Nonlinear Mixed-Effects Modeling of Luminex Bead-Based Multiplex Assays: A Bioinformatics Post-Hoc Approach to Improve Signal-to-Noise Ratios

Authors

• J Candia
• A Biancotto
• JS Tsang

Abstract

We developed a bioinformatics pipeline of data quality control, calibration and analysis based on Luminex bead-level multiplex assays. The first step is a dip test analysis that flags non-unimodal bead-level distributions per well and analyte. Then, nonlinear mixed-effects models of standard curves are generated recursively, in which outliers are identified and corrected by an analysis of residuals. After obtaining a convergent model of standard curves, quality control bridge probes are calibrated to assess batch and plate effects. Finally, the standard curve model is applied to calibrate donor samples. This bioinformatics post-hoc approach is aimed at improving the quality of data typically characterized by poor signal-to-noise ratios. Although this pipeline is here applied to Luminex datasets, a similar framework could be implemented to analyze Mesoscale, Somalogic and other analyte-based multiplex assays.

Scientific Focus Area: Computational Biology

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