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Didong Li
University of North Carolina at Chapel Hill
Contrastive latent variable models to expose changes in case-control sequencing experiments

Here, we propose two different types of contrastive latent variable models 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 describe a model-based hypothesis testing framework in the context of count data that can test for global and gene subset-specific changes in expression. We validate our model through extensive simulations and analyses with 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 data. We then describe an extension to this model, called contrastive regression, that can be applied to data in which there is a continuous covariate associated with the case data, such as disease severity, treatment dose, or time, to characterize multivariate associations capturing variation exclusive to the case covariates.

Sarah Nyquist
Gladstone Institutes
Current techniques for case-control comparisons in high-throughput transcriptomics and the need for contrastive methods

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. Typical analysis strategies will begin with encoding samples in some low-dimensional space based on shared variation, followed by identification of cell states or types that change between conditions. With these states identified, the function, identities, and differences between conditions are described using univariate tests of differential expression on individual genes. These approaches ignore changes in transcriptional correlation and gene pathways across conditions. In this talk, we will motivate the need to identify the low-dimensional structure capturing variation exclusive to the case data.

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