Deep generative models for end-to-end analysis of single-cell omics data.
Project description
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 will be available shortly.
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