Comprehensive normalization and binary classification methods for enhanced sensitivity and reproducibility in Luminex assay quantitation.

The Luminex assay is a powerful tool for large-scale quantitation of antibody levels and cytokines, but its utility can be limited by issues of specificity, sensitivity, and reproducibility. The corrections for background fluorescence and machine drift are essential steps in the normalization process. However, traditional methods often oversimplify these steps, failing to account for the complexity of the data, leading to the introduction of error and decreasing the sensitivity and reproducibility of the analysis. Furthermore, conventional methods to determine cut-points in binary measures do not consider the true distribution of the data, leading to arbitrary cut-points that compromise the integrity of the analysis. Here, we present a novel approach to normalize Luminex data and split the normalized bimodal data. Our method uses orthogonal regression of the measured fluorescence of a negative control bead and a blank bead to correct for background fluorescence, enhancing accuracy by preventing overcorrection due to cross-reactivity. To account for machine drift, we use a generalized additive model (GAM) on the standard curves to calculate a plate correction, thus reducing error and improving reproducibility. To distinguish between positive and negative results in bimodal measures, we use a clustering analysis to accurately split the data based on distribution. Finally, we developed a web application to easily carry out the developed method. These methods collectively increase sensitivity, specificity, and reproducibility of Luminex assay data analysis by effectively addressing the limitations of current normalization techniques, correcting for background fluorescence and machine drift, and improving the specificity and accuracy in splitting bimodal data.