A Bayesian hierarchical model for signal extraction from protein microarrays

Author:

Bérubé Sophie1ORCID,Kobayashi Tamaki2,Wesolowski Amy2,Norris Douglas E.3,Ruczinski Ingo1ORCID,Moss William J.23,Louis Thomas A.1ORCID

Affiliation:

1. Department of Biostatistics Johns Hopkins University Bloomberg School of Public Health Baltimore Maryland USA

2. Department of Epidemiology Johns Hopkins University Bloomberg School of Public Health Baltimore Maryland USA

3. Department of Molecular Microbiology and Immunology Johns Hopkins University Bloomberg School of Public Health Baltimore Maryland USA

Abstract

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within‐sample ranks of protein levels can mitigate the impact of between‐sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

Funder

National Institute of Allergy and Infectious Diseases

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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