An empirical Bayes approach to normalization and differential abundance testing for microbiome data.

ABSTRACT:

Background

Advances in DNA sequencing have offered researchers an unprecedented opportunity to better study the variety of species living in and on the human body. However, the analysis of microbiome data is complicated by several challenges. First, the sequencing depth may vary by orders of magnitude across samples. Second, species are rare and the data often contain many zeros. Third, the specimen is a fraction of the microbial ecosystem, and so the data are compositional carrying only relative information. Other characteristics of microbiome data include pronounced over-dispersion in taxon abundances, and the existence of a phylogenetic tree that relates all bacterial species. To address some of these challenges, microbiome analysis workflows often normalize the read counts prio

SUBMITTER: Liu T 

PROVIDER: S-EPMC7268703 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature