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:
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 repo
if the container is stale.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)## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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_1.28.0 scuttle_1.10.0
## [3] reshape2_1.4.4 ggplot2_3.4.2
## [5] scviR_1.0.0 SingleCellExperiment_1.22.0
## [7] SummarizedExperiment_1.30.0 Biobase_2.60.0
## [9] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
## [11] IRanges_2.34.0 S4Vectors_0.38.0
## [13] BiocGenerics_0.46.0 MatrixGenerics_1.12.0
## [15] matrixStats_0.63.0 shiny_1.7.4
## [17] basilisk_1.12.0 BiocStyle_2.28.0
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## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] gridExtra_2.3 rlang_1.1.0
## [5] magrittr_2.0.3 compiler_4.3.0
## [7] RSQLite_2.3.1 mgcv_1.8-42
## [9] dir.expiry_1.8.0 DelayedMatrixStats_1.22.0
## [11] png_0.1-8 vctrs_0.6.2
## [13] stringr_1.5.0 pkgconfig_2.0.3
## [15] fastmap_1.1.1 magick_2.7.4
## [17] dbplyr_2.3.2 XVector_0.40.0
## [19] ellipsis_0.3.2 labeling_0.4.2
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## [35] DelayedArray_0.26.0 BiocParallel_1.34.0
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## [39] R6_2.5.1 stringi_1.7.12
## [41] bslib_0.4.2 RColorBrewer_1.1-3
## [43] limma_3.56.0 reticulate_1.28
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## [49] splines_4.3.0 httpuv_1.6.9
## [51] Matrix_1.5-4 tidyselect_1.2.0
## [53] yaml_2.3.7 viridis_0.6.2
## [55] codetools_0.2-19 curl_5.0.0
## [57] plyr_1.8.8 lattice_0.21-8
## [59] tibble_3.2.1 basilisk.utils_1.12.0
## [61] withr_2.5.0 evaluate_0.20
## [63] BiocFileCache_2.8.0 pillar_1.9.0
## [65] BiocManager_1.30.20 filelock_1.0.2
## [67] generics_0.1.3 rprojroot_2.0.3
## [69] RCurl_1.98-1.12 sparseMatrixStats_1.12.0
## [71] munsell_0.5.0 scales_1.2.1
## [73] xtable_1.8-4 glue_1.6.2
## [75] pheatmap_1.0.12 tools_4.3.0
## [77] BiocNeighbors_1.18.0 ScaledMatrix_1.8.0
## [79] cowplot_1.1.1 grid_4.3.0
## [81] colorspace_2.1-0 nlme_3.1-162
## [83] GenomeInfoDbData_1.2.10 beeswarm_0.4.0
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## [89] fansi_1.0.4 viridisLite_0.4.1
## [91] dplyr_1.1.2 gtable_0.3.3
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## [97] memoise_2.0.1 htmltools_0.5.5
## [99] lifecycle_1.0.3 here_1.0.1
## [101] httr_1.4.5 mime_0.12
## [103] bit64_4.0.5