{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baa6ec5b",
   "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 = \"Crohns_Disease\"\n",
    "cohort = \"GSE66407\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE66407\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE66407.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE66407.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE66407.csv\"\n",
    "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78122d19",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "badbdfd4",
   "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": "69899cd0",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f263e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis of dataset to determine gene expression data availability and clinical feature extraction\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Gene Expression Data Availability \n",
    "# Based on the background information, this dataset contains gut biopsies with transcriptome analysis\n",
    "# This indicates gene expression data, not just miRNA or methylation\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Trait (Crohn's Disease) - From key 3 \"diagnosis: CD\"\n",
    "trait_row = 3\n",
    "\n",
    "# Age - From key 2 \"age: XX\"\n",
    "age_row = 2\n",
    "\n",
    "# Gender - Not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert diagnosis information to binary trait value (0: Control, 1: CD).\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value == 'CD':\n",
    "        return 1  # Has Crohn's Disease\n",
    "    elif value == 'Control':\n",
    "        return 0  # Control/Healthy\n",
    "    else:\n",
    "        return None  # UC or other diagnoses\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age information to continuous value.\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Placeholder function for gender conversion, though gender data is not available.\"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata - initial filtering\n",
    "# trait_row is not None, so trait data is available\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=(trait_row is not None)\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction (since trait_row is not None)\n",
    "# Use a safer approach to parse the sample characteristics dictionary from the previous output\n",
    "sample_char_dict = {0: ['patient: 10', 'patient: 53', 'patient: 22', 'patient: 91', 'patient: 23', 'patient: 96', 'patient: 50', 'patient: 9', 'patient: 25', 'patient: 97', 'patient: 12', 'patient: 52', 'patient: 101', 'patient: 29', 'patient: 51', 'patient: 107', 'patient: 43', 'patient: 11', 'patient: 109', 'patient: 40', 'patient: 113', 'patient: 116', 'patient: 39', 'patient: 120', 'patient: 34', 'patient: 48', 'patient: 59', 'patient: 65', 'patient: 99', 'patient: 28'], \n",
    "                    1: ['biopsy: 2', 'biopsy: 3', 'biopsy: 6', 'biopsy: 5', 'biopsy: 1', 'biopsy: 4', 'biopsy: 7', 'biopsy: 8', 'biopsy: G1', 'biopsy: 9', 'biopsy: 1A', 'biopsy: 1B'], \n",
    "                    2: ['age: 37', 'age: 18', 'age: 19', 'age: 54', 'age: 70', 'age: 22', 'age: 45', 'age: 62', 'age: 31', 'age: 39', 'age: 67', 'age: 24', 'age: 59', 'age: 20', 'age: 77', 'age: 68', 'age: 41', 'age: 50', 'age: 35', 'age: 36', 'age: 43', 'age: 52', 'age: 21', 'age: 63', 'age: 29', 'age: 25', 'age: 26', 'age: 28', 'age: 53', 'age: 69'], \n",
    "                    3: ['diagnosis: Control', 'diagnosis: CD', 'diagnosis: UC', None, 'inflammation: non', 'inflammation: yes'], \n",
    "                    4: ['gastroscopy: FALSE', 'gastroscopy: TRUE', None, 'tissue: transversum', 'tissue: sigmoideum'], \n",
    "                    5: ['inflammation: non', 'inflammation: yes', None, 'tissue: descendens', 'tissue: sigmoideum', 'tissue: rectum'], \n",
    "                    6: ['tissue: ascendens', 'tissue: sigmoideum', 'tissue: ileum', 'tissue: rectum', 'tissue: descendens', 'tissue: transversum', 'tissue: coecum', None, 'tissue: bulbus durodenum', 'tissue: valvula']}\n",
    "\n",
    "# Create clinical data DataFrame properly\n",
    "# We need to account for the fact that each list in the dictionary may have different lengths\n",
    "# Find the maximum length\n",
    "max_length = max(len(values) for values in sample_char_dict.values())\n",
    "\n",
    "# Pad shorter lists with NaN\n",
    "padded_dict = {}\n",
    "for key, values in sample_char_dict.items():\n",
    "    padded_values = values + [None] * (max_length - len(values))\n",
    "    padded_dict[key] = padded_values\n",
    "\n",
    "# Create DataFrame from padded dictionary\n",
