A Bayesian hierarchical model for signal extraction from protein microarrays

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1445-1460
Number of pages16
JournalStatistics in Medicine
Volume42
Issue number9
DOIs
StatePublished - Apr 30 2023

Keywords

  • Bayesian models
  • bioinformatics
  • optimal ranks
  • preprocessing
  • protein microarrays

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint

Dive into the research topics of 'A Bayesian hierarchical model for signal extraction from protein microarrays'. Together they form a unique fingerprint.

Cite this