{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac2d1d5e",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T08:31:11.392890Z"
    }
   },
   "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 = \"COVID-19\"\n",
    "cohort = \"GSE213313\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/COVID-19\"\n",
    "in_cohort_dir = \"../../input/GEO/COVID-19/GSE213313\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/COVID-19/GSE213313.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE213313.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE213313.csv\"\n",
    "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d885f6",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c17b6736",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:11.394731Z",
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     "iopub.status.idle": "2025-03-25T08:31:11.548047Z",
     "shell.execute_reply": "2025-03-25T08:31:11.547665Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Serial whole blood transcriptomic analysis demonstrates activation of neutrophils, platelets and coagulation in severe and critical COVID-19 – submitted\"\n",
      "!Series_summary\t\"Introduction: A maladaptive inflammatory response has been implicated in the pathogenesis of severe and critical COVID-19. This study aimed to characterize the temporal dynamics of this response and investigate whether critical disease is associated with distinct gene expression patterns.\"\n",
      "!Series_summary\t\"Methods: We performed microarray analysis of serial whole blood RNA samples from 19 patients with critical COVID-19, 15 patients with severe but non-critical disease and 11 healthy controls. We assessed whole blood gene expression patterns by differential gene expression analysis, gene set enrichment, two clustering methods and estimated relative leukocyte abundance using CIBERSORT.\"\n",
      "!Series_summary\t\"Results: Neutrophils, platelets, cytokine signaling, and the coagulation system were activated in COVID-19, and more pronounced in critical vs. non-critical disease. We observed two different trajectories of neutrophil-associated genes, indicating the emergence of a more immature neutrophil phenotype over time. Interferon-associated genes were strongly enriched in early COVID-19 before falling markedly, with modest severity-associated differences in trajectory.\"\n",
      "!Series_summary\t\"Conclusions: Severe COVID-19 is associated with a broad inflammatory response, which is more pronounced in critical disease. Our data suggest a progressively more immature circulating neutrophil phenotype over time. Interferon signaling is enriched in COVID-19 but does not seem to drive critical disease.\"\n",
      "!Series_overall_design\t\"Between March and May 2020, 135 patients admitted to Akershus University Hospital with COVID-19 confirmed by SARS-CoV-2 RT-PCR were prospectively recruited to the Coronavirus Disease Mechanisms (COVID MECH) observational cohort study. Thirty-six patients (27%) were admitted to the ICU and 8 (6%) died. Inclusion predated the use of corticosteroids in severe COVID-19. This substudy included 19 patients with critical disease, defined as requiring invasive mechanical ventilation, and 15 patients with non-critical disease receiving supplemental O2. Patients were selected based on the availability of sequential whole blood RNA samples, and time from symptom onset to baseline sampling between five and 15 days. RNA samples from 11 healthy volunteers matched to patients by age and gender served as controls.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['individual: patient 018', 'individual: patient 053', 'individual: patient 063', 'individual: patient 089', 'individual: patient 115', 'individual: patient 130', 'individual: patient 141', 'individual: patient F014', 'individual: patient 117', 'individual: patient 051', 'individual: patient F016', 'individual: patient 066', 'individual: patient 135', 'individual: patient 062', 'individual: patient 002', 'individual: patient 050', 'individual: patient 061', 'individual: patient 087', 'individual: patient 129', 'individual: patient 138', 'individual: patient F011', 'individual: patient F013', 'individual: patient 086', 'individual: patient 113', 'individual: patient F009', 'individual: patient 022', 'individual: patient 057', 'individual: patient 096', 'individual: patient 091', 'individual: patient F002'], 1: ['disease state: COVID-19', 'disease state: Healthy'], 2: ['severity: Critical', 'severity: Non-critical', 'severity: Healthy'], 3: ['time: T1', 'time: T2', 'time: T3', 'time: T0'], 4: ['tissue: whole blood']}\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": "8d9140fd",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db007bc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:11.549298Z",
     "iopub.status.busy": "2025-03-25T08:31:11.549183Z",
     "iopub.status.idle": "2025-03-25T08:31:11.553812Z",
     "shell.execute_reply": "2025-03-25T08:31:11.553508Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import json\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this is a microarray analysis of whole blood RNA samples\n",
    "# which indicates gene expression data should be available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# For trait (COVID-19 severity):\n",
    "# From the sample characteristics dictionary, key 2 contains severity information\n",
    "trait_row = 2\n",
    "\n",
    "# For age:\n",
    "# There is no age information in the sample characteristics dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender:\n",
    "# There is no gender information in the sample characteristics dictionary\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "# Function to convert trait values (severity)\n",
    "def convert_trait(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert severity to binary (0 for Non-critical/Healthy, 1 for Critical)\n",
    "    if 'critical' in value.lower():\n",
    "        return 1\n",
    "    elif 'non-critical' in value.lower() or 'healthy' in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Function to convert age (not available in this dataset)\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "# Function to convert gender (not available in this dataset)\n",
    "def convert_gender(value):\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Conduct initial filtering on 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",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data first (assuming it's available from previous steps)\n",
    "    if os.path.exists(os.path.join(in_cohort_dir, \"clinical_data.csv\")):\n",
    "        clinical_data = pd.read_csv(os.path.join(in_cohort_dir, \"clinical_data.csv\"))\n",
    "        \n",
    "        # Extract clinical features using the provided function\n",
    "        selected_clinical = 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 selected clinical data\n",
    "        preview = preview_df(selected_clinical)\n",
    "        print(\"Preview of selected clinical data:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save the selected clinical data to the specified output file\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        selected_clinical.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3338e91",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4eec8559",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:11.554978Z",
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     "iopub.status.idle": "2025-03-25T08:31:11.828902Z",
     "shell.execute_reply": "2025-03-25T08:31:11.828504Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOFT file: ../../input/GEO/COVID-19/GSE213313/GSE213313_family.soft.gz\n",
      "Matrix file: ../../input/GEO/COVID-19/GSE213313/GSE213313_series_matrix.txt.gz\n",
      "Found the matrix table marker at line 66\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (25469, 94)\n",
      "First 20 gene/probe identifiers:\n",
      "['A_19_P00315452', 'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315506', 'A_19_P00315529', 'A_19_P00315543', 'A_19_P00315551', 'A_19_P00315581', 'A_19_P00315584', 'A_19_P00315593', 'A_19_P00315603', 'A_19_P00315649', 'A_19_P00315668', 'A_19_P00315716', 'A_19_P00315753', 'A_19_P00315764', 'A_19_P00315780', 'A_19_P00315810', 'A_19_P00315824', 'A_19_P00315843']\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# Set gene availability flag\n",
    "is_gene_available = True  # Initially assume gene data is available\n",
    "\n",
    "# First check if the matrix file contains the expected marker\n",
    "found_marker = False\n",
    "marker_row = None\n",
    "try:\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        for i, line in enumerate(file):\n",
    "            if \"!series_matrix_table_begin\" in line:\n",
    "                found_marker = True\n",
    "                marker_row = i\n",
    "                print(f\"Found the matrix table marker at line {i}\")\n",
    "                break\n",
    "    \n",
    "    if not found_marker:\n",
    "        print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
    "        is_gene_available = False\n",
    "        \n",
    "    # If marker was found, try to extract gene data\n",
    "    if is_gene_available:\n",
    "        try:\n",
    "            # Try using the library function\n",
    "            gene_data = get_genetic_data(matrix_file)\n",
    "            \n",
    "            if gene_data.shape[0] == 0:\n",
    "                print(\"Warning: Extracted gene data has 0 rows.\")\n",
    "                is_gene_available = False\n",
    "            else:\n",
    "                print(f\"Gene data shape: {gene_data.shape}\")\n",
    "                # Print the first 20 gene/probe identifiers\n",
    "                print(\"First 20 gene/probe identifiers:\")\n",
    "                print(gene_data.index[:20].tolist())\n",
    "        except Exception as e:\n",
    "            print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n",
    "            is_gene_available = False\n",
    "    \n",
    "    # If gene data extraction failed, examine file content to diagnose\n",
    "    if not is_gene_available:\n",
    "        print(\"Examining file content to diagnose the issue:\")\n",
    "        try:\n",
    "            with gzip.open(matrix_file, 'rt') as file:\n",
    "                # Print lines around the marker if found\n",
    "                if marker_row is not None:\n",
    "                    for i, line in enumerate(file):\n",
    "                        if i >= marker_row - 2 and i <= marker_row + 10:\n",
    "                            print(f\"Line {i}: {line.strip()[:100]}...\")\n",
    "                        if i > marker_row + 10:\n",
    "                            break\n",
    "                else:\n",
    "                    # If marker not found, print first 10 lines\n",
    "                    for i, line in enumerate(file):\n",
    "                        if i < 10:\n",
    "                            print(f\"Line {i}: {line.strip()[:100]}...\")\n",
    "                        else:\n",
    "                            break\n",
    "        except Exception as e2:\n",
    "            print(f\"Error examining file: {e2}\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error processing file: {e}\")\n",
    "    is_gene_available = False\n",
    "\n",
    "# Update validation information if gene data extraction failed\n",
    "if not is_gene_available:\n",
    "    print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
    "    # Update the validation record since gene data isn't available\n",
    "    is_trait_available = False  # We already determined trait data isn't available in step 2\n",
    "    validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
    "                                 is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d14ffdc",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab06f05b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:11.830252Z",
     "iopub.status.busy": "2025-03-25T08:31:11.830128Z",
     "iopub.status.idle": "2025-03-25T08:31:11.832075Z",
     "shell.execute_reply": "2025-03-25T08:31:11.831770Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers (A_19_P...) are Agilent microarray probe IDs, not human gene symbols\n",
    "# They need to be mapped to official gene symbols for downstream analysis\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de4cadf",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed052b4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:11.833236Z",
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     "shell.execute_reply": "2025-03-25T08:31:15.716022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'LOCUSLINK_ID', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE', 'SPOT_ID']\n",
      "{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE'], 'REFSEQ': [nan, nan, nan], 'GB_ACC': [nan, nan, nan], 'LOCUSLINK_ID': [nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan], 'GENE_NAME': [nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, 'unmapped'], 'CYTOBAND': [nan, nan, nan], 'DESCRIPTION': [nan, nan, nan], 'GO_ID': [nan, nan, nan], 'SEQUENCE': [nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386']}\n",
      "\n",
      "Examining gene mapping columns:\n",
      "Column 'ID' examples:\n",
      "Example 1: GE_BrightCorner\n",
      "Example 2: DarkCorner\n",
      "Example 3: A_21_P0014386\n",
      "Example 4: A_33_P3396872\n",
      "Example 5: A_33_P3267760\n",
      "\n",
      "Column 'GENE_SYMBOL' examples:\n",
      "Example 1: nan\n",
      "Example 2: nan\n",
      "Example 3: nan\n",
      "Example 4: CPED1\n",
      "Example 5: BCOR\n",
      "\n",
      "Gene symbol column completeness: 48862/2452521 rows (1.99%)\n",
      "\n",
      "Columns identified for gene mapping:\n",
      "- 'ID': Contains probe IDs\n",
      "- 'GENE_SYMBOL': Contains gene symbols for mapping\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=3))\n",
    "\n",
    "# Examine the GENE_SYMBOL column which contains gene symbol information\n",
    "print(\"\\nExamining gene mapping columns:\")\n",
    "print(\"Column 'ID' examples:\")\n",
    "id_samples = gene_annotation['ID'].head(5).tolist()\n",
    "for i, sample in enumerate(id_samples):\n",
    "    print(f\"Example {i+1}: {sample}\")\n",
    "\n",
    "print(\"\\nColumn 'GENE_SYMBOL' examples:\")\n",
    "if 'GENE_SYMBOL' in gene_annotation.columns:\n",
    "    # Display a few examples of the GENE_SYMBOL column\n",
    "    symbol_samples = gene_annotation['GENE_SYMBOL'].head(5).tolist()\n",
    "    for i, sample in enumerate(symbol_samples):\n",
    "        print(f\"Example {i+1}: {sample}\")\n",
    "    \n",
    "    # Check the quality and completeness of the GENE_SYMBOL column\n",
    "    non_null_symbols = gene_annotation['GENE_SYMBOL'].notna().sum()\n",
    "    total_rows = len(gene_annotation)\n",
    "    print(f\"\\nGene symbol column completeness: {non_null_symbols}/{total_rows} rows ({non_null_symbols/total_rows:.2%})\")\n",
    "    \n",
    "    # Identify the columns needed for gene mapping\n",
    "    print(\"\\nColumns identified for gene mapping:\")\n",
    "    print(\"- 'ID': Contains probe IDs\")\n",
    "    print(\"- 'GENE_SYMBOL': Contains gene symbols for mapping\")\n",
    "else:\n",
    "    print(\"Error: 'GENE_SYMBOL' column not found in annotation data.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af076b8f",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62dbfb65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:15.717762Z",
     "iopub.status.busy": "2025-03-25T08:31:15.717628Z",
     "iopub.status.idle": "2025-03-25T08:31:16.864550Z",
     "shell.execute_reply": "2025-03-25T08:31:16.864104Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping shape: (48862, 2)\n",
      "Gene mapping preview:\n",
      "{'ID': ['A_33_P3396872', 'A_33_P3267760', 'A_32_P194264', 'A_23_P153745', 'A_21_P0014180'], 'Gene': ['CPED1', 'BCOR', 'CHAC2', 'IFI30', 'GPR146']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data shape after mapping: (17305, 94)\n",
      "First few gene symbols after mapping:\n",
      "['A1BG', 'A1BG-AS1', 'A2M-AS1', 'A4GALT', 'AAAS', 'AAAS-1', 'AACS', 'AADACL3', 'AADACP1', 'AAED1']\n",
      "Gene expression data shape after normalization: (14593, 94)\n",
      "First few normalized gene symbols:\n",
      "['A1BG', 'A1BG-AS1', 'A2M-AS1', 'A4GALT', 'AAAS', 'AACS', 'AADACL3', 'AADACP1', 'AAGAB', 'AAK1']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/COVID-19/gene_data/GSE213313.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify which columns to use for mapping\n",
    "# From the gene expression data output, we have IDs like A_19_P00315452\n",
    "# From the gene annotation data, we see the 'ID' column contains similar identifiers\n",
    "# and 'GENE_SYMBOL' contains the human gene symbols we need to map to\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the required columns\n",
    "prob_col = 'ID'\n",
    "gene_col = 'GENE_SYMBOL'\n",
    "\n",
    "# Extract the mapping between probe IDs and gene symbols\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping, n=5))\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression values\n",
    "# Apply the gene mapping to convert from probe IDs to gene symbols\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols after mapping:\")\n",
    "print(list(gene_data.index[:10]))\n",
    "\n",
    "# Normalize gene symbols to handle synonyms\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n",
    "print(\"First few normalized gene symbols:\")\n",
    "print(list(gene_data.index[:10]))\n",
    "\n",
    "# Save gene data to the specified output file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49061ebc",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89299b2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:16.865937Z",
     "iopub.status.busy": "2025-03-25T08:31:16.865802Z",
     "iopub.status.idle": "2025-03-25T08:31:25.439581Z",
     "shell.execute_reply": "2025-03-25T08:31:25.438944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape after normalization: (14593, 94)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE213313.csv\n",
      "Clinical features saved to ../../output/preprocess/COVID-19/clinical_data/GSE213313.csv\n",
      "Clinical features preview:\n",
      "{'COVID-19': [1.0, 1.0, 1.0, 1.0, 1.0]}\n",
      "Linked data shape: (94, 14594)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (94, 14594)\n",
      "For the feature 'COVID-19', the least common label is '0.0' with 11 occurrences. This represents 11.70% of the dataset.\n",
      "The distribution of the feature 'COVID-19' in this dataset is fine.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/COVID-19/GSE213313.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Create output directory if it doesn't exist\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "\n",
    "# Save the normalized gene data\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Extract clinical features using the previously identified feature rows\n",
    "# Use the clinical data from Step 1 and the row identifiers from Step 2\n",
    "clinical_features = geo_select_clinical_features(\n",
    "    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",
    "# Create directory for clinical data output\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "\n",
    "# Save the clinical features\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Preview the clinical features\n",
    "clinical_features_preview = preview_df(clinical_features.T)\n",
    "print(\"Clinical features preview:\")\n",
    "print(clinical_features_preview)\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "print(f\"Linked data shape: {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\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine if trait and demographic features are biased\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Validate and save 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=is_gene_available,\n",
    "    is_trait_available=True,  # We have trait data as identified in Step 2\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data for COVID-19 severity analysis.\"\n",
    ")\n",
    "\n",
    "# 7. Save the linked data if it's usable\n",
    "if is_usable:\n",
    "    # Create output directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the linked data\n",
    "    linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Linked data not saved due to quality issues.\")"
   ]
  }
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