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Significance How do the cells of an organism—each with an identical genome—give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that—by describing those biological and technical processes—allows us to infer the expression state of genes from replicate transcriptomic datasets.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes