{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1946716d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:54:05.146977Z",
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     "shell.execute_reply": "2025-03-25T05:54:05.311022Z"
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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 = \"Multiple_sclerosis\"\n",
    "cohort = \"GSE193442\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Multiple_sclerosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Multiple_sclerosis/GSE193442\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Multiple_sclerosis/GSE193442.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Multiple_sclerosis/gene_data/GSE193442.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Multiple_sclerosis/clinical_data/GSE193442.csv\"\n",
    "json_path = \"../../output/preprocess/Multiple_sclerosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f96dc6",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a303513",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:54:05.312723Z",
     "iopub.status.busy": "2025-03-25T05:54:05.312592Z",
     "iopub.status.idle": "2025-03-25T05:54:05.402430Z",
     "shell.execute_reply": "2025-03-25T05:54:05.402121Z"
    }
   },
   "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": "d15bf249",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f12ef706",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:54:05.403486Z",
     "iopub.status.busy": "2025-03-25T05:54:05.403375Z",
     "iopub.status.idle": "2025-03-25T05:54:05.409769Z",
     "shell.execute_reply": "2025-03-25T05:54:05.409484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from typing import Optional, Callable\n",
    "\n",
    "# Check gene expression data availability\n",
    "# Based on the Series title and description, this appears to be a transcriptional profiling dataset\n",
    "# This suggests gene expression data is likely available\n",
    "is_gene_available = True\n",
    "\n",
    "# Analyze clinical feature availability\n",
    "# From the Sample Characteristics Dictionary, we don't have explicit trait (Multiple_sclerosis), age, or gender information\n",
    "# The data only shows tissue (PBMC) and cell type (KIR+ CD8 T) information\n",
    "\n",
    "# Set availability of trait, age, and gender\n",
    "trait_row = None  # No explicit trait information available\n",
    "age_row = None    # No age information available\n",
    "gender_row = None # No gender information available\n",
    "\n",
    "# Define conversion functions (these won't be used but defined for completeness)\n",
    "def convert_trait(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if value.lower() in ['ms', 'multiple sclerosis']:\n",
    "        return 1\n",
    "    elif value.lower() in ['control', 'healthy', 'normal']:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    value = value.lower()\n",
    "    if value in ['female', 'f']:\n",
    "        return 0\n",
    "    elif value in ['male', 'm']:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Initial validation of dataset usability\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",
    "# No need to extract clinical features since trait data is not available (trait_row is None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af486d18",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5efe565",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T05:54:05.410705Z",
     "iopub.status.busy": "2025-03-25T05:54:05.410602Z",
     "iopub.status.idle": "2025-03-25T05:54:06.033605Z",
     "shell.execute_reply": "2025-03-25T05:54:06.033261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for SubSeries information in the SuperSeries...\n",
      "SubSeries found: []\n",
      "\n",
      "Attempting direct extraction with debugging:\n",
      "First 10 lines of the matrix file:\n",
      "Line 1: !Series_title\t\"Transcriptional profiling of human KIR+ CD8 T cells\"\n",
      "Line 2: !Series_geo_accession\t\"GSE193442\"\n",
      "Line 3: !Series_status\t\"Public on Mar 08 2022\"\n",
      "Line 4: !Series_submission_date\t\"Jan 11 2022\"\n",
      "Line 5: !Series_last_update_date\t\"Apr 20 2022\"\n",
      "Line 6: !Series_pubmed_id\t\"35258337\"\n",
      "Line 7: !Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "Line 8: !Series_overall_design\t\"Refer to individual Series\"\n",
      "Line 9: !Series_type\t\"Expression profiling by high throughput sequencing\"\n",
      "Line 10: !Series_type\t\"Other\"\n",
      "Found table marker at line 69\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene data extraction stats:\n",
      "Number of rows: 0\n",
      "Number of columns: 4512\n",
      "No gene data rows found. This confirms this is a SuperSeries without direct gene expression data.\n",
      "\n",
      "Updated gene data availability: False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The SuperSeries nature of GSE193442 is causing issues with our standard data extraction\n",
    "# Let's try to check if we can find any SubSeries information\n",
    "\n",
    "import gzip\n",
    "import re\n",
    "\n",
    "def extract_subseries_info(soft_file_path):\n",
    "    \"\"\"Extract SubSeries information from a SuperSeries SOFT file\"\"\"\n",
    "    subseries_ids = []\n",
    "    \n",
    "    try:\n",
    "        with gzip.open(soft_file_path, 'rt') as f:\n",
    "            for line in f:\n",
    "                if line.startswith('!Series_relation'):\n",
    "                    # Look for SubSeries relation entries\n",
    "                    match = re.search(r'SubSeries of:(\\S+)', line)\n",
    "                    if match:\n",
    "                        subseries_ids.append(match.group(1))\n",
    "                    # Also check for \"SuperSeries of\" pattern which lists the component series\n",
    "                    match = re.search(r'SuperSeries of:(\\S+)', line)\n",
    "                    if match:\n",
    "                        subseries_ids.append(match.group(1))\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading SOFT file: {e}\")\n",
    "    \n",
    "    return subseries_ids\n",
    "\n",
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Check if we can find subseries information\n",
    "print(\"Checking for SubSeries information in the SuperSeries...\")\n",
    "subseries = extract_subseries_info(soft_file)\n",
    "print(f\"SubSeries found: {subseries}\")\n",
    "\n",
    "# 3. Try direct extraction method with additional debugging\n",
    "print(\"\\nAttempting direct extraction with debugging:\")\n",
    "try:\n",
    "    # Modified approach to print more information about the file\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        # Print first 10 lines to understand file structure\n",
    "        print(\"First 10 lines of the matrix file:\")\n",
    "        for i, line in enumerate(file):\n",
    "            if i < 10:\n",
    "                print(f\"Line {i+1}: {line.strip()}\")\n",
    "            if i == 0 and \"SuperSeries\" in line:\n",
    "                print(\"This confirms the file is a SuperSeries metadata file\")\n",
    "            if \"!series_matrix_table_begin\" in line:\n",
    "                print(f\"Found table marker at line {i+1}\")\n",
    "                break\n",
    "        else:\n",
    "            print(\"No table marker found in the file\")\n",
    "            \n",
    "    # Try standard extraction again but with error handling\n",
    "    try:\n",
    "        gene_data = get_genetic_data(matrix_file)\n",
    "        print(\"\\nGene data extraction stats:\")\n",
    "        print(f\"Number of rows: {gene_data.shape[0]}\")\n",
    "        print(f\"Number of columns: {gene_data.shape[1]}\")\n",
    "        \n",
    "        if gene_data.shape[0] > 0:\n",
    "            print(\"First 20 gene/probe identifiers:\")\n",
    "            print(gene_data.index[:20])\n",
    "        else:\n",
    "            print(\"No gene data rows found. This confirms this is a SuperSeries without direct gene expression data.\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error during gene data extraction: {e}\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error examining matrix file: {e}\")\n",
    "\n",
    "# 4. Update data availability flag based on our findings\n",
    "is_gene_available = False  # Updating based on our inspection\n",
    "print(f\"\\nUpdated gene data availability: {is_gene_available}\")\n",
    "\n",
    "# 5. Re-validate cohort info with updated gene availability\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",
    ")"
   ]
  }
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