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bioRxiv · 10.1101/2022.10.04.510898

Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics

Boyeau, P.·Hong, J.·Gayoso, A.·Jordan, M.·Azizi, E.·Yosef, N.
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Abstract

The field of single-cell genomics is now observing a marked increase in the prevalence of cohort-level studies that include hundreds of samples and feature complex designs. These data have tremendous potential for discovering how sample or tissue-level phenotypes relate to cellular and molecular composition. However, current analyses are based on simplified representations of these data by averaging information across cells. We present MrVI, a deep generative model designed to realize the potential of cohort studies at the single-cell level. MrVI tackles two fundamental and intertwined problems: stratifying samples into groups and evaluating the cellular and molecular differences between groups, both without requiring a priori grouping of cells into types or states. Due to its single-cell perspective, MrVI is able to detect clinically relevant stratifications of patients in COVID-19 and inflammatory bowel disease (IBD) cohorts that are only manifested in certain cellular subsets, thus enabling new discoveries that would otherwise be overlooked. Similarly, we demonstrate that MrVI can de-novo identify groups of small molecules with similar biochemical properties and evaluate their effects on cellular composition and gene expression in large-scale perturbation studies. MrVI is available as open source at scvi-tools.org.

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Boyeau, P., Hong, J., Gayoso, A., Jordan, M., Azizi, E., Yosef, N.. 2022-10-06. Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics. https://doi.org/10.1101/2022.10.04.510898

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