{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd8b22b7",
   "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 = \"Celiac_Disease\"\n",
    "cohort = \"GSE87629\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Celiac_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Celiac_Disease/GSE87629\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Celiac_Disease/GSE87629.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Celiac_Disease/gene_data/GSE87629.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Celiac_Disease/clinical_data/GSE87629.csv\"\n",
    "json_path = \"../../output/preprocess/Celiac_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923a604f",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c028ed6",
   "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": "5e59e5e5",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "162d98ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Dict, Any, Callable, Optional\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data from DNA microarray\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait: we can use the biopsy data (villus height to crypt depth) as a measure of celiac disease severity\n",
    "trait_row = 5  # biopsy data, villus height to crypt depth\n",
    "\n",
    "# No age information is available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# No gender information is available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert villus height to crypt depth ratio to a continuous value.\"\"\"\n",
    "    if not value or value == 'NA' or ':' not in value:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        # Extract the numeric value after the colon\n",
    "        parts = value.split(':', 1)\n",
    "        if len(parts) < 2:\n",
    "            return None\n",
    "        \n",
    "        # Convert to float\n",
    "        numeric_value = float(parts[1].strip())\n",
    "        return numeric_value\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Placeholder function for age conversion.\"\"\"\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Placeholder function for gender conversion.\"\"\"\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save 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. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Sample characteristics dictionary from the previous step\n",
    "    sample_char_dict = {\n",
    "        0: ['individual: celiac patient A', 'individual: celiac patient C', 'individual: celiac patient G', 'individual: celiac patient H', 'individual: celiac patient K', 'individual: celiac patient L', 'individual: celiac patient M', 'individual: celiac patient N', 'individual: celiac patient O', 'individual: celiac patient P', 'individual: celiac patient Q', 'individual: celiac patient R', 'individual: celiac patient S', 'individual: celiac patient T', 'individual: celiac patient U', 'individual: celiac patient V', 'individual: celiac patient W', 'individual: celiac patient X', 'individual: celiac patient Y', 'individual: celiac patient Z'],\n",
    "        1: ['disease state: biopsy confirmed celiac disease on gluten-free diet greater than one year'],\n",
    "        2: ['treatment: control', 'treatment: 6 weeks gluten challenge'],\n",
    "        3: ['tissue: peripheral whole blood'],\n",
    "        4: ['cell type: purified pool of B and T cells'],\n",
    "        5: ['biopsy data, villus height to crypt depth: 2.9', 'biopsy data, villus height to crypt depth: 2.6', 'biopsy data, villus height to crypt depth: 1.1', 'biopsy data, villus height to crypt depth: 0.5', 'biopsy data, villus height to crypt depth: 0.3', 'biopsy data, villus height to crypt depth: 2', 'biopsy data, villus height to crypt depth: 0.4', 'biopsy data, villus height to crypt depth: 2.4', 'biopsy data, villus height to crypt depth: 1.4', 'biopsy data, villus height to crypt depth: 2.7', 'biopsy data, villus height to crypt depth: 3.5', 'biopsy data, villus height to crypt depth: 0.7', 'biopsy data, villus height to crypt depth: 0.2', 'biopsy data, villus height to crypt depth: 2.8', 'biopsy data, villus height to crypt depth: 3', 'biopsy data, villus height to crypt depth: 0.8', 'biopsy data, villus height to crypt depth: 1.2', 'biopsy data, villus height to crypt depth: 1.7', 'biopsy data, villus height to crypt depth: 2.5', 'biopsy data, villus height to crypt depth: 2.1', 'biopsy data, villus height to crypt depth: 3.1'],\n",
    "        6: ['hybridization batch: 1']\n",
    "    }\n",
    "    \n",
    "    # Convert to a proper format for the geo_select_clinical_features function\n",
    "    # We need to create a DataFrame where each row is a feature type and columns are samples\n",
    "    # First, get the maximum number of samples for any feature\n",
    "    max_samples = max(len(values) for values in sample_char_dict.values())\n",
    "    \n",
    "    # Create a DataFrame with features as rows and pad with NaN for missing values\n",
    "    data = {}\n",
    "    for row_id, values in sample_char_dict.items():\n",
    "        data[row_id] = values + [None] * (max_samples - len(values))\n",
    "    \n",
    "    clinical_data = pd.DataFrame(data).T  # Transpose to have features as rows\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 the clinical features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(f\"Clinical features preview: {preview}\")\n",
    "    \n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save clinical features to CSV\n",
    "    clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical features saved to: {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e058a0",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "766799fc",
   "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": "29fef58d",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a52f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the identifiers, I can recognize these as Illumina probe IDs (ILMN_xxxxxxx format)\n",