    "clinical_data = pd.DataFrame(padded_dict)\n",
    "\n",
    "# Extract clinical features\n",
    "clinical_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 clinical features\n",
    "preview_clinical = preview_df(clinical_features)\n",
    "print(\"Preview of extracted clinical features:\")\n",
    "print(preview_clinical)\n",
    "\n",
    "# Save clinical features to CSV\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97fadc0",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1012839",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract the gene expression 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)\n",
    "print(\"\\nFirst 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# 4. Print the dimensions of the gene expression data\n",
    "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07b021e",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0344dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers provided, they appear to be Ensembl gene IDs with an \"_at\" suffix\n",
    "# Ensembl IDs typically start with \"ENSG\" for human genes, followed by a unique number\n",
    "# These are not standard human gene symbols (like BRCA1, TP53, etc.)\n",
    "# Therefore, they need to be mapped to standard gene symbols for better interpretability\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d337dc",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ddbc0d3",
   "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": "d5e080bc",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50003d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Looking at the gene annotation preview, we need to extract gene symbols from 'Description'\n",
    "# Example: \"tetraspanin 6 [Source:HGNC Symbol;Acc:11858]\" should yield \"TSPAN6\"\n",
    "\n",
    "# Let's check the Description field format\n",
    "print(\"\\nSample Description fields:\")\n",
    "print(gene_annotation['Description'].head(10).tolist())\n",
    "\n",
    "# Create a function to extract gene symbols from the Description field\n",
    "def extract_gene_name(description):\n",
    "    \"\"\"Extract gene name from the description field - everything before [Source: part\"\"\"\n",
    "    if pd.isna(description):\n",
    "        return None\n",
    "    \n",
    "    # Extract the gene name - everything before [Source: part\n",
    "    if '[Source:' in description:\n",
    "        gene_name = description.split('[Source:')[0].strip()\n",
    "        return gene_name\n",
    "    \n",
    "    return None\n",
    "\n",
    "# Apply the function to extract gene names\n",
    "gene_annotation['Gene_Name'] = gene_annotation['Description'].apply(extract_gene_name)\n",
    "\n",
    "# Print some examples to verify extraction\n",
    "print(\"\\nSample Gene Name extractions:\")\n",
    "sample_extractions = gene_annotation[['ID', 'Description', 'Gene_Name']].head(10)\n",
    "print(sample_extractions)\n",
    "\n",
    "# Use extract_human_gene_symbols to get likely gene symbols from the gene names\n",
    "gene_annotation['Symbol'] = gene_annotation['Gene_Name'].apply(extract_human_gene_symbols)\n",
    "\n",
    "# Check which rows have symbols\n",
    "has_symbols = gene_annotation['Symbol'].apply(lambda x: len(x) > 0 if isinstance(x, list) else False)\n",
    "print(f\"\\nRows with extracted symbols: {has_symbols.sum()} out of {len(gene_annotation)}\")\n",
    "\n",
    "# Create an exploded dataframe for mapping\n",
    "mapping_df = gene_annotation[['ID', 'Symbol']].copy()\n",
    "# Convert empty lists to None to make dropna work correctly\n",
    "mapping_df.loc[mapping_df['Symbol'].apply(lambda x: isinstance(x, list) and len(x) == 0), 'Symbol'] = None\n",
    "# Explode the Symbol column to handle cases where multiple symbols were extracted\n",
    "mapping_df = mapping_df.explode('Symbol')\n",
    "mapping_df = mapping_df.dropna(subset=['Symbol'])\n",
    "mapping_df = mapping_df.rename(columns={'Symbol': 'Gene'})\n",
    "\n",
    "# Print the shape of the mapping dataframe\n",
    "print(f\"\\nMapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"\\nSample mappings:\")\n",
    "print(mapping_df.head(10))\n",
    "\n",
    "# Apply the mapping to convert probe-level data to gene expression data\n",
    "gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print the shape of the resulting gene expression data\n",
    "print(f\"\\nAfter mapping, gene data dimensions: {gene_data_mapped.shape[0]} genes × {gene_data_mapped.shape[1]} samples\")\n",
    "\n",
    "# Preview the first few gene symbols\n",
    "if gene_data_mapped.shape[0] > 0:\n",
    "    print(\"\\nFirst few gene symbols after mapping:\")\n",
    "    print(gene_data_mapped.index[:10])\n",
    "else:\n",
    "    print(\"\\nWarning: No genes were mapped. Check mapping process.\")\n",
    "\n",
    "# Save the gene expression data\n",
    "gene_data_mapped.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Update the gene_data variable for subsequent steps\n",
    "gene_data = gene_data_mapped\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe8af0e",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9980d133",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Re-extract clinical data from the SOFT file to ensure we have properly structured data\n",
    "print(\"Re-extracting clinical data from the SOFT file...\")\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Get raw clinical data\n",
    "background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Transpose the clinical data to have samples as columns\n",
    "clinical_df_t = clinical_df.T\n",
    "# First row contains the geo accessions, use it as column names\n",
    "clinical_df_t.columns = clinical_df_t.iloc[0]\n",
    "# Remove the first row that has been used as column names\n",
    "clinical_df_t = clinical_df_t.iloc[1:]\n",
    "\n",
    "# Now extract relevant clinical features (for Crohn's Disease and age)\n",
    "trait_values = []\n",
    "if trait_row is not None:\n",
    "    for col in clinical_df_t.columns:\n",
    "        trait_val = convert_trait(clinical_df_t.iloc[trait_row-1, col])  # Adjust index for 0-based\n",
    "        trait_values.append(trait_val)\n",
    "\n",
    "age_values = []\n",
    "if age_row is not None:\n",
    "    for col in clinical_df_t.columns:\n",
    "        age_val = convert_age(clinical_df_t.iloc[age_row-1, col])  # Adjust index for 0-based\n",
    "        age_values.append(age_val)\n",
    "\n",
    "# Create proper clinical features dataframe with samples as rows\n",
    "sample_ids = clinical_df_t.columns.tolist()\n",
    "clinical_features = pd.DataFrame()\n",
    "\n",
    "if trait_values:\n",
    "    clinical_features[trait] = trait_values\n",
    "if age_values:\n",
    "    clinical_features['Age'] = age_values\n",
    "\n",
    "# Set index to sample IDs\n",
    "clinical_features.index = sample_ids\n",
    "print(f\"Re-extracted clinical features shape: {clinical_features.shape}\")\n",
    "print(\"Clinical features preview:\")\n",
    "print(clinical_features.head())\n",
    "\n",
    "# Save the improved clinical features\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Improved clinical features saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Check if clinical features were successfully extracted with non-null values\n",
    "if clinical_features.empty or clinical_features[trait].isnull().all():\n",
    "    print(\"Failed to extract valid clinical features with trait values. Dataset cannot be processed further.\")\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=False,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Valid clinical features with trait values could not be extracted.\"\n",
    "    )\n",
    "else:\n",
    "    # 2. Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    \n",
    "    # Transpose gene_data to have samples as rows and genes as columns\n",
    "    gene_data_t = gene_data.T\n",
    "    \n",
    "    # Keep only common samples between clinical and gene data\n",
    "    common_samples = list(set(clinical_features.index) & set(gene_data_t.index))\n",
    "    print(f\"Common samples between clinical and gene data: {len(common_samples)}\")\n",
    "    \n",
    "    if len(common_samples) == 0:\n",
    "        print(\"No common samples between clinical and gene data. Dataset cannot be processed further.\")\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=True,\n",
    "            df=pd.DataFrame(),\n",
    "            note=\"No common samples between clinical and gene data.\"\n",
    "        )\n",
    "    else:\n",
    "        # Filter both datasets to only include common samples\n",
    "        clinical_common = clinical_features.loc[common_samples]\n",
    "        gene_data_common = gene_data_t.loc[common_samples]\n",
    "        \n",
    "        # Merge the datasets\n",
    "        linked_data = pd.concat([clinical_common, gene_data_common], axis=1)\n",
    "        print(f\"Linked data shape: {linked_data.shape}\")\n",
    "        \n",
    "        # 3. Handle missing values systematically\n",
    "        linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "        \n",
    "        # Check if there are still samples after missing value handling\n",
