A Python library for probabilistic analysis of single-cell omics data

Methods for analyzing single-cell data perform a core set of computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state annotation, removal of unwanted variation, analysis of differential expression, identification of spatial patterns of gene expression, and joint analysis of multi-modal omics data. Many of these methods rely on likelihood-based models to represent variation in the data; we refer to these as 'probabilistic models'. Probabilistic models provide principled ways to capture uncertainty in biological systems and are convenient for decomposing the many sources of variation that give rise to omics data. ; © 2022 Nature Publishing Group. Published 07 February 2022. We acknowledge members of the Streets and Yosef laboratories for general feedback. We thank all the GitHub users who contributed code to scvi-tools over the years. We thank Nicholas Everetts for help with the analysis of the Drosophila data. We thank David Kelley and Nick Bernstein for help implementing Solo. We thank Marco Wagenstetter and Sergei Rybakov for help with the transition of the scGen package to use scvi-tools, as well as feedback on the scArches implementation. We thank Hector Roux de Bézieux for insightful discussions about the R ecosystem. We thank Kieran Campbell and Allen Zhang for clarifying aspects of the original CellAssign implementation. We thank the Pyro team, including Eli Bingham, Martin Jankowiak and Fritz Obermeyer, for help integrating Pyro in scvi-tools. Research reported in this manuscript was supported by the NIGMS of the National Institutes of Health under award number R35GM124916 and by the Chan-Zuckerberg Foundation Network under grant number 2019-02452. O.C. is supported by the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning (EP/S023151/1, studentship 2420649). A.G. is supported by NIH Training Grant 5T32HG000047-19. A.S. and N.Y. are Chan Zuckerberg Biohub investigators. Contributions: A.G., R.L and G.X. contributed equally. A.G. ...