{
  "abstract": "Background: Batch effects represent a major source of non-biological variability in clinical bead-based multiplex immunoassays (BBMIs), particularly when samples are processed across different analytical days and when measurements include a substantial proportion of values at or near the limit of detection (LOD).\n\nMethods: Here, we developed and evaluated a comparative framework for reducing inter-batch variability in plasma cytokines with the Essential Cytokines Panel and soluble immune checkpoints (sICs) measurement with the Immune Checkpoint Panel. We also implemented this workflow in a freely accessible R Shiny-based web application that enables interactive batch-effect correction, visualization of correction performance, and export of harmonized datasets. Twenty-five proteins were quantified in 127 clinical samples processed across three analytical days: day 1, n = 26; day 2, n = 72; and day 3, n = 29. Seven batch-correction approaches were evaluated: median-centering, ComBat , and five methods from the batchtma R package: btma_simple , btma_standardize , btma_ipw , btma_quantreg and btma_quantnorm . Each method was assessed under two preprocessing strategies: log ₂ (x+1) transformation and analysis of raw concentrations expressed in pg/mL values. Correction performance was evaluated using the intraclass correlation coefficient (ICC), Kruskal-Wallis test, and inter-batch coefficient of variation (CV).\n\nResults: Before correction, ICC values were 0.207 for the Essential Cytokines Panel and 0.362 for the Immune Checkpoint Panel; furthermore, 13/13 and 12/12 markers, respectively, showed significant inter-day differences. btma_quantnorm achieved the largest and most consistent improvements across both panels and preprocessing strategies, reducing mean ICC values to ≤0.004 corresponding to a ≥98.1% reduction. After correction, only 0–1 markers retained significant Kruskal-Wallis results, and btma_quantnorm yielded the lowest inter-day CV for the Immune Checkpoint Panel (80.9%). Importantly, this method does not rely on normality assumptions and showed stable performance across the analytical workflows evaluated.\n\nConclusion: These findings support btma_quantnorm as a practical batch-correction approach for clinical BBMI dataset affected by inter-batch variability and LOD-related distributional constraints. The accompanying web-based tool translates this benchmarking framework into an accessible resource for applying, comparing and evaluating batch-correction in multiplex immunoassay datasets.",
  "abstract_shown": true,
  "affiliations": [
    "Research Unit, Osakidetza Basque Health Service, Barrualde-Galdakao Integrated Health Organization, Galdakao-Usansolo Hospital",
    "Kronikgune Institute for Health Services Research",
    "Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)",
    "The Innate Immune Response Group, IdiPAZ, La Paz University Hospital",
    "Tumour Immunology Laboratory, IdiPAZ, La Paz University Hospital",
    "Digestive Surgery Service, La Paz University Hospital",
    "Digestive Surgery Department, Faculty of Medicine, Universidad Autónoma de Madrid",
    "Translational Research and Innovation in Surgery Group, La Paz University Hospital",
    "CORE Laboratory, Hospital Galdakao-Usansolo",
    "BioBizkaia Health Research Institute",
    "Faculty of Health Sciences, UNIE University",
    "Immunomodulation Laboratory, IdiPAZ, La Paz University Hospital",
    "CIBER of Respiratory Diseases (CIBERES)"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Urko Aguirre-Larracoechea"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Laura Hurtado-Navarro"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Julia del Prado-Montero"
    },
    {
      "affs": [
        5,
        6,
        7
      ],
      "name": "Ramón Cantero-Cid"
    },
    {
      "affs": [
        8,
        9
      ],
      "name": "Eloisa Urrechaga-Igartua"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Verónica Terrón-Arcos"
    },
    {
      "affs": [
        3,
        4,
        10
      ],
      "name": "Jesús Fernández-Felipe"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Laura Córdoba"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "José G. Rigor-Pérez"
    },
    {
      "affs": [
        5,
        6,
        7
      ],
      "name": "Rebeca Abad-Moret"
    },
    {
      "affs": [
        5,
        6,
        7
      ],
      "name": "Esteban Díaz-Serrano"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Roberto Lozano-Rodríguez"
    },
    {
      "affs": [
        0,
        11
      ],
      "name": "Carlos del Fresno"
    },
    {
      "affs": [
        3,
        4,
        10,
        12
      ],
      "name": "Eduardo López-Collazo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dc53gr4mv4k0t.bib",
  "cited_by": 0,
  "doi": "10.3389/fimmu.2026.1944271",
  "fulltext_url": "https://europepmc.org/articles/PMC13687280",
  "id": "dc53gr4mv4k0t",
  "issns": [
    "1664-3224"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1944271/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Advanced Biosensing Techniques and Applications"
    ]
  ],
  "openalex": "W7221020276",
  "pdf_url": "https://europepmc.org/articles/PMC13687280?pdf=render",
  "pmcid": "PMC13687280",
  "pmid": "42923334",
  "published": "2026-10-08",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/dc53gr4mv4k0t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:15Z",
      "native_id": "10.3389/fimmu.2026.1944271",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1944271/full"
    },
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42923334",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42923334"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Advanced Biosensing Techniques and Applications",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Benchmarking batch-effect correction in clinical bead-based multiplex immunoassays: a framework and web tool for cytokine and soluble immune checkpoint data",
  "title_text": "Benchmarking batch-effect correction in clinical bead-based multiplex immunoassays: a framework and web tool for cytokine and soluble immune checkpoint data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/dc53gr4mv4k0t",
  "venue": "Frontiers in Immunology",
  "versions": []
}