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

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, researchers from the University of Colorado and the Center for Genes, Environment and Health propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference.

In simulation studies, the researchers showed that their proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements.

Directed acyclic graph of the MCMSeq Model


Squares represent observed data, while circles represent model parameters to be estimated. Diamonds represent prior parameters and hyper-parameters. Solid arrows indicate stochastic dependence, while dashed arrows indicate deterministic dependence

Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods investigated by these researchers, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package best combined sensitivity and nominal error rate control.


Vestal BE, Moore CM, Wynn E, Saba L, Fingerlin T, Kechris K.  (2020) MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments. BMC Bioinformatics 21(1):375. [article]

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