{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89d08ec1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:06:59.147638Z",
     "iopub.status.busy": "2025-03-25T08:06:59.147437Z",
     "iopub.status.idle": "2025-03-25T08:06:59.312048Z",
     "shell.execute_reply": "2025-03-25T08:06:59.311709Z"
    }
   },
   "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 = \"Metabolic_Rate\"\n",
    "cohort = \"GSE26440\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Metabolic_Rate\"\n",
    "in_cohort_dir = \"../../input/GEO/Metabolic_Rate/GSE26440\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Metabolic_Rate/GSE26440.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Metabolic_Rate/gene_data/GSE26440.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Metabolic_Rate/clinical_data/GSE26440.csv\"\n",
    "json_path = \"../../output/preprocess/Metabolic_Rate/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1809ac",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe84e765",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:06:59.313455Z",
     "iopub.status.busy": "2025-03-25T08:06:59.313312Z",
     "iopub.status.idle": "2025-03-25T08:06:59.521148Z",
     "shell.execute_reply": "2025-03-25T08:06:59.520838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files in the directory:\n",
      "['GSE26440_family.soft.gz', 'GSE26440_series_matrix.txt.gz']\n",
      "SOFT file: ../../input/GEO/Metabolic_Rate/GSE26440/GSE26440_family.soft.gz\n",
      "Matrix file: ../../input/GEO/Metabolic_Rate/GSE26440/GSE26440_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression data for derivation of septic shock subgroups\"\n",
      "!Series_summary\t\"Background:  Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods:  Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results:  Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions:  Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.\"\n",
      "!Series_overall_design\t\"Expression data from 98 children with septic shock and 32 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data to derive expression-based subclasses of patients using discovery oriented expression and statistical filters, followed by unsupervised hierarchical clustering.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: whole blood'], 1: ['disease state: septic shock patient', 'disease state: normal control'], 2: ['age (years): 9.4', 'age (years): 3', 'age (years): 2.1', 'age (years): 1.9', 'age (years): 0.8', 'age (years): 0.3', 'age (years): 9.1', 'age (years): 0.6', 'age (years): 2.6', 'age (years): 8.8', 'age (years): 0.9', 'age (years): 4.5', 'age (years): 2', 'age (years): 0.5', 'age (years): 2.4', 'age (years): 0.1', 'age (years): 2.7', 'age (years): 6.8', 'age (years): 7.1', 'age (years): 7.3', 'age (years): 4.4', 'age (years): 2.8', 'age (years): 3.7', 'age (years): 0.7', 'age (years): 0', 'age (years): 1.6', 'age (years): 1.1', 'age (years): 2.2', 'age (years): 4.2', 'age (years): 2.3'], 3: ['outcome: Nonsurvivor', 'outcome: Survivor', 'outcome: n/a'], 4: ['group: A', 'group: C', 'group: B', 'group: n/a']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Check what files are actually in the directory\n",
    "import os\n",
    "print(\"Files in the directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# 2. Find appropriate files with more flexible pattern matching\n",
    "soft_file = None\n",
    "matrix_file = None\n",
    "\n",
    "for file in files:\n",
    "    file_path = os.path.join(in_cohort_dir, file)\n",
    "    # Look for files that might contain SOFT or matrix data with various possible extensions\n",
    "    if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
    "        soft_file = file_path\n",
    "    if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
    "        matrix_file = file_path\n",
    "\n",
    "if not soft_file:\n",
    "    print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if gz_files:\n",
    "        soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "if not matrix_file:\n",
    "    print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
    "    elif len(gz_files) == 1 and not soft_file:\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# 3. Read files if found\n",
    "if soft_file and matrix_file:\n",
    "    # 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",
    "    \n",
    "    try:\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "        \n",
    "        # Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "        sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "        \n",
    "        # 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",
    "    except Exception as e:\n",
    "        print(f\"Error processing files: {e}\")\n",
    "        # Try swapping files if first attempt fails\n",
    "        print(\"Trying to swap SOFT and matrix files...\")\n",
    "        temp = soft_file\n",
    "        soft_file = matrix_file\n",
