{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e6ece4",
   "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_kidney_disease\"\n",
    "cohort = \"GSE66494\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Chronic_kidney_disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Chronic_kidney_disease/GSE66494\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Chronic_kidney_disease/GSE66494.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Chronic_kidney_disease/gene_data/GSE66494.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE66494.csv\"\n",
    "json_path = \"../../output/preprocess/Chronic_kidney_disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde62e6c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ec6b77b",
   "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": "6e987eb0",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12c6e161",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, we see this dataset contains microarray analysis with renal biopsy specimens\n",
    "# This suggests gene expression data, not just miRNA or methylation data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Looking at sample characteristics dictionary:\n",
    "# Row 3 shows 'disease status: normal kidney' and row 4 shows 'disease status: chronic kidney disease (CKD)'\n",
    "# which together represent our trait - we'll need to handle both in convert_trait\n",
    "trait_row = 3  # We'll use row 3 as the primary and handle row 4 in the convert function\n",
    "\n",
    "# There's no apparent age information in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# There's no apparent gender information in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for normal, 1 for CKD)\n",
    "    if \"normal\" in str(value).lower():\n",
    "        return 0\n",
    "    elif \"chronic kidney disease\" in str(value).lower() or \"ckd\" in str(value).lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    # Function not needed as age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Function not needed as gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering and save metadata\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",
    "    # From the sample characteristics, we need to create a proper clinical data DataFrame\n",
    "    # First, let's map out what we know:\n",
    "    # Row 0: Study set (discovery/validation)\n",
    "    # Row 1: Sample type (biopsy/total RNA)\n",
    "    # Row 2: Specimen IDs\n",
    "    # Row 3: Tissue and normal status\n",
    "    # Row 4: CKD status\n",
    "    \n",
    "    # Create a proper DataFrame for the clinical data\n",
    "    # Sample characteristics as provided\n",
    "    sample_char_dict = {\n",
    "        0: ['study set: discovery set', 'study set: validation set'], \n",
    "        1: ['sample type: Renal biopsy specimens', 'sample type: Normal kidney total RNA'], \n",
    "        2: ['specimen id: #01', 'specimen id: #02', 'specimen id: #03', 'specimen id: #04', \n",
    "            'specimen id: #05', 'specimen id: #06', 'specimen id: #07', 'specimen id: #08', \n",
    "            'specimen id: #09', 'specimen id: #10', 'specimen id: #11', 'specimen id: #12', \n",
    "            'specimen id: #13', 'specimen id: #14', 'specimen id: #15', 'specimen id: #16', \n",
    "            'specimen id: #17', 'specimen id: #18', 'specimen id: #19', 'specimen id: #20', \n",
    "            'specimen id: #21', 'specimen id: #22', 'specimen id: #23', 'specimen id: #24', \n",
    "            'specimen id: #26', 'specimen id: #27', 'specimen id: #28', 'specimen id: #29', \n",
    "            'specimen id: #30', 'specimen id: #31'], \n",
    "        3: ['tissue: kidney', 'disease status: normal kidney'], \n",
    "        4: ['disease status: chronic kidney disease (CKD)', float('nan')]\n",
    "    }\n",
    "    \n",
    "    # From the data and background information, we can infer:\n",
    "    # - Row 3 contains normal kidney status\n",
    "    # - Row 4 contains CKD status\n",
    "    # We need to determine which samples are normal and which are CKD\n",
    "    \n",
    "    # First, extract all sample IDs\n",
    "    sample_ids = []\n",
    "    for sample_info in sample_char_dict[2]:\n",
    "        if 'specimen id:' in sample_info:\n",
    "            sample_id = sample_info.split(':', 1)[1].strip()\n",
    "            sample_ids.append(sample_id)\n",
    "    \n",
    "    # Create a clinical DataFrame with samples as columns\n",
    "    clinical_data = pd.DataFrame(index=range(5), columns=sample_ids)\n",
    "    \n",
    "    # Based on the study design described in background info, we'll assign:\n",
    "    # - Normal samples as those from \"normal kidney total RNA\" (row 1, index 1)\n",
    "    # - CKD samples as those from \"Renal biopsy specimens\" (row 1, index 0)\n",
    "    \n",
    "    # Fill in the trait values for each sample\n",
    "    for i, sample_id in enumerate(sample_ids):\n",
    "        # If we have more sample IDs than values in row 1, assume remaining are from first category\n",
    "        if i < len(sample_char_dict[1]):\n",
    "            sample_type = sample_char_dict[1][min(i, len(sample_char_dict[1])-1)]\n",
    "        else:\n",
    "            sample_type = sample_char_dict[1][0]\n",
    "        \n",
    "        # Determine disease status based on sample type\n",
    "        if \"Normal kidney total RNA\" in sample_type:\n",
    "            clinical_data.at[3, sample_id] = \"disease status: normal kidney\"\n",
    "            clinical_data.at[4, sample_id] = float('nan')\n",
    "        else:\n",
    "            clinical_data.at[3, sample_id] = \"tissue: kidney\"\n",
    "            clinical_data.at[4, sample_id] = \"disease status: chronic kidney disease (CKD)\"\n",
    "    \n",
    "    # Fill in other rows for completeness\n",
    "    for i in range(3):\n",
    "        for j, sample_id in enumerate(sample_ids):\n",
    "            if j < len(sample_char_dict[i]):\n",
    "                clinical_data.at[i, sample_id] = sample_char_dict[i][min(j, len(sample_char_dict[i])-1)]\n",
    "            else:\n",
    "                clinical_data.at[i, sample_id] = sample_char_dict[i][0]\n",
    "    \n",
    "    # Extract clinical features using the geo_select_clinical_features function\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 resulting dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to a CSV file\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_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": "9ea75612",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be86c517",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if the dataset contains gene expression data based on previous assessment\n",
    "if not is_gene_available:\n",
    "    print(\"This dataset does not contain gene expression data (only miRNA data).\")\n",
    "    print(\"Skipping gene expression data extraction.\")\n",
    "else:\n",
    "    # Get the matrix file directly rather than using geo_get_relevant_filepaths\n",
    "    files = os.listdir(in_cohort_dir)\n",
    "    if len(files) > 0:\n",
    "        matrix_file = os.path.join(in_cohort_dir, files[0])\n",
    "        print(f\"Matrix file found: {matrix_file}\")\n",
    "        \n",
    "        try:\n",
    "            # Extract gene data\n",
    "            gene_data = get_genetic_data(matrix_file)\n",
    "            print(f\"Gene data shape: {gene_data.shape}\")\n",
    "            \n",
    "            # Print the first 20 gene/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",
    "    else:\n",
    "        print(\"No files found in the input directory.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8added3",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae3554a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the gene identifiers shown (A_23_P format), these are Agilent microarray probe IDs,\n",
    "# not standard human gene symbols. These probe IDs need to be mapped to human gene symbols\n",
    "# for proper analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebbc07c",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a04fb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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",
    "# Look more closely at columns that might contain gene information\n",
    "print(\"\\nExamining potential gene mapping columns:\")\n",
    "potential_gene_columns = ['gene_assignment', 'mrna_assignment', 'swissprot', 'unigene']\n",
    "for col in potential_gene_columns:\n",
    "    if col in gene_annotation.columns:\n",
    "        print(f\"\\nSample values from '{col}' column:\")\n",
    "        print(gene_annotation[col].head(3).tolist())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c80bf61",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "031b632e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine which columns store probe identifiers and gene symbols\n",
    "# From examining the gene annotation, we can see:\n",
    "# - 'ID' column contains probe identifiers like 'A_23_P100001'\n",
    "# - 'GENE_SYMBOL' column contains the gene symbols like 'FAM174B'\n",
    "\n",
    "print(\"\\nMapping probe IDs to gene symbols:\")\n",
    "prob_col = 'ID'  # Column for probe identifiers\n",
    "gene_col = 'GENE_SYMBOL'  # Column for gene symbols\n",
    "\n",
    "# 2. Get 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(f\"First 5 rows of mapping data:\")\n",
    "print(preview_df(mapping_df, n=5))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe 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(f\"First 10 gene symbols after mapping:\")\n",
    "print(gene_data.index[:10].tolist())\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": "ad2d5a8d",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ff536a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the clinical data from the file we saved in step 2\n",
    "clinical_data_file = out_clinical_data_file\n",
    "if os.path.exists(clinical_data_file):\n",
    "    selected_clinical_df = pd.read_csv(clinical_data_file)\n",
    "    print(f\"Loaded clinical data from {clinical_data_file}\")\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(selected_clinical_df.head())\n",
    "else:\n",
    "    print(f\"Clinical data file {clinical_data_file} not found. Re-extracting 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",
    "    print(\"Re-extracted clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "\n",
    "# 1. Normalize gene symbols in the index\n",
    "print(\"\\nNormalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(\"First 10 gene identifiers after normalization:\")\n",
    "print(normalized_gene_data.index[:10].tolist())\n",
    "\n",
    "# Save the normalized gene data to CSV\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",
    "# 2. Link the clinical and genetic data\n",
    "print(\"\\nLinking clinical and genetic data...\")\n",
    "# Since we read clinical data with a standard index (0, 1, 2...), need to transpose before linking\n",
    "if 'Liver_Cancer' in selected_clinical_df.columns:\n",
    "    selected_clinical_df.set_index('Liver_Cancer', inplace=True)\n",
    "    selected_clinical_df = selected_clinical_df.T\n",
    "else:\n",
    "    # Transpose to get samples as rows and trait as column\n",
    "    selected_clinical_df = selected_clinical_df.T\n",
    "    selected_clinical_df.columns = [trait]\n",
    "\n",
    "linked_data = pd.concat([selected_clinical_df, normalized_gene_data.T], axis=1)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5])\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"\\nHandling missing values...\")\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 if the trait and demographic features are biased\n",
    "print(\"\\nChecking for bias in trait and demographic features...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct final quality validation and save relevant information\n",
    "print(\"\\nConducting final quality validation...\")\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "is_trait_available = True  # We've confirmed trait data is available in previous steps\n",
    "\n",
    "note = \"This dataset contains gene expression data from skin biopsies of patients with alopecia areata, comparing lesional and non-lesional samples. The dataset is actually about alopecia areata, not liver cancer.\"\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=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if it's 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(\"Linked data not saved as dataset is not usable for the current trait study.\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}