Data Science & Analytics
Tooluniverse scVI Tools
Deep learning for single-cell omics using scvi-tools — probabilistic (VAE-based) batch correction and integration with scVI, semi-supervised integration and label transfer with scANVI, CITE-seq RNA+protein joint modeling with totalVI, scATAC-seq analysis with PeakVI, multiome RNA+ATAC joint modeling with MultiVI, spatial transcriptomics cell-type deconvolution with DestVI, and RNA velocity with veloVI. Use when scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, or veloVI are mentioned by name, or when a request needs deep-learning-based batch correction, reference mapping / scArches-style query-to-reference mapping, denoising single-cell counts, joint modeling of two modalities (RNA+protein, RNA+ATAC, single-cell+spatial), or a variational-autoencoder latent space for single-cell data — as opposed to classic scanpy/Harmony-based integration.
- Publisher / source
- mims-harvard/ToolUniverse (found on GitHub top repositories)
- Upstream record
- mims-harvard/ToolUniverse
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
7c48346b4f96ffee222ab15b5907e1be01521c018bf7ac40717334d0768612c8