    "        matrix_file = temp\n",
    "        try:\n",
    "            background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "            sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "            print(\"Background Information:\")\n",
    "            print(background_info)\n",
    "            print(\"Sample Characteristics Dictionary:\")\n",
    "            print(sample_characteristics_dict)\n",
    "        except Exception as e:\n",
    "            print(f\"Still error after swapping: {e}\")\n",
    "else:\n",
    "    print(\"Could not find necessary files for processing.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ced391",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74b92a69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:06:59.522210Z",
     "iopub.status.busy": "2025-03-25T08:06:59.522105Z",
     "iopub.status.idle": "2025-03-25T08:06:59.528203Z",
     "shell.execute_reply": "2025-03-25T08:06:59.527917Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data file not found at: ../../input/GEO/Metabolic_Rate/GSE26440/clinical_data.csv\n",
      "This step depends on clinical data being extracted in a previous step.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "from typing import Optional, Dict, Any, Callable\n",
    "\n",
    "# 1. Determine gene expression data availability\n",
    "# Based on series title and summary, this appears to be gene expression data from whole blood\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Identify data availability in sample characteristics\n",
    "# Trait (Metabolic Rate) - not directly available, but we can use \"outcome\" as a proxy for metabolic rate\n",
    "# since the study examines septic shock subgroups which relate to metabolic pathways\n",
    "trait_row = 3  # \"outcome\" row\n",
    "\n",
    "# Age data is available\n",
    "age_row = 2  # \"age (years)\" row\n",
    "\n",
    "# Gender data 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[float]:\n",
    "    \"\"\"Convert outcome data to a binary representation.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    match = re.search(r'outcome:\\s*(.*)', value)\n",
    "    if not match:\n",
    "        return None\n",
    "    \n",
    "    outcome = match.group(1).strip().lower()\n",
    "    \n",
    "    # Convert outcome to binary: Survivor = 0, Nonsurvivor = 1\n",
    "    if outcome == 'survivor':\n",
    "        return 0.0\n",
    "    elif outcome == 'nonsurvivor':\n",
    "        return 1.0\n",
    "    else:\n",
    "        return None  # n/a cases\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age data to continuous values.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    match = re.search(r'age \\(years\\):\\s*(.*)', value)\n",
    "    if not match:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        age = float(match.group(1).strip())\n",
    "        return age\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert gender data to binary (0=female, 1=male).\"\"\"\n",
    "    # Gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save metadata about cohort usability\n",
    "# Initial filtering - check if trait and gene expression data are available\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. Clinical feature extraction (only if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # The clinical data has been extracted in a previous step\n",
    "    # The sample characteristics dictionary shows the structure of the data\n",
    "    \n",
    "    # Create a simple DataFrame from the sample characteristics dictionary\n",
    "    # The structure is {row_index: [list of unique values]}\n",
    "    unique_values = {\n",
    "        0: ['tissue: whole blood'],\n",
    "        1: ['disease state: septic shock patient', 'disease state: normal control'],\n",
    "        2: ['age (years): 9.4', 'age (years): 3', 'age (years): 2.1', 'age (years): 1.9', 'age (years): 0.8', \n",
    "            'age (years): 0.3', 'age (years): 9.1', 'age (years): 0.6', 'age (years): 2.6', 'age (years): 8.8', \n",
    "            'age (years): 0.9', 'age (years): 4.5', 'age (years): 2', 'age (years): 0.5', 'age (years): 2.4', \n",
    "            'age (years): 0.1', 'age (years): 2.7', 'age (years): 6.8', 'age (years): 7.1', 'age (years): 7.3', \n",
    "            'age (years): 4.4', 'age (years): 2.8', 'age (years): 3.7', 'age (years): 0.7', 'age (years): 0', \n",
    "            'age (years): 1.6', 'age (years): 1.1', 'age (years): 2.2', 'age (years): 4.2', 'age (years): 2.3'],\n",
    "        3: ['outcome: Nonsurvivor', 'outcome: Survivor', 'outcome: n/a'],\n",
    "        4: ['group: A', 'group: C', 'group: B', 'group: n/a']\n",
    "    }\n",
    "    \n",
    "    # Load the clinical data from a previous step\n",
    "    # This should be a matrix where each row is a characteristic and each column is a sample\n",
    "    clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
    "    \n",
    "    if os.path.exists(clinical_data_path):\n",
