Bayesian hierarchical modeling for subject-level response classification in peptide microarray immunoassays.

ABSTRACT: The peptide microarray immunoassay simultaneously screens sample serum against thousands of peptides, determining the presence of antibodies bound to array probes. Peptide microarrays tiling immunogenic regions of pathogens (e.g., envelope proteins of a virus) are an important high throughput tool for querying and mapping antibody binding. Because of the assay's many steps, from probe synthesis to incubation, peptide microarray data can be noisy with extreme outliers. In addition, subjects may produce different antibody profiles in response to an identical vaccine stimulus or infection, due to variability among subjects' immune systems. We present a robust Bayesian hierarchical model for peptide microarray experiments, pepBayes, to estimate the probability of antibody response for each sub

SUBMITTER: Imholte G 

PROVIDER: S-EPMC5617895 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature