scviR: an R package interfacing Bioconductor and scvi-tools (original) (raw)
Contents
Overview
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:
- scvi-tools components develop rapidly; we are using basiliskto manage R/python interoperation, and as of current writing we work with version 0.20.0 of scvi-tools. Specific versions of python components are enumerated in the file R/basilisk.R.
- A docker container based on a PyTorch-oriented image in the NVIDIA container registry includes R 4.2.2 and sufficient python 3 infrastructure to use scvi-tools 0.20.0 and scviR 0.0.2. A Dockerfile is in scviR/inst/Docker;
vjcitn/nvidpt_bioc:0.0.2can be pulled from dockerhub, it reports size 26.5GB. Container resources will be updated as needed. Users should file issues at the package GitHub repoif the container is stale. - Code presented in the cite-seq tutorial vignette follows the colab notebook for scvi-tools 0.18.0. We will check for modifications in the scvi-tools 0.20.0 notebook.
- Additional work on this package will facilitate comparisons between outcomes of Bioconductor, scVI, and other VI-oriented analytic toolkits in the single-cell domain.
Installation and use
As of Feb 2023, use BiocManager to install scviR in R 4.2.2 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)getCiteseqTutvae()(1.2 GB will be retrieved and cached)getTotalVINormalized5k10k()(191 MB will be retrieved and cached)
Session information
## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] scater_1.36.0 scuttle_1.18.0
## [3] reshape2_1.4.4 ggplot2_3.5.2
## [5] scviR_1.8.0 SingleCellExperiment_1.30.0
## [7] SummarizedExperiment_1.38.0 Biobase_2.68.0
## [9] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
## [11] IRanges_2.42.0 S4Vectors_0.46.0
## [13] BiocGenerics_0.54.0 generics_0.1.3
## [15] MatrixGenerics_1.20.0 matrixStats_1.5.0
## [17] shiny_1.10.0 basilisk_1.20.0
## [19] reticulate_1.42.0 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 gridExtra_2.3 rlang_1.1.6
## [4] magrittr_2.0.3 compiler_4.5.0 RSQLite_2.3.9
## [7] mgcv_1.9-3 dir.expiry_1.16.0 png_0.1-8
## [10] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3
## [13] crayon_1.5.3 fastmap_1.2.0 magick_2.8.6
## [16] dbplyr_2.5.0 XVector_0.48.0 labeling_0.4.3
## [19] promises_1.3.2 rmarkdown_2.29 UCSC.utils_1.4.0
## [22] ggbeeswarm_0.7.2 tinytex_0.57 purrr_1.0.4
## [25] bit_4.6.0 xfun_0.52 cachem_1.1.0
## [28] beachmat_2.24.0 jsonlite_2.0.0 blob_1.2.4
## [31] later_1.4.2 DelayedArray_0.34.0 BiocParallel_1.42.0
## [34] irlba_2.3.5.1 parallel_4.5.0 R6_2.6.1
## [37] stringi_1.8.7 bslib_0.9.0 RColorBrewer_1.1-3
## [40] limma_3.64.0 jquerylib_0.1.4 Rcpp_1.0.14
## [43] bookdown_0.43 knitr_1.50 splines_4.5.0
## [46] httpuv_1.6.15 Matrix_1.7-3 tidyselect_1.2.1
## [49] abind_1.4-8 yaml_2.3.10 viridis_0.6.5
## [52] codetools_0.2-20 curl_6.2.2 lattice_0.22-7
## [55] tibble_3.2.1 plyr_1.8.9 basilisk.utils_1.20.0
## [58] withr_3.0.2 evaluate_1.0.3 BiocFileCache_2.16.0
## [61] pillar_1.10.2 BiocManager_1.30.25 filelock_1.0.3
## [64] munsell_0.5.1 scales_1.3.0 xtable_1.8-4
## [67] glue_1.8.0 pheatmap_1.0.12 tools_4.5.0
## [70] BiocNeighbors_2.2.0 ScaledMatrix_1.16.0 cowplot_1.1.3
## [73] grid_4.5.0 colorspace_2.1-1 nlme_3.1-168
## [76] GenomeInfoDbData_1.2.14 beeswarm_0.4.0 BiocSingular_1.24.0
## [79] vipor_0.4.7 cli_3.6.4 rsvd_1.0.5
## [82] S4Arrays_1.8.0 viridisLite_0.4.2 dplyr_1.1.4
## [85] gtable_0.3.6 sass_0.4.10 digest_0.6.37
## [88] SparseArray_1.8.0 ggrepel_0.9.6 farver_2.1.2
## [91] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
## [94] httr_1.4.7 statmod_1.5.0 mime_0.13
## [97] bit64_4.6.0-1