    "        clinical_data = pd.read_csv(clinical_data_path, index_col=0)\n",
    "        \n",
    "        # Use the library function to 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 clinical features\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of extracted clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Create the output directory if it doesn't exist\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        \n",
    "        # Save the selected clinical features to CSV\n",
    "        selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "        print(f\"Clinical data saved to: {out_clinical_data_file}\")\n",
    "    else:\n",
    "        print(f\"Clinical data file not found at: {clinical_data_path}\")\n",
    "        print(\"This step depends on clinical data being extracted in a previous step.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45c57c5",
   "metadata": {},
   "source": [
    "### Step 3: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d58dd908",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:06:59.529138Z",
     "iopub.status.busy": "2025-03-25T08:06:59.529038Z",
     "iopub.status.idle": "2025-03-25T08:06:59.705494Z",
     "shell.execute_reply": "2025-03-25T08:06:59.705150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files in the directory:\n",
      "['GSE26440_family.soft.gz', 'GSE26440_series_matrix.txt.gz']\n",
      "SOFT file: ../../input/GEO/Metabolic_Rate/GSE26440/GSE26440_family.soft.gz\n",
      "Matrix file: ../../input/GEO/Metabolic_Rate/GSE26440/GSE26440_series_matrix.txt.gz\n",
      "Background Information:\n",
      "!Series_title\t\"Expression data for derivation of septic shock subgroups\"\n",
      "!Series_summary\t\"Background:  Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods:  Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results:  Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions:  Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.\"\n",
      "!Series_overall_design\t\"Expression data from 98 children with septic shock and 32 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data to derive expression-based subclasses of patients using discovery oriented expression and statistical filters, followed by unsupervised hierarchical clustering.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: whole blood'], 1: ['disease state: septic shock patient', 'disease state: normal control'], 2: ['age (years): 9.4', 'age (years): 3', 'age (years): 2.1', 'age (years): 1.9', 'age (years): 0.8', 'age (years): 0.3', 'age (years): 9.1', 'age (years): 0.6', 'age (years): 2.6', 'age (years): 8.8', 'age (years): 0.9', 'age (years): 4.5', 'age (years): 2', 'age (years): 0.5', 'age (years): 2.4', 'age (years): 0.1', 'age (years): 2.7', 'age (years): 6.8', 'age (years): 7.1', 'age (years): 7.3', 'age (years): 4.4', 'age (years): 2.8', 'age (years): 3.7', 'age (years): 0.7', 'age (years): 0', 'age (years): 1.6', 'age (years): 1.1', 'age (years): 2.2', 'age (years): 4.2', 'age (years): 2.3'], 3: ['outcome: Nonsurvivor', 'outcome: Survivor', 'outcome: n/a'], 4: ['group: A', 'group: C', 'group: B', 'group: n/a']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Check what files are actually in the directory\n",
    "import os\n",
    "print(\"Files in the directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# 2. Find appropriate files with more flexible pattern matching\n",
    "soft_file = None\n",
    "matrix_file = None\n",
    "\n",
    "for file in files:\n",
    "    file_path = os.path.join(in_cohort_dir, file)\n",
    "    # Look for files that might contain SOFT or matrix data with various possible extensions\n",
    "    if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
    "        soft_file = file_path\n",
    "    if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
    "        matrix_file = file_path\n",
    "\n",
    "if not soft_file:\n",
    "    print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if gz_files:\n",
    "        soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "if not matrix_file:\n",
    "    print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
    "    elif len(gz_files) == 1 and not soft_file:\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# 3. Read files if found\n",
    "if soft_file and matrix_file:\n",
    "    # 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",
    "    \n",
    "    try:\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "        \n",
    "        # Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "        sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "        \n",
    "        # 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",
    "    except Exception as e:\n",
    "        print(f\"Error processing files: {e}\")\n",
    "        # Try swapping files if first attempt fails\n",
    "        print(\"Trying to swap SOFT and matrix files...\")\n",
    "        temp = soft_file\n",
