Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
Instructions, documentation, and tutorials can be found at:
Seurat is also hosted on GitHub, you can view and clone the repository at
Seurat has been successfully installed on Mac OS X, Linux, and Windows, using the devtools package to install directly from GitHub
Improvements and new features will be added on a regular basis, please post on the github page with any questions or if you would like to contribute
For a version history/changelog, please see the NEWS file.
R 4.1.0 / Ubuntu 20.04 on WSL2 / Ryzen 9 4900HS 8-core CPU / 16GB memory
Comparison of elapsed time in second.
| # workers | Original version without limma | Original version with limma | Modified version without limma | Modified version with limma |
|---|---|---|---|---|
| 1 | 149.326 | 67.102 | 38.926 | 35.586 |
| 2 | 81.133 | 39.678 | 25.752 | 25.205 |
| 4 | 49.811 | 24.716 | 17.001 | 16.203 |
| 8 | 37.819 | 18.974 | 13.559 | 12.891 |
> install.packages("Seurat")
> library(Seurat)
> library(SeuratData)
> library(future)
# https://satijalab.org/seurat/articles/de_vignette.html
> pbmc <- LoadData("pbmc3k", type = "pbmc3k.final")
> plan(multiprocess(workers = 1), .cleanup=TRUE)
> system.time(a0 <- FindAllMarkers(pbmc))
user system elapsed
66.747 0.329 67.102
> save(a0, file="pbmc3k_all_org.RData")
> plan(multiprocess(workers = 2), .cleanup=TRUE)
> system.time(a2 <- FindAllMarkers(pbmc))
user system elapsed
71.175 5.599 39.678
> all.equal(a0, a2)
[1] TRUE
> plan(multiprocess(workers = 4), .cleanup=TRUE)
> system.time(a4 <- FindAllMarkers(pbmc))
user system elapsed
78.607 7.777 24.716
> all.equal(a0, a4)
[1] TRUE
>
> plan(multiprocess(workers = 8), .cleanup=TRUE)
> system.time(a8 <- FindAllMarkers(pbmc))
user system elapsed
96.006 13.866 18.974
> all.equal(a0, a8)
[1] TRUE
> devtools::install_github("mogushi/Seurat")
> library(Seurat)
> library(SeuratData)
> library(future)
> pbmc <- LoadData("pbmc3k", type = "pbmc3k.final")
load(file="pbmc3k_all_org.RData")
> plan(multiprocess(workers = 1), .cleanup=TRUE)
> system.time(a1 <- FindAllMarkers(pbmc))
user system elapsed
35.304 0.271 35.586 # original: 67.102
> all.equal(a0, a1)
[1] TRUE
> plan(multiprocess(workers = 2), .cleanup=TRUE)
> system.time(a2 <- FindAllMarkers(pbmc))
user system elapsed
39.802 5.029 25.205 # original: 39.678
> all.equal(a0, a2)
[1] TRUE
> plan(multiprocess(workers = 4), .cleanup=TRUE)
> system.time(a4 <- FindAllMarkers(pbmc))
user system elapsed
42.361 6.146 16.203 # original: 24.716
> all.equal(a0, a4)
[1] TRUE
> plan(multiprocess(workers = 8), .cleanup=TRUE)
> system.time(a8 <- FindAllMarkers(pbmc))
user system elapsed
50.166 9.109 12.891 # original: 18.974
> all.equal(a0, a8)
[1] TRUE
> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] future_1.21.0 pbmc3k.SeuratData_3.1.4
[3] panc8.SeuratData_3.0.2 ifnb.SeuratData_3.1.0
[5] hcabm40k.SeuratData_3.0.0 cbmc.SeuratData_3.1.4
[7] SeuratData_0.2.1 SeuratObject_4.0.2
[9] Seurat_4.0.3
loaded via a namespace (and not attached):
[1] nlme_3.1-152 matrixStats_0.59.0 spatstat.sparse_2.0-0
[4] RcppAnnoy_0.0.18 RColorBrewer_1.1-2 httr_1.4.2
[7] sctransform_0.3.2 tools_4.1.0 utf8_1.2.1
[10] R6_2.5.0 irlba_2.3.3 rpart_4.1-15
[13] KernSmooth_2.23-20 uwot_0.1.10 mgcv_1.8-36
[16] lazyeval_0.2.2 colorspace_2.0-1 tidyselect_1.1.1
[19] gridExtra_2.3 compiler_4.1.0 cli_2.5.0
[22] plotly_4.9.4 scales_1.1.1 lmtest_0.9-38
[25] spatstat.data_2.1-0 ggridges_0.5.3 pbapply_1.4-3
[28] rappdirs_0.3.3 goftest_1.2-2 stringr_1.4.0
[31] digest_0.6.27 spatstat.utils_2.2-0 pkgconfig_2.0.3
[34] htmltools_0.5.1.1 parallelly_1.26.0 fastmap_1.1.0
[37] htmlwidgets_1.5.3 rlang_0.4.11 rstudioapi_0.13
[40] shiny_1.6.0 generics_0.1.0 zoo_1.8-9
[43] jsonlite_1.7.2 ica_1.0-2 dplyr_1.0.6
[46] magrittr_2.0.1 patchwork_1.1.1 Matrix_1.3-4
[49] Rcpp_1.0.6 munsell_0.5.0 fansi_0.5.0
[52] abind_1.4-5 reticulate_1.20 lifecycle_1.0.0
[55] stringi_1.6.2 MASS_7.3-54 Rtsne_0.15
[58] plyr_1.8.6 grid_4.1.0 parallel_4.1.0
[61] listenv_0.8.0 promises_1.2.0.1 ggrepel_0.9.1
[64] crayon_1.4.1 miniUI_0.1.1.1 deldir_0.2-10
[67] lattice_0.20-44 cowplot_1.1.1 splines_4.1.0
[70] tensor_1.5 ps_1.6.0 pillar_1.6.1
[73] igraph_1.2.6 spatstat.geom_2.2-0 future.apply_1.7.0
[76] reshape2_1.4.4 codetools_0.2-18 leiden_0.3.8
[79] glue_1.4.2 data.table_1.14.0 png_0.1-7
[82] vctrs_0.3.8 httpuv_1.6.1 gtable_0.3.0
[85] RANN_2.6.1 purrr_0.3.4 spatstat.core_2.1-2
[88] polyclip_1.10-0 tidyr_1.1.3 scattermore_0.7
[91] ggplot2_3.3.4 mime_0.10 xtable_1.8-4
[94] later_1.2.0 survival_3.2-11 viridisLite_0.4.0
[97] tibble_3.1.2 cluster_2.1.2 globals_0.14.0
[100] fitdistrplus_1.1-5 ellipsis_0.3.2 ROCR_1.0-11