{
 "cells": [
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   "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 = \"GSE193442\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)\"\n",
    "in_cohort_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE193442.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE193442.csv\"\n",
    "json_path = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a48629",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf0623c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:56:07.351220Z",
     "iopub.status.busy": "2025-03-25T07:56:07.351084Z",
     "iopub.status.idle": "2025-03-25T07:56:07.439909Z",
     "shell.execute_reply": "2025-03-25T07:56:07.439436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptional profiling of human KIR+ CD8 T cells\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: PBMC'], 1: ['cell type: KIR+ CD8 T']}\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": "01f09e30",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9527f20a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:56:07.441198Z",
     "iopub.status.busy": "2025-03-25T07:56:07.441086Z",
     "iopub.status.idle": "2025-03-25T07:56:07.447669Z",
     "shell.execute_reply": "2025-03-25T07:56:07.447324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyze the dataset based on the background information and sample characteristics\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the series title and sample characteristics, this dataset seems to focus on transcriptional profiling\n",
    "# of human KIR+ CD8 T cells, which suggests it contains gene expression data.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics, we don't see explicit fields for lupus/SLE status, age, or gender\n",
    "# The dataset appears to be focusing only on cell types rather than patient characteristics\n",
    "\n",
    "# For trait (SLE)\n",
    "# No explicit SLE status is provided in the sample characteristics\n",
    "trait_row = None  # No explicit trait information available\n",
    "\n",
    "# For age\n",
    "# No age information is provided in the sample characteristics\n",
    "age_row = None  # No age information available\n",
    "\n",
    "# For gender\n",
    "# No gender information is provided in the sample characteristics\n",
    "gender_row = None  # No gender information available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# Since we don't have access to these variables, we'll define placeholder conversion functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    # Placeholder function since trait data is not available\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    value = value.split(': ')[-1].strip().lower()\n",
    "    if 'lupus' in value or 'sle' in value:\n",
    "        return 1\n",
    "    elif 'control' in value or 'healthy' in value or 'normal' in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    # Placeholder function since age data is not available\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        # Extract value after colon and convert to float\n",
    "        age_str = value.split(': ')[-1].strip()\n",
    "        return float(age_str)\n",
    "    except (ValueError, AttributeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Placeholder function since gender data is not available\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    value = value.split(': ')[-1].strip().lower()\n",
    "    if 'female' in value or 'f' == value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Check if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save the initial filtering result\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. Since trait_row is None, we skip the clinical feature extraction step\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f35d3f2",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d51a12ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:56:07.448860Z",
     "iopub.status.busy": "2025-03-25T07:56:07.448757Z",
     "iopub.status.idle": "2025-03-25T07:56:07.992086Z",
     "shell.execute_reply": "2025-03-25T07:56:07.991423Z"
    }
   },
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene data extraction result:\n",
      "Number of rows: 0\n",
      "First 20 gene/probe identifiers:\n",
      "Index([], 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.\")"
   ]
  }
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