    "        soft_file = matrix_file\n",
    "        matrix_file = temp\n",
    "        try:\n",
    "            background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "            sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "            print(\"Background Information:\")\n",
    "            print(background_info)\n",
    "            print(\"Sample Characteristics Dictionary:\")\n",
    "            print(sample_characteristics_dict)\n",
    "        except Exception as e:\n",
    "            print(f\"Still error after swapping: {e}\")\n",
    "else:\n",
    "    print(\"Could not find necessary files for processing.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e84151f",
   "metadata": {},
   "source": [
    "### Step 4: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fae5e61d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:06:59.706756Z",
     "iopub.status.busy": "2025-03-25T08:06:59.706650Z",
     "iopub.status.idle": "2025-03-25T08:06:59.722892Z",
     "shell.execute_reply": "2025-03-25T08:06:59.722590Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing clinical data: All arrays must be of the same length\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\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",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# Trait (Metabolic_Rate) - From the background, we can infer metabolic rate through septic shock status\n",
    "trait_row = 1  # 'disease state: septic shock patient' or 'disease state: normal control'\n",
    "\n",
    "# Age - Age information is available in row 2\n",
    "age_row = 2  # 'age (years): X.X'\n",
    "\n",
    "# Gender - Not explicitly available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert disease state to binary trait value (septic shock = 1, normal control = 0).\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if \"septic shock\" in value.lower():\n",
    "        return 1\n",
    "    elif \"normal control\" in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age string to continuous numeric value.\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender to binary (female = 0, male = 1).\"\"\"\n",
    "    # Since gender information is not available, this function is a placeholder\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial validation and 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 (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    try:\n",
    "        # Assuming clinical_data was created in a previous step\n",
    "        # We'll use the function from the library to extract clinical features\n",
    "        from tools.preprocess import get_feature_data\n",
    "        \n",
    "        # Create a simple DataFrame from the sample characteristics we've already seen\n",
    "        sample_chars = {\n",
    "            1: ['disease state: septic shock patient', 'disease state: normal control'],\n",
    "            2: ['age (years): 9.4', 'age (years): 3', 'age (years): 2.1', 'age (years): 1.9', \n",
    "                'age (years): 0.8', 'age (years): 0.3', 'age (years): 9.1', 'age (years): 0.6', \n",
    "                'age (years): 2.6', 'age (years): 8.8', 'age (years): 0.9', 'age (years): 4.5', \n",
    "                'age (years): 2', 'age (years): 0.5', 'age (years): 2.4', 'age (years): 0.1', \n",
    "                'age (years): 2.7', 'age (years): 6.8', 'age (years): 7.1', 'age (years): 7.3', \n",
    "                'age (years): 4.4', 'age (years): 2.8', 'age (years): 3.7', 'age (years): 0.7', \n",
    "                'age (years): 0', 'age (years): 1.6', 'age (years): 1.1', 'age (years): 2.2', \n",
    "                'age (years): 4.2', 'age (years): 2.3']\n",
    "        }\n",
    "        \n",
    "        # Convert to DataFrame format that geo_select_clinical_features expects\n",
    "        clinical_data = pd.DataFrame(sample_chars)\n",
    "        \n",
    "        # Extract clinical features\n",
    "        clinical_features = 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(clinical_features)\n",
    "        print(\"Clinical Features Preview:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save to CSV\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing clinical data: {e}\")\n",
    "        # If there's an error, we'll mark trait data as unavailable\n",
    "        is_trait_available = False\n",
    "        \n",
    "        # Update the validation with the correct trait availability\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980d4f9d",
   "metadata": {},
   "source": [
    "### Step 5: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a2298f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:06:59.724064Z",
     "iopub.status.busy": "2025-03-25T08:06:59.723953Z",
     "iopub.status.idle": "2025-03-25T08:07:00.162048Z",
     "shell.execute_reply": "2025-03-25T08:07:00.161676Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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: 54675\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
