scvi-tools is an element of the scverse toolchest for single-cell omics data analysis.
The scviR package is a very elementary approach to interfacing between R, Bioconductor and scvi-tools. The long-term plan is to illustrate several aspects of variational inference (VI) applied to single cell genomics in a way that is convenient for Bioconductor users.
The package makes use of SingleCellExperiment and anndata representations of single-cell genomic assays.
Several points should be kept in mind when using this package:
As of May 2025, use BiocManager to install scviR in R 4.5.0 or above:
BiocManager::install("vjcitn/scviR")Be sure the remotes package has been installed. If you are working at a slow
internet connection, it may be useful to set options(timeout=3600) when running
functions
getCh12AllSce() (74 MB will be retrieved and cached)getCh12Sce() (58 MB will be retrieved and cached)getTotalVINormalized5k10k() (191 MB will be retrieved and cached)## R version 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] scviR_1.12.0 shiny_1.13.0
## [3] basilisk_1.24.0 reticulate_1.46.0
## [5] scater_1.40.0 ggplot2_4.0.3
## [7] scuttle_1.22.0 SingleCellExperiment_1.34.0
## [9] SummarizedExperiment_1.42.0 Biobase_2.72.0
## [11] GenomicRanges_1.64.0 Seqinfo_1.2.0
## [13] IRanges_2.46.0 S4Vectors_0.50.0
## [15] BiocGenerics_0.58.0 generics_0.1.4
## [17] MatrixGenerics_1.24.0 matrixStats_1.5.0
## [19] BiocStyle_2.40.0
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## [4] rlang_1.2.0 magrittr_2.0.5 otel_0.2.0
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## [13] pkgconfig_2.0.3 fastmap_1.2.0 magick_2.9.1
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## [76] cli_3.6.6 rsvd_1.0.5 rappdirs_0.3.4
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