scVI - Single cell Variational Inference

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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.

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