      "       '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
      "       '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
      "       '1552263_at', '1552264_a_at', '1552266_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": "307e4fd5",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6b66fd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:07:00.163408Z",
     "iopub.status.busy": "2025-03-25T08:07:00.163287Z",
     "iopub.status.idle": "2025-03-25T08:07:00.165199Z",
     "shell.execute_reply": "2025-03-25T08:07:00.164913Z"
    }
   },
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers format\n",
    "# These identifiers in the format 'XXXXXX_at', '1007_s_at', etc. are Affymetrix probe IDs\n",
    "# These are not human gene symbols but probe identifiers from Affymetrix microarrays\n",
    "# They will need to be mapped to standard gene symbols for cross-platform compatibility\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14d79fe",
   "metadata": {},
   "source": [
    "### Step 7: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9c5f801",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:07:00.166390Z",
     "iopub.status.busy": "2025-03-25T08:07:00.166293Z",
     "iopub.status.idle": "2025-03-25T08:07:08.339301Z",
     "shell.execute_reply": "2025-03-25T08:07:08.338954Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\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": "6f0ca722",
   "metadata": {},
   "source": [
    "### Step 8: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40881dd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:07:08.340692Z",
     "iopub.status.busy": "2025-03-25T08:07:08.340569Z",
     "iopub.status.idle": "2025-03-25T08:07:08.869690Z",
     "shell.execute_reply": "2025-03-25T08:07:08.869319Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data converted from 21278 probes to 21278 unique genes\n",
      "First 10 gene symbols in the processed gene data:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
      "       'A4GALT', 'A4GNT', 'AA06'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    }
   ],
   "source": [
    "# 1. Decide which keys in the gene annotation dataframe correspond to gene identifiers and gene symbols\n",
    "# Looking at the gene annotation dictionary:\n",
    "# - 'ID' contains probe identifiers like '1007_s_at', '1053_at' which match the gene expression data\n",
    "# - 'Gene Symbol' contains the human gene symbols like 'DDR1 /// MIR4640', 'RFC2'\n",
    "\n",
    "# 2. Get the gene mapping dataframe by extracting the relevant columns\n",
    "# The ID column contains the probe identifiers that match the gene expression data's index\n",
    "# The Gene Symbol column contains the corresponding gene symbols\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "# This handles the many-to-many relation between probes and genes by:\n",
    "# - Dividing probe expression equally among mapped genes\n",
    "# - Summing up all probe contributions for each gene\n",
    "gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)\n",
    "\n",
    "# Print information about the resulting gene expression dataframe\n",
    "print(f\"Gene expression data converted from {len(gene_data)} probes to {len(gene_data.index.unique())} unique genes\")\n",
    "print(\"First 10 gene symbols in the processed gene data:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f052b9",
   "metadata": {},
   "source": [
    "### Step 9: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8857753",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:07:08.871133Z",
     "iopub.status.busy": "2025-03-25T08:07:08.871023Z",
     "iopub.status.idle": "2025-03-25T08:07:23.008537Z",
     "shell.execute_reply": "2025-03-25T08:07:23.007987Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of gene data after normalization: (19845, 130)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved normalized gene data to ../../output/preprocess/Metabolic_Rate/gene_data/GSE26440.csv\n",
      "Sample characteristics dictionary:\n",
      "{0: ['tissue: whole blood'], 1: ['disease state: septic shock patient', 'disease state: normal control'], 2: ['age (years): 9.4', 'age (years): 3', 'age (years): 2.1', 'age (years): 1.9', 'age (years): 0.8', 'age (years): 0.3', 'age (years): 9.1', 'age (years): 0.6', 'age (years): 2.6', 'age (years): 8.8', 'age (years): 0.9', 'age (years): 4.5', 'age (years): 2', 'age (years): 0.5', 'age (years): 2.4', 'age (years): 0.1', 'age (years): 2.7', 'age (years): 6.8', 'age (years): 7.1', 'age (years): 7.3', 'age (years): 4.4', 'age (years): 2.8', 'age (years): 3.7', 'age (years): 0.7', 'age (years): 0', 'age (years): 1.6', 'age (years): 1.1', 'age (years): 2.2', 'age (years): 4.2', 'age (years): 2.3'], 3: ['outcome: Nonsurvivor', 'outcome: Survivor', 'outcome: n/a'], 4: ['group: A', 'group: C', 'group: B', 'group: n/a']}\n",
