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Workshop: Machine Learning for Drug Discovery (MLDD)

Isolating salient variations of interest in single-cell transcriptomic data with contrastiveVI

Ethan Weinberger · Chris Lin · Su-In Lee

Keywords: [ generative modeling ]


Single-cell RNA sequencing (scRNA-seq) technologies enable a better understanding of previously unexplored biological diversity. Oftentimes, researchers are specifically interested in modeling the latent structures and variations enriched in one target scRNA-seq dataset as compared to another background dataset generated from sources of variation irrelevant to the task at hand. For example, we may wish to isolate factors of variation only present in measurements from patients with a given disease as opposed to those shared with data from healthy control subjects. Here we introduce Contrastive Variational Inference (contrastiveVI;, a framework for end-to-end analysis of target scRNA-seq datasets that decomposes the variations into shared and target-specific factors of variation. On four target-background dataset pairs, we apply contrastiveVI to perform a number of standard analysis tasks, including visualization, clustering, and differential expression testing, and we consistently achieve results that agree with known biological ground truths.

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