gene_data = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee466cb3",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdcf0926",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observing the gene identifiers in the data\n",
    "# Based on the preview, we see identifiers like 'A_23_P100001' which are Agilent microarray probe IDs, \n",
    "# not standard human gene symbols\n",
    "# These IDs need to be mapped to gene symbols for biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b1a1f6",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a91eadb3",
   "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": "ae7fa827",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a63aa368",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examine the gene annotation to identify mapping columns\n",
    "# From the preview, we can see that 'ID' in the gene annotation corresponds to the probe identifiers\n",
    "# 'GENE_SYMBOL' contains the human gene symbols we want to map to\n",
    "\n",
    "# 1. Extract the mapping between probe IDs and gene symbols using the get_gene_mapping function\n",
    "probe_col = 'ID'\n",
    "gene_symbol_col = 'GENE_SYMBOL'\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)\n",
    "\n",
    "# 2. Print a sample of the mapping to verify\n",
    "print(\"Gene mapping preview (probe ID to gene symbol):\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level data to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# 4. Preview the first few rows of the gene expression data\n",
    "print(\"\\nGene expression data preview after mapping:\")\n",
    "print(gene_data.head())\n",
    "\n",
    "# 5. Report the shape of the gene expression data\n",
    "print(f\"\\nGene expression data shape: {gene_data.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3fc5c2",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ffd4abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Load clinical features from the previously saved file\n",
    "clinical_features_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "\n",
    "# Create a mapping between GSM IDs and subject IDs from the SOFT file\n",
    "gsm_to_subject_mapping = {}\n",
    "\n",
    "# Extract the mapping from the SOFT file\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for line in f:\n",
    "        if line.startswith('!Sample_geo_accession'):\n",
    "            gsm_id = line.strip().split('=')[1].strip('\"')\n",
    "        elif line.startswith('!Sample_source_name_ch1'):\n",
    "            if 'E' in line:\n",
    "                # Extract subject ID (usually in format \"subject id: E##\")\n",
    "                subject_id = 'E' + line.split('E')[1].split()[0]\n",
    "                gsm_to_subject_mapping[gsm_id] = subject_id\n",
    "\n",
    "# If mapping was created successfully, transform clinical features to align with GSM IDs\n",
    "if gsm_to_subject_mapping:\n",
    "    # Create a new clinical dataframe using GSM IDs as index\n",
    "    new_clinical_df = pd.DataFrame(index=normalized_gene_data.columns)\n",
    "    \n",
    "    # Map trait values from subject IDs to GSM IDs\n",
    "    for gsm_id, subject_id in gsm_to_subject_mapping.items():\n",
    "        if gsm_id in new_clinical_df.index and subject_id in clinical_features_df.columns:\n",
    "            for feature in clinical_features_df.index:\n",
    "                if feature == trait:\n",
    "                    new_clinical_df.loc[gsm_id, feature] = clinical_features_df.loc[feature, subject_id]\n",
    "                elif feature == 'Age':\n",
    "                    new_clinical_df.loc[gsm_id, feature] = clinical_features_df.loc[feature, subject_id]\n",
    "    \n",
    "    clinical_features_df = new_clinical_df.T  # Transpose to get features as rows\n",
    "else:\n",
    "    # If mapping failed, create clinical data from scratch based on SOFT file information\n",
    "    # Extract tissue and age information from the SOFT file\n",
    "    tissue_dict = {}\n",
    "    age_dict = {}\n",
    "    \n",
    "    with gzip.open(soft_file, 'rt') as f:\n",
    "        current_gsm = None\n",
    "        for line in f:\n",
    "            line = line.strip()\n",
    "            if line.startswith('!Sample_geo_accession'):\n",
    "                current_gsm = line.split('=')[1].strip('\"')\n",
    "            elif current_gsm and line.startswith('!Sample_characteristics_ch1'):\n",
    "                value = line.split('=')[1].strip('\"')\n",
    "                if 'tissue:' in value:\n",
    "                    tissue_dict[current_gsm] = 1 if \"Endometrioma_ectopic\" in value else 0\n",
    "                elif 'age (y):' in value:\n",
    "                    try:\n",
    "                        age = int(value.split(':')[1].strip().split()[0])\n",
    "                        age_dict[current_gsm] = age\n",
    "                    except (IndexError, ValueError):\n",
    "                        pass\n",
    "    \n",
    "    # Create clinical dataframe with GSM IDs as index\n",
    "    new_clinical_df = pd.DataFrame(index=normalized_gene_data.columns)\n",
    "    new_clinical_df[trait] = new_clinical_df.index.map(tissue_dict)\n",
    "    new_clinical_df['Age'] = new_clinical_df.index.map(age_dict)\n",