    "        if linked_data.shape[0] == 0:\n",
    "            print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
    "                df=pd.DataFrame(),\n",
    "                note=\"All samples were removed during missing value handling.\"\n",
    "            )\n",
    "        else:\n",
    "            # 4. Check if the dataset is biased\n",
    "            print(\"\\nChecking for bias in feature variables:\")\n",
    "            is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "            \n",
    "            # 5. Conduct final quality validation\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_biased,\n",
    "                df=linked_data,\n",
    "                note=\"Dataset contains gene expression data for Crohn's Disease.\"\n",
    "            )\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 deemed not usable for trait association studies, linked data not saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b21d50f",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79d61edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Skip gene symbol normalization and use the accession numbers directly\n",
    "print(\"Processing gene expression data...\")\n",
    "# Don't normalize - these are GenBank accessions, not gene symbols\n",
    "gene_data_normalized = gene_data  # Use the original gene data with accession numbers\n",
    "\n",
    "# Save the gene data (without normalization)\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",
    "print(f\"Gene data shape: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# 2. Extract clinical features from scratch\n",
    "print(\"Extracting clinical features from original clinical data...\")\n",
    "clinical_features = geo_select_clinical_features(\n",
    "    clinical_data, \n",
    "    trait, \n",
    "    trait_row,\n",
    "    convert_trait,\n",
    "    age_row,\n",
    "    convert_age,\n",
    "    gender_row,\n",
    "    convert_gender\n",
    ")\n",
    "\n",
    "# Save the extracted clinical features\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "\n",
    "print(\"Clinical features preview:\")\n",
    "print(preview_df(clinical_features))\n",
    "\n",
    "# Check if clinical features were successfully extracted\n",
    "if clinical_features.empty:\n",
    "    print(\"Failed to extract clinical features. Dataset cannot be processed further.\")\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=False,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Clinical features could not be extracted from the dataset.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of clinical features.\")\n",
    "else:\n",
    "    # 2. Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "    # Check if the linked data has gene features\n",
    "    if linked_data.shape[1] <= 1:\n",
    "        print(\"Error: Linked data has no gene features. Dataset cannot be processed further.\")\n",
    "        is_usable = validate_and_save_cohort_info(\n",
    "            is_final=True,\n",
    "            cohort=cohort,\n",
    "            info_path=json_path,\n",
    "            is_gene_available=False,\n",
    "            is_trait_available=True,\n",
    "            is_biased=True,\n",
    "            df=linked_data,\n",
    "            note=\"Failed to link gene expression data with clinical features.\"\n",
    "        )\n",
    "    else:\n",
    "        # 3. Handle missing values systematically\n",
    "        linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "        \n",
    "        # Check if there are still samples after missing value handling\n",
    "        if linked_data.shape[0] == 0:\n",
    "            print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
    "                df=pd.DataFrame(),\n",
    "                note=\"All samples were removed during missing value handling.\"\n",
    "            )\n",
    "        else:\n",
    "            # 4. Check if the dataset is biased\n",
    "            print(\"\\nChecking for bias in feature variables:\")\n",
    "            is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "            # 5. Conduct final quality validation\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_biased,\n",
    "                df=linked_data,\n",
    "                note=\"Dataset contains gene expression data for Crohn's Disease patients and healthy controls.\"\n",
    "            )\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 deemed not usable for trait association studies, linked data not saved.\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}