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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 and API reference are available in the documentation. Please use the issues here to discuss usage, or submit bug reports. If you’d like to contribute, please check out our contributing guide. If you find a model useful for your research, please consider citing the corresponding publication (linked above).

Package transition

scvi is transitioning to 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. An alpha-release of scvi-tools is currently available.

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