    "    \n",
    "    # Transpose to get features as rows\n",
    "    clinical_features_df = new_clinical_df.T\n",
    "\n",
    "# Now link the clinical and genetic data\n",
    "linked_data = pd.concat([clinical_features_df, normalized_gene_data], axis=0)\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "\n",
    "# Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "\n",
    "# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct quality check and save the cohort information.\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=True, \n",
    "    is_trait_available=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=\"Dataset contains gene expression from eutopic and ectopic endometrial tissues from women with endometriosis.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\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\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Data was determined to be unusable and was not saved\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e458a3",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c0f254e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Create a dictionary to store GSM to tissue type mapping\n",
    "gsm_to_tissue = {}\n",
    "gsm_to_age = {}\n",
    "\n",
    "# Extract tissue type and age for each GSM ID directly from the SOFT file\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    current_gsm = None\n",
    "    for line in f:\n",
    "        line = line.strip()\n",
    "        if line.startswith('!Sample_geo_accession'):\n",
    "            current_gsm = line.split('=')[1].strip('\"')\n",
    "        elif current_gsm and line.startswith('!Sample_characteristics_ch1'):\n",
    "            value = line.split('=')[1].strip('\"')\n",
    "            if 'tissue:' in value:\n",
    "                gsm_to_tissue[current_gsm] = 1 if \"Endometrioma_ectopic\" in value else 0\n",
    "            elif 'age (y):' in value:\n",
    "                try:\n",
    "                    age = int(value.split(':')[1].strip().split()[0])\n",
    "                    gsm_to_age[current_gsm] = age\n",
    "                except (IndexError, ValueError):\n",
    "                    pass\n",
    "\n",
    "# Print sample of mappings to verify data extraction\n",
    "print(f\"Sample of tissue mappings: {list(gsm_to_tissue.items())[:5]}\")\n",
    "print(f\"Sample of age mappings: {list(gsm_to_age.items())[:5]}\")\n",
    "print(f\"Total GSMs with tissue data: {len(gsm_to_tissue)}\")\n",
    "print(f\"Total GSMs with age data: {len(gsm_to_age)}\")\n",
    "\n",
    "# Create clinical data as a DataFrame with appropriate structure for linking\n",
    "# Using the gene expression data column names as sample IDs\n",
    "clinical_data = pd.DataFrame(index=[trait, 'Age'])\n",
    "\n",
    "# Add data for each sample\n",
    "for gsm in normalized_gene_data.columns:\n",
    "    if gsm in gsm_to_tissue:\n",
    "        clinical_data.at[trait, gsm] = gsm_to_tissue[gsm]\n",
    "    if gsm in gsm_to_age:\n",
    "        clinical_data.at['Age', gsm] = gsm_to_age[gsm]\n",
    "\n",
    "# Verify clinical data content\n",
    "print(\"Clinical data shape:\", clinical_data.shape)\n",
    "print(\"Clinical data sample:\")\n",
    "print(clinical_data.iloc[:, :5])\n",
    "\n",
    "# Save clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_data.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = pd.concat([clinical_data, normalized_gene_data])\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "\n",
    "# Print a quick check of the trait column\n",
    "trait_values = clinical_data.loc[trait]\n",
    "print(f\"Number of samples with trait values: {sum(~pd.isna(trait_values))}\")\n",
    "print(f\"Trait value counts: {trait_values.value_counts().to_dict()}\")\n",
    "\n",
    "# Handle missing values using the library function\n",
    "processed_df = handle_missing_values(linked_data, trait)\n",
    "print(\"Shape after handling missing values:\", processed_df.shape)\n",
    "\n",
    "# Check if any data remains after handling missing values\n",
    "if processed_df.shape[0] == 0 or processed_df.shape[1] == 0:\n",
    "    print(\"WARNING: No data remains after handling missing values.\")\n",
    "    # In this case, we'll set is_trait_biased to True as the dataset is unusable\n",
    "    is_trait_biased = True\n",
    "    unbiased_linked_data = processed_df\n",
    "else:\n",
    "    # Determine whether the trait and demographic features are severely biased\n",
    "    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(processed_df, trait)\n",
    "\n",
    "# Conduct quality validation and save cohort information\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=True, \n",
    "    is_trait_available=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=\"Dataset contains gene expression from eutopic and ectopic endometrial tissues from women with endometriosis.\"\n",
    ")\n",
    "\n",
    "# If the linked data is usable, save it\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\"Processed data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Data was determined to be unusable and was not saved\")"
   ]
  }
 ],
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}