{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f434122e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:01.376569Z",
     "iopub.status.busy": "2025-03-25T07:55:01.376303Z",
     "iopub.status.idle": "2025-03-25T07:55:01.542902Z",
     "shell.execute_reply": "2025-03-25T07:55:01.542550Z"
    }
   },
   "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 = \"Lupus_(Systemic_Lupus_Erythematosus)\"\n",
    "cohort = \"GSE148810\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)\"\n",
    "in_cohort_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE148810.csv\"\n",
    "json_path = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec60231",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d90f3b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:01.544420Z",
     "iopub.status.busy": "2025-03-25T07:55:01.544265Z",
     "iopub.status.idle": "2025-03-25T07:55:01.632339Z",
     "shell.execute_reply": "2025-03-25T07:55:01.632018Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Gene expression of skin biopsie samples from juvenile myositis (JM) and childhood-onset lupus (cSLE).\"\n",
      "!Series_summary\t\"Skin inflammaton heralds systemic disease in juvenile myositis (JM), yet we lack an understanding of pathogenic mechanisms driving skin inflammation in JM. The goal of this study is to define cutaneous gene expression signatures in JM and identify key genes and pathways that differentiate skin disease in JM from childhood-onset SLE (cSLE).\"\n",
      "!Series_overall_design\t\"Formalin formalin-fixed paraffin-embedded (FFPE) skin biopsy samples from JM, cSLE, and control (HC) patients were used to perform Affymetrix ST 2.1 microarray analysis and determine differentially expressed genes (DEGs; q-value ≤ 5%) between patient groups.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: Skin biopsy'], 1: ['disease: JM Lesional skin', 'disease: JM Non-lesional skin', 'disease: cSLE skin lesion', 'disease: Normal skin']}\n"
     ]
    }
   ],
   "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": "3af8cde5",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca41a1a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:01.633542Z",
     "iopub.status.busy": "2025-03-25T07:55:01.633427Z",
     "iopub.status.idle": "2025-03-25T07:55:01.638225Z",
     "shell.execute_reply": "2025-03-25T07:55:01.637921Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset appears to contain gene expression data\n",
    "# from microarray analysis (Affymetrix ST 2.1)\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait: Looking at the sample characteristics dictionary, disease information is in row 1\n",
    "trait_row = 1\n",
    "\n",
    "# Age information is not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender information is not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert trait value to binary (1 for SLE, 0 for others)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Check if the value indicates SLE (Systemic Lupus Erythematosus)\n",
    "    if \"cSLE\" in value:\n",
    "        return 1  # SLE positive\n",
    "    else:\n",
    "        return 0  # Not SLE (JM or Normal)\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value to continuous numeric\"\"\"\n",
    "    # Not applicable as age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
    "    # Not applicable 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",
    "# Validate and save cohort info for initial filtering\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",
    "# Since trait_row is not None, we need to extract clinical features\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data\n",
    "    clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
    "    if os.path.exists(clinical_data_path):\n",
    "        clinical_data = pd.read_csv(clinical_data_path)\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=age_row,\n",
    "            convert_age=convert_age,\n",
    "            gender_row=gender_row,\n",
    "            convert_gender=convert_gender\n",
    "        )\n",
    "        \n",
    "        # Preview the extracted features\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of selected clinical features:\")\n",
    "        print(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 the extracted features\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": "68bf01bf",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3484b945",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:01.639317Z",
     "iopub.status.busy": "2025-03-25T07:55:01.639205Z",
     "iopub.status.idle": "2025-03-25T07:55:01.765413Z",
     "shell.execute_reply": "2025-03-25T07:55:01.765020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
      "No subseries references found in the first 1000 lines of the SOFT file.\n",
      "\n",
      "Gene data extraction result:\n",
      "Number of rows: 29635\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['100009613_at', '100009676_at', '10000_at', '10001_at', '10002_at',\n",
      "       '100033411_at', '100033413_at', '100033422_at', '100033423_at',\n",
      "       '100033424_at', '100033425_at', '100033426_at', '100033427_at',\n",
      "       '100033428_at', '100033430_at', '100033431_at', '100033432_at',\n",
      "       '100033434_at', '100033435_at', '100033436_at'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the path to the soft and matrix files\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Looking more carefully at the background information\n",
    "# This is a SuperSeries which doesn't contain direct gene expression data\n",
    "# Need to investigate the soft file to find the subseries\n",
    "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
    "\n",
    "# Open the SOFT file to try to identify subseries\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    subseries_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'Series_relation' in line and 'SuperSeries of' in line:\n",
    "            subseries_lines.append(line.strip())\n",
    "        if i > 1000:  # Limit search to first 1000 lines\n",
    "            break\n",
    "\n",
    "# Display the subseries found\n",
    "if subseries_lines:\n",
    "    print(\"Found potential subseries references:\")\n",
    "    for line in subseries_lines:\n",
    "        print(line)\n",
    "else:\n",
    "    print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
    "\n",
    "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(\"\\nGene data extraction result:\")\n",
    "    print(\"Number of rows:\", len(gene_data))\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",
    "    print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4424abe6",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83115426",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:01.766711Z",
     "iopub.status.busy": "2025-03-25T07:55:01.766586Z",
     "iopub.status.idle": "2025-03-25T07:55:01.768533Z",
     "shell.execute_reply": "2025-03-25T07:55:01.768238Z"
    }
   },
   "outputs": [],
   "source": [
    "# The gene identifiers consist of numbers followed by \"_at\" suffix, which is typical for Affymetrix microarray probes\n",
    "# These are not standard human gene symbols and need to be mapped to proper gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da3e492",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99da98a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:01.769689Z",
     "iopub.status.busy": "2025-03-25T07:55:01.769573Z",
     "iopub.status.idle": "2025-03-25T07:55:02.792213Z",
     "shell.execute_reply": "2025-03-25T07:55:02.791809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'SPOT_ID': ['1', '10', '100', '1000', '10000'], 'DESCRIPTION': ['alpha-1-B glycoprotein', 'N-acetyltransferase 2', 'adenosine deaminase', 'cadherin 2', 'AKT serine/threonine kinase 3'], 'ORF': ['A1BG', 'NAT2', 'ADA', 'CDH2', 'AKT3']}\n"
     ]
    }
   ],
   "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": "94df63f2",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "979b7584",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:02.793643Z",
     "iopub.status.busy": "2025-03-25T07:55:02.793508Z",
     "iopub.status.idle": "2025-03-25T07:55:02.916704Z",
     "shell.execute_reply": "2025-03-25T07:55:02.916286Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping preview:\n",
      "{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'Gene': ['A1BG', 'NAT2', 'ADA', 'CDH2', 'AKT3']}\n",
      "\n",
      "Gene expression data after mapping:\n",
      "Number of genes: 23124\n",
      "First few genes:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
      "       'A4GALT', 'A4GNT', 'AA06'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify columns for gene mapping \n",
    "# From the preview, it seems 'ID' contains probe identifiers and 'ORF' contains gene symbols\n",
    "probe_col = 'ID'  # Column containing probe identifiers\n",
    "gene_col = 'ORF'  # Column containing gene symbols\n",
    "\n",
    "# 2. Extract gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression data\n",
    "# The library function 'apply_gene_mapping' will handle the many-to-many relationship\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(f\"Number of genes: {len(gene_data)}\")\n",
    "print(\"First few genes:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608ab528",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40ae519f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:55:02.918097Z",
     "iopub.status.busy": "2025-03-25T07:55:02.917963Z",
     "iopub.status.idle": "2025-03-25T07:55:13.749013Z",
     "shell.execute_reply": "2025-03-25T07:55:13.748611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 gene indices before normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 gene indices after normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n",
      "Shape of normalized gene data: (22856, 30)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved normalized gene data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv\n",
      "Saved clinical data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE148810.csv\n",
      "Shape of linked data: (30, 22857)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of linked data after handling missing values: (30, 22857)\n",
      "For the feature 'Lupus_(Systemic_Lupus_Erythematosus)', the least common label is '1.0' with 7 occurrences. This represents 23.33% of the dataset.\n",
      "The distribution of the feature 'Lupus_(Systemic_Lupus_Erythematosus)' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved processed linked data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "print(f\"Top 10 gene indices before normalization: {gene_data.index[:10].tolist()}\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Top 10 gene indices after normalization: {normalized_gene_data.index[:10].tolist()}\")\n",
    "print(f\"Shape of normalized gene data: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Create directory for gene data file if it doesn't exist\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "# Save the normalized gene data\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Saved normalized gene data to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Extract clinical features using the clinical data from step 1\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",
    "# Extract clinical features using the convert_trait function from step 2\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_df=clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=1,  # From step 2\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=None,\n",
    "    convert_age=None,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "# Save clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Saved clinical data to {out_clinical_data_file}\")\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Shape of linked data: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape of linked data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine if the trait and demographic features are biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Validate the dataset 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 data from juvenile myositis (JM) and childhood-onset lupus (cSLE) skin biopsies.\"\n",
    ")\n",
    "\n",
    "# 7. 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",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Saved processed linked data to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset validation failed. Final linked data not saved.\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}