zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes
Thompson, Ammon; May, Michael; Moore, Brian; Kopp, Artyom (2020), zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes, Dryad, Dataset, https://doi.org/10.25338/B8XW4B
Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process, and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets--where we demonstrate its ability to correctly infer true (known) expression states--and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-seq datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and, remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely-available R package zigzag.
Simulated data sets.
MCMC log files and posterior probabilites of active gene expression generated by zigzag.
Public data used to benchmark method (described in manuscript)
National Institutes of Health, Award: 5F32GM125107
National Institutes of Health, Award: R35GM122592
National Science Foundation, Award: DEB-0842181
National Science Foundation
National Science Foundation, Award: DEB-0919529
National Science Foundation, Award: DBI-1356737