A Bayesian Hierarchical Model for Signal Extraction from Protein Microarrays

Abstract

SummaryProtein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to the high technical variability of these assays and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Using within-array ranks of protein levels for analysis can mitigate the impact of between-sample variation on downstream analysis. Although ranks are sensitive to pre-processing steps, ranking methods that accommodate uncertainty provide robust and loss-function optimal ranks. Such ranking methods require Bayesian modeling that produces full posterior distributions for parameters of interest. Bayesian models that produce such outputs have been developed for other assays, for example DNA microarrays, but those modeling assumptions are not appropriate for protein microarrays. We develop and evaluate a Bayesian model to extract a full posterior distribution of normalized fluorescent signals and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microrarrays from different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.