    "# These are not standard human gene symbols and will need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8796b3c5",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b024383c",
   "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": "173d1067",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677e16e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine the columns for mapping\n",
    "# Looking at the gene annotation preview, 'ID' seems to contain the probe identifiers (ILMN_xxxxxxx)\n",
    "# and 'Symbol' contains the gene symbols\n",
    "prob_col = 'ID'\n",
    "gene_col = 'Symbol'\n",
    "\n",
    "print(f\"Using columns for mapping: {prob_col} -> {gene_col}\")\n",
    "\n",
    "# 2. Get a gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"First few rows of mapping dataframe:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Preview the first few rows of gene expression data\n",
    "print(\"Preview of gene expression data:\")\n",
    "preview_gene_data = preview_df(gene_data)\n",
    "print(preview_gene_data)\n",
    "\n",
    "# Create directory if it doesn't exist\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "\n",
    "# Save gene expression data to CSV\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": "44383014",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9b702cb",
   "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",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Load clinical data from the saved file\n",
    "clinical_df = pd.read_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data shape: {clinical_df.shape}\")\n",
    "\n",
    "# Rename the first column to be our trait\n",
    "clinical_df = pd.DataFrame({trait: clinical_df.iloc[:, 0]})\n",
    "print(f\"Reformatted clinical data shape: {clinical_df.shape}\")\n",
    "print(f\"Clinical data columns: {clinical_df.columns}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are severely biased\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",
    "note = \"Dataset contains gene expression from B and T cells in celiac disease patients during a gluten challenge. The biopsy data (villus height to crypt depth ratio) is used as a continuous measure of celiac disease severity.\"\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=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as a CSV file\n",
    "if is_usable:\n",
    "    print(f\"Data is usable. Saving to {out_data_file}\")\n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "else:\n",
    "    print(\"Data is not usable. Not saving linked data file.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4949198a",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b7e2724",
   "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",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Debug: examine the structure of clinical_data\n",
    "print(\"Clinical data structure:\")\n",
    "print(f\"Shape: {clinical_data.shape}\")\n",
    "print(\"First row of clinical data:\")\n",
    "print(clinical_data.iloc[0])\n",
    "\n",
    "# Extract clinical features properly\n",
    "# The first row contains sample accession IDs\n",
    "sample_ids = clinical_data.iloc[0].values\n",
    "print(f\"Number of sample IDs: {len(sample_ids)}\")\n",
    "print(\"First few sample IDs:\", sample_ids[:5])\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=None,\n",
    "    convert_age=None,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "print(\"Selected clinical data:\")\n",
    "print(f\"Shape: {selected_clinical_df.shape}\")\n",
    "print(\"First few elements:\")\n",
    "print(selected_clinical_df.iloc[:, :5])\n",
    "\n",
    "# 2. Link the clinical and genetic data using the correct function\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data columns:\", linked_data.columns[:10])  # Show first 10 columns\n",
    "\n",
    "# Check if we have any samples with trait data\n",
    "if linked_data.shape[0] > 0 and trait in linked_data.columns:\n",
    "    # 3. Handle missing values in the linked data\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",
    "    if linked_data_clean.shape[0] > 0:\n",
    "        # 4. Determine whether the trait and demographic features are severely biased\n",
    "        is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "        \n",
    "        # 5. Conduct quality check and save cohort information\n",
    "        note = \"Dataset contains gene expression from B and T cells in celiac disease patients during a gluten challenge. The biopsy data (villus height to crypt depth ratio) is used as a continuous measure of celiac disease severity.\"\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=True,\n",
    "            is_biased=is_trait_biased,\n",
    "            df=unbiased_linked_data,\n",
    "            note=note\n",
    "        )\n",
    "        \n",
    "        # 6. If the linked data is usable, save it as a CSV file\n",
    "        if is_usable:\n",
    "            print(f\"Data is usable. Saving to {out_data_file}\")\n",
    "            # Create directory if it doesn't exist\n",
    "            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "            unbiased_linked_data.to_csv(out_data_file)\n",
    "        else:\n",
    "            print(\"Data is not usable. Not saving linked data file.\")\n",
    "    else:\n",
    "        print(\"No samples remaining after cleaning 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=\"No valid samples remained after cleaning. Cannot proceed with analysis.\"\n",
    "        )\n",
    "else:\n",
    "    print(\"No trait column in linked data. Cannot proceed with analysis.\")\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=\"Failed to properly link clinical and genetic data. No trait column present in linked data.\"\n",
    "    )\n",
    "    print(\"Data is not usable. Not saving linked data file.\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}