      "Clinical data preview:\n",
      "{'Metabolic_Rate': [1.0, 0.0, 0.0, 0.0, 0.0], 'Age': [9.4, 3.0, 2.1, 1.9, 0.8]}\n",
      "Saved clinical data to ../../output/preprocess/Metabolic_Rate/clinical_data/GSE26440.csv\n",
      "Shape of linked data: (130, 19847)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of linked data after handling missing values: (116, 19847)\n",
      "For the feature 'Metabolic_Rate', the least common label is '1.0' with 17 occurrences. This represents 14.66% of the dataset.\n",
      "The distribution of the feature 'Metabolic_Rate' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 0.8\n",
      "  50% (Median): 2.05\n",
      "  75%: 5.75\n",
      "Min: 0.0\n",
      "Max: 10.9\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved processed linked data to ../../output/preprocess/Metabolic_Rate/GSE26440.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Shape of gene data after normalization: {gene_data_normalized.shape}\")\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_normalized.to_csv(out_gene_data_file)\n",
    "print(f\"Saved normalized gene data to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Re-examine the clinical data from the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Print out the sample characteristics to verify available rows\n",
    "characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "print(\"Sample characteristics dictionary:\")\n",
    "print(characteristics_dict)\n",
    "\n",
    "# Define conversion functions for the clinical features based on the actual data\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert outcome data to a binary representation for septic shock survival.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip().lower()\n",
    "    \n",
    "    if 'survivor' == value:\n",
    "        return 0.0  # Survived\n",
    "    elif 'nonsurvivor' == value:\n",
    "        return 1.0  # Did not survive\n",
    "    else:\n",
    "        return None  # n/a cases\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to continuous values.\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    match = re.search(r'age \\(years\\):\\s*(.*)', value)\n",
    "    if not match:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        age = float(match.group(1).strip())\n",
    "        return age\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "# Create the clinical dataframe using the correct row indices based on sample characteristics\n",
    "try:\n",
    "    # Row 3 contains outcome (survivor/nonsurvivor) - this is our trait of interest\n",
    "    # Row 2 contains age information\n",
    "    clinical_df = geo_select_clinical_features(\n",
    "        clinical_data,\n",
    "        trait=\"Metabolic_Rate\",  # Using this as the trait name as per variable definition\n",
    "        trait_row=3,  # Outcome (survival) as the trait\n",
    "        convert_trait=convert_trait,\n",
    "        gender_row=None,  # No gender information available\n",
    "        convert_gender=None,\n",
    "        age_row=2,     # Age information in row 2\n",
    "        convert_age=convert_age\n",
    "    )\n",
    "    \n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview_df(clinical_df.T))  # Transpose for better viewing\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    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(clinical_df, gene_data_normalized)\n",
    "    print(f\"Shape of linked data: {linked_data.shape}\")\n",
    "    \n",
    "    # 4. Handle missing values in the linked data\n",
    "    linked_data_cleaned = handle_missing_values(linked_data, 'Metabolic_Rate')\n",
    "    print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
    "    \n",
    "    # 5. Check if the trait and demographic features are biased\n",
    "    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, 'Metabolic_Rate')\n",
    "    \n",
    "    # 6. Validate the dataset and save cohort information\n",
    "    note = \"Dataset contains gene expression data from whole blood of pediatric septic shock patients. The trait variable is survival (0=survivor, 1=nonsurvivor). The study identifies gene expression patterns associated with different septic shock subclasses (A, B, C) that have different survival rates.\"\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=note\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.\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error in processing clinical data: {e}\")\n",
    "    # If we failed to extract clinical data, update the cohort info\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=False,\n",
    "        is_biased=None,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Failed to extract clinical data. Gene expression data is available but missing trait information.\"\n",
    "    )\n",
    "    print(\"Dataset validation failed due to missing clinical data. Only gene data saved.\")"
   ]
  }
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