  High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for
understanding cellular state. Often it is of interest to quantify and summarize
changes in cell state that occur between experimental or biological conditions.
Differential expression is typically assessed using univariate tests to measure
gene-wise shifts in expression. However, these methods largely ignore changes
in transcriptional correlation. Furthermore, there is a need to identify the
low-dimensional structure of the gene expression shift to identify collections
of genes that change between conditions. Here, we propose contrastive latent
variable models designed for count data to create a richer portrait of
differential expression in sequencing data. These models disentangle the
sources of transcriptional variation in different conditions, in the context of
an explicit model of variation at baseline. Moreover, we develop a model-based
hypothesis testing framework that can test for global and gene subset-specific
changes in expression. We test our model through extensive simulations and
analyses with count-based gene expression data from perturbation and
observational sequencing experiments. We find that our methods can effectively
summarize and quantify complex transcriptional changes in case-control
experimental sequencing data.
