Benchmarking batch-effect correction in clinical bead-based multiplex immunoassays: a framework and web tool for cytokine and soluble immune checkpoint data
13 affiliations
- 1Research Unit, Osakidetza Basque Health Service, Barrualde-Galdakao Integrated Health Organization, Galdakao-Usansolo Hospital
- 2Kronikgune Institute for Health Services Research
- 3Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)
- 4The Innate Immune Response Group, IdiPAZ, La Paz University Hospital
- 5Tumour Immunology Laboratory, IdiPAZ, La Paz University Hospital
- 6Digestive Surgery Service, La Paz University Hospital
- 7Digestive Surgery Department, Faculty of Medicine, Universidad Autónoma de Madrid
- 8Translational Research and Innovation in Surgery Group, La Paz University Hospital
- 9CORE Laboratory, Hospital Galdakao-Usansolo
- 10BioBizkaia Health Research Institute
- 11Faculty of Health Sciences, UNIE University
- 12Immunomodulation Laboratory, IdiPAZ, La Paz University Hospital
- 13CIBER of Respiratory Diseases (CIBERES)
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).
Methods: 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).
Results: 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.
Conclusion: 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.
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BibTeX
@article{aguirrelarracoechea2026benchmarking,
title = {{Benchmarking batch-effect correction in clinical bead-based multiplex immunoassays: a framework and web tool for cytokine and soluble immune checkpoint data}},
author = {Urko Aguirre-Larracoechea and Laura Hurtado-Navarro and Julia del Prado-Montero and Ramón Cantero-Cid and Eloisa Urrechaga-Igartua and Verónica Terrón-Arcos and Jesús Fernández-Felipe and Laura Córdoba and José G. Rigor-Pérez and Rebeca Abad-Moret and Esteban Díaz-Serrano and Roberto Lozano-Rodríguez and Carlos del Fresno and Eduardo López-Collazo},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1944271},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1944271/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Benchmarking batch-effect correction in clinical bead-based multiplex immunoassays: a framework and web tool for cytokine and soluble immune checkpoint data
AU - Urko Aguirre-Larracoechea
AU - Laura Hurtado-Navarro
AU - Julia del Prado-Montero
AU - Ramón Cantero-Cid
AU - Eloisa Urrechaga-Igartua
AU - Verónica Terrón-Arcos
AU - Jesús Fernández-Felipe
AU - Laura Córdoba
AU - José G. Rigor-Pérez
AU - Rebeca Abad-Moret
AU - Esteban Díaz-Serrano
AU - Roberto Lozano-Rodríguez
AU - Carlos del Fresno
AU - Eduardo López-Collazo
PY - 2026
DA - 2026/10/08
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1944271
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1944271/full
AB - 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). Methods: 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). Results: 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. Conclusion: 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.
SN - 1664-3224
ER -