About this Class

Presenter: Adam J Gayoso, Streets and Yosef Groups at UC Berkeley Abstract: Probabilistic models have demonstrated state-of-the-art performance for many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. As many of these models are amenable to scalable stochastic inference techniques, they will also be able to process single-cell datasets of growing sizes. However, the community-wide adoption of probabilistic models is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and help set a community standard for probabilistic modeling of single cell omics. Biography: Adam Gayoso is a Ph.D. candidate in the Center for Computational Biology graduate group at UC Berkeley, advised by Prof. Aaron Streets and Prof. Nir Yosef. His research interest lies at the intersection of machine learning and computational biology, with an emphasis on developing probabilistic models to aid in the interpretation of single-cell omics data. Prior to his Ph.D., Adam studied operations research and computer science at Columbia University, where he worked with Prof. Dana Pe'er on methodology for the analysis of single-cell transcriptomics data.