Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models.

ABSTRACT: As the number of single-cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA-seq data, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage existing cell state annotations. We demonstr

SUBMITTER: Xu C 

PROVIDER: S-EPMC7829634 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature