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:0.0.2
can 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