MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments.

ABSTRACT:

Background

As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical neg

SUBMITTER: Vestal BE 

PROVIDER: S-EPMC7455910 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature