A Bayesian hierarchical model for signal extraction from protein microarrays.

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 assa

SUBMITTER: Berube S 

PROVIDER: S-EPMC11806441 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature