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Deep generative models for end-to-end analysis of single-cell omics data.

Project description

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scvi-tools (single-cell variational inference tools) 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, as well as its improved differential expression framework
  • scANVI for cell annotation of scRNA-seq data using semi-labeled examples
  • totalVI for analysis of CITE-seq data
  • gimVI for imputation of missing genes in spatial transcriptomics from scRNA-seq data
  • AutoZI for assessing gene-specific levels of zero-inflation in scRNA-seq data
  • LDVAE for an interpretable linear factor model version of scVI

Tutorials, API reference, and installation guides are available in the documentation. Please use the issues here to submit bug reports. For discussion of usage, checkout out our forum. If you’d like to contribute, check out our development guide. If you find a model useful for your research, please consider citing the corresponding publication (linked above).

Package transition

scvi is now scvi-tools. If you’re looking for scvi source code, please use the branch tags. scvi is still available via pypi and bioconda, but there will be no future releases past 0.6.8. Documentation for scvi is available at the same documentation website.

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