{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9077b62",
   "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 = \"GSE112102\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Celiac_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Celiac_Disease/GSE112102\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Celiac_Disease/GSE112102.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Celiac_Disease/gene_data/GSE112102.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Celiac_Disease/clinical_data/GSE112102.csv\"\n",
    "json_path = \"../../output/preprocess/Celiac_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea494e7c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f33c9d95",
   "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": "fc79e0b1",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c871202",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Analyze gene expression data availability\n",
    "is_gene_available = True  # Based on the Series_summary, this dataset contains gene expression data\n",
    "\n",
    "# 2. Analyze variable availability and conversion functions\n",
    "# 2.1 Identify rows for trait, age, and gender\n",
    "trait_row = 1  # The trait information is in row 1 (group: CeD, control, FDR)\n",
    "age_row = 2    # Age information is in row 2\n",
    "gender_row = 4  # Gender information is in row 4\n",
    "\n",
    "# 2.2 Define conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait values to binary (1 for CeD, 0 for control, None for FDR)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if value.lower() == 'ced':\n",
    "        return 1  # Celiac Disease\n",
    "    elif value.lower() == 'control':\n",
    "        return 0  # Control\n",
    "    else:\n",
    "        return None  # FDR (First Degree Relatives) are neither cases nor controls\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous numeric values\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary (0 for Female, 1 for Male)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if value.lower() == 'female':\n",
    "        return 0\n",
    "    elif value.lower() == 'male':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save metadata for initial filtering\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. Extract clinical features if trait data is available\n",
    "if trait_row is not None:\n",
    "    # Get the clinical data by filtering and processing\n",
    "    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 data\n",
    "    print(\"Preview of extracted clinical data:\")\n",
    "    print(preview_df(clinical_df))\n",
    "    \n",
    "    # Save clinical data to CSV file\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bf6594",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f0e6d5e",
   "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": "2f7c9c58",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e69d07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These identifiers begin with \"ILMN_\" which indicates they are Illumina probe IDs,\n",
    "# specifically from an Illumina BeadArray microarray platform.\n",
    "# These are not human gene symbols but rather probe identifiers that need to be\n",
    "# mapped to gene symbols for biological interpretation.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a615f808",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9bb14b3",
   "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": "c119e2c7",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58e2c871",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the columns for mapping\n",
    "# From the gene annotation preview, we can see that 'ID' contains the probe identifiers (ILMN_*) \n",
    "# and 'Symbol' contains the gene symbols\n",
    "prob_col = 'ID'  # Column with probe IDs matching the gene expression data index\n",
    "gene_col = 'Symbol'  # Column with gene symbols\n",
    "\n",
    "# 2. Create the gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(f\"Gene expression data after mapping - shape: {gene_data.shape}\")\n",
    "print(\"First 5 genes in the mapped data:\")\n",
    "print(gene_data.index[:5])\n",
    "\n",
    "# Save the gene expression data to CSV\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": "1cea372c",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "052096f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
    "print(\"First 10 normalized gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Load the clinical data that was saved in Step 2\n",
    "clinical_data = pd.read_csv(out_clinical_data_file)\n",
    "\n",
    "# Convert to proper format for linking\n",
    "clinical_df = pd.DataFrame()\n",
    "sample_ids = clinical_data.columns\n",
    "\n",
    "# Create proper clinical dataframe with samples as columns and features as rows\n",
    "clinical_df[trait] = clinical_data.iloc[0]\n",
    "clinical_df['Age'] = clinical_data.iloc[1]\n",
    "clinical_df['Gender'] = clinical_data.iloc[2]\n",
    "clinical_df.index = [trait, 'Age', 'Gender']\n",
    "clinical_df.columns = sample_ids\n",
    "\n",
    "print(f\"Clinical data shape: {clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_df))\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5])\n",
    "\n",
    "# Transpose linked data to have samples as rows and features as columns\n",
    "linked_data = linked_data.T\n",
    "\n",
    "print(\"After transposing:\")\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Actual column names in linked_data:\", linked_data.columns.tolist()[:10])  # Show first 10 columns\n",
    "\n",
    "# 4. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Validate 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_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from Celiac Disease patients and controls.\"\n",
    ")\n",
    "\n",
    "# 7. 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.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for analysis. No linked data file saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d49f29",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a897ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
    "print(\"First 10 normalized gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Load the clinical data from Step 2\n",
    "clinical_data_path = out_clinical_data_file\n",
    "clinical_data = pd.read_csv(clinical_data_path)\n",
    "print(f\"Loaded clinical data from {clinical_data_path}\")\n",
    "\n",
    "# Create a proper clinical DataFrame with rows as features and columns as samples\n",
    "# First, get column names from the CSV (these are the sample IDs)\n",
    "sample_ids = clinical_data.columns.tolist()\n",
    "# Extract and prepare the clinical features\n",
    "trait_values = clinical_data.iloc[0].values\n",
    "age_values = clinical_data.iloc[1].values\n",
    "gender_values = clinical_data.iloc[2].values\n",
    "\n",
    "# Create DataFrame with correct format for linking\n",
    "clinical_df = pd.DataFrame({\n",
    "    trait: trait_values,\n",
    "    'Age': age_values,\n",
    "    'Gender': gender_values\n",
    "}, index=sample_ids).T\n",
    "\n",
    "print(f\"Clinical data shape: {clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(clinical_df.iloc[:, :5])  # Show first 5 columns\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = pd.concat([clinical_df, gene_data], axis=0)\n",
    "print(f\"Linked data shape after concatenation: {linked_data.shape}\")\n",
    "\n",
    "# Transpose to have samples as rows and features as columns\n",
    "linked_data = linked_data.T\n",
    "print(f\"Linked data shape after transpose: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5])\n",
    "\n",
    "# 4. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Validate 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_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from Celiac Disease patients and controls. FDR samples were excluded from the trait analysis.\"\n",
    ")\n",
    "\n",
    "# 7. 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.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset is not usable for analysis. No linked data file saved.\")"
   ]
  }
 ],
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 "nbformat": 4,
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}