zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes

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.