Benchmarking batch-effect correction in clinical bead-based multiplex immunoassays: a framework and web tool for...

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).

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: log₂(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).

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.

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.