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High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools forunderstanding cellular state. Often it is of interest to quantify and summarizechanges in cell state that occur between experimental or biological conditions.Differential expression is typically assessed using univariate tests to measuregene-wise shifts in expression. However, these methods largely ignore changesin transcriptional correlation. Furthermore, there is a need to identify thelow-dimensional structure of the gene expression shift to identify collectionsof genes that change between conditions. Here, we propose contrastive latentvariable models designed for count data to create a richer portrait ofdifferential expression in sequencing data. These models disentangle thesources of transcriptional variation in different conditions, in the context ofan explicit model of variation at baseline. Moreover, we develop a model-basedhypothesis testing framework that can test for global and gene subset-specificchanges in expression. We test our model through extensive simulations andanalyses with count-based gene expression data from perturbation andobservational sequencing experiments. We find that our methods can effectivelysummarize and quantify complex transcriptional changes in case-controlexperimental sequencing data.

Contrastive latent variable modeling with application to case-control sequencing experiments