Quick Start to ScrubletR
2024-02-02
QuickStart.RmdInstalling scrubletR
You will need to have the devtools package installed…
devtools::install_github("furlan-lab/scrubletR")Load data
suppressPackageStartupMessages({
library(viewmastR)
library(Seurat)
library(scCustomize)
library(scrubletR)
})
if(grepl("^gizmo", Sys.info()["nodename"])){
ROOT_DIR2<-"/fh/fast/furlan_s/grp/data/ddata/BM_data"
} else {
ROOT_DIR2<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/experiments/patient_marrows/aggr/cds/indy"
}
#query dataset
seuP<-readRDS(file.path(ROOT_DIR2, "220831_WC1.RDS"))
DimPlot_scCustom(seuP, label = F)
Run scrubletR the easy way (compatible with Seurat and monocle3 objects)
seuP<-scrublet(seuP)
FeaturePlot_scCustom(seuP, features = "doublet_scores")
seuP$doublets<-seuP$doublet_scores > 0.15 #(You pick this)
DimPlot(seuP, group.by = "doublets", cols=c("goldenrod", "navy"))
You can then remove them from your object and re-embed!
Appendix
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.6.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scrubletR_0.2.0 scCustomize_2.0.1 Seurat_5.0.1.9004 SeuratObject_5.0.1
## [5] sp_2.1-3 viewmastR_0.2.1
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.3 matrixStats_1.2.0
## [3] spatstat.sparse_3.0-3 bitops_1.0-7
## [5] RcppMsgPack_0.2.3 lubridate_1.9.3
## [7] httr_1.4.7 RColorBrewer_1.1-3
## [9] doParallel_1.0.17 tools_4.3.1
## [11] sctransform_0.4.1 backports_1.4.1
## [13] utf8_1.2.4 R6_2.5.1
## [15] lazyeval_0.2.2 uwot_0.1.16
## [17] GetoptLong_1.0.5 withr_3.0.0
## [19] gridExtra_2.3 progressr_0.14.0
## [21] cli_3.6.2 Biobase_2.60.0
## [23] textshaping_0.3.7 spatstat.explore_3.2-6
## [25] fastDummies_1.7.3 labeling_0.4.3
## [27] prismatic_1.1.1 sass_0.4.8
## [29] spatstat.data_3.0-4 ggridges_0.5.6
## [31] pbapply_1.7-2 pkgdown_2.0.7
## [33] systemfonts_1.0.5 foreign_0.8-86
## [35] parallelly_1.36.0 rstudioapi_0.15.0
## [37] generics_0.1.3 shape_1.4.6
## [39] ica_1.0-3 spatstat.random_3.2-2
## [41] dplyr_1.1.4 Matrix_1.6-5
## [43] ggbeeswarm_0.7.2 fansi_1.0.6
## [45] S4Vectors_0.38.2 abind_1.4-5
## [47] lifecycle_1.0.4 yaml_2.3.8
## [49] snakecase_0.11.1 SummarizedExperiment_1.30.2
## [51] recipes_1.0.9 Rtsne_0.17
## [53] paletteer_1.6.0 grid_4.3.1
## [55] promises_1.2.1 crayon_1.5.2
## [57] miniUI_0.1.1.1 lattice_0.22-5
## [59] cowplot_1.1.3 pillar_1.9.0
## [61] knitr_1.45 ComplexHeatmap_2.16.0
## [63] GenomicRanges_1.52.1 rjson_0.2.21
## [65] boot_1.3-28.1 future.apply_1.11.1
## [67] codetools_0.2-19 leiden_0.4.3.1
## [69] glue_1.7.0 data.table_1.15.0
## [71] vctrs_0.6.5 png_0.1-8
## [73] spam_2.10-0 gtable_0.3.4
## [75] rematch2_2.1.2 assertthat_0.2.1
## [77] cachem_1.0.8 gower_1.0.1
## [79] xfun_0.41 S4Arrays_1.2.0
## [81] mime_0.12 prodlim_2023.08.28
## [83] survival_3.5-7 timeDate_4032.109
## [85] SingleCellExperiment_1.22.0 iterators_1.0.14
## [87] pbmcapply_1.5.1 hardhat_1.3.0
## [89] lava_1.7.3 ellipsis_0.3.2
## [91] fitdistrplus_1.1-11 ROCR_1.0-11
## [93] ipred_0.9-14 nlme_3.1-164
## [95] RcppAnnoy_0.0.22 GenomeInfoDb_1.36.4
## [97] bslib_0.6.1 irlba_2.3.5.1
## [99] vipor_0.4.7 KernSmooth_2.23-22
## [101] rpart_4.1.23 colorspace_2.1-0
## [103] BiocGenerics_0.46.0 Hmisc_5.1-1
## [105] nnet_7.3-19 ggrastr_1.0.2
## [107] tidyselect_1.2.0 compiler_4.3.1
## [109] htmlTable_2.4.2 desc_1.4.3
## [111] DelayedArray_0.26.7 plotly_4.10.4
## [113] checkmate_2.3.1 scales_1.3.0
## [115] lmtest_0.9-40 stringr_1.5.1
## [117] digest_0.6.34 goftest_1.2-3
## [119] spatstat.utils_3.0-4 minqa_1.2.6
## [121] rmarkdown_2.25 XVector_0.40.0
## [123] htmltools_0.5.7 pkgconfig_2.0.3
## [125] base64enc_0.1-3 lme4_1.1-35.1
## [127] sparseMatrixStats_1.12.2 MatrixGenerics_1.12.3
## [129] highr_0.10 fastmap_1.1.1
## [131] rlang_1.1.3 GlobalOptions_0.1.2
## [133] htmlwidgets_1.6.4 shiny_1.8.0
## [135] DelayedMatrixStats_1.22.6 farver_2.1.1
## [137] jquerylib_0.1.4 zoo_1.8-12
## [139] jsonlite_1.8.8 ModelMetrics_1.2.2.2
## [141] RCurl_1.98-1.14 magrittr_2.0.3
## [143] Formula_1.2-5 GenomeInfoDbData_1.2.10
## [145] dotCall64_1.1-1 patchwork_1.2.0
## [147] munsell_0.5.0 Rcpp_1.0.12
## [149] reticulate_1.35.0 stringi_1.8.3
## [151] pROC_1.18.5 zlibbioc_1.46.0
## [153] MASS_7.3-60.0.1 plyr_1.8.9
## [155] parallel_4.3.1 listenv_0.9.1
## [157] ggrepel_0.9.5 forcats_1.0.0
## [159] deldir_2.0-2 splines_4.3.1
## [161] tensor_1.5 circlize_0.4.15
## [163] igraph_2.0.1.1 spatstat.geom_3.2-8
## [165] RcppHNSW_0.6.0 reshape2_1.4.4
## [167] stats4_4.3.1 evaluate_0.23
## [169] ggprism_1.0.4 nloptr_2.0.3
## [171] foreach_1.5.2 httpuv_1.6.14
## [173] RANN_2.6.1 tidyr_1.3.1
## [175] purrr_1.0.2 polyclip_1.10-6
## [177] future_1.33.1 clue_0.3-65
## [179] scattermore_1.2 ggplot2_3.4.4
## [181] janitor_2.2.0 xtable_1.8-4
## [183] monocle3_1.4.3 RSpectra_0.16-1
## [185] later_1.3.2 viridisLite_0.4.2
## [187] class_7.3-22 ragg_1.2.7
## [189] tibble_3.2.1 memoise_2.0.1
## [191] beeswarm_0.4