scVI - Single cell Variational Inference¶
scVI is a package for end-to-end analysis of single-cell omics data. The package is composed of several deep generative models for omics data analysis, namely:
scVI for analysis of single-cell RNA-seq data [Lopez18]
scANVI for cell annotation of scRNA-seq data using semi-labeled examples [Xu19]
totalVI for analysis of CITE-seq data [Gayoso19]
gimVI for imputation of missing genes in spatial transcriptomics from scRNA-seq data [Lopez19]
AutoZI for assessing gene-specific levels of zero-inflation in scRNA-seq data [Clivio19]
LDVAE for an interpretable linear factor model version of scVI [Svensson20]
These models are able to simultaneously perform many downstream tasks such as learning low-dimensional cell representations, harmonizing datasets from different experiments, and identifying differential expressed features [Boyeau19]. By levaraging advances in stochastic optimization, these models scale to millions of cells. We invite you to explore these models in our tutorials.
If you find a model useful for your research, please consider citing the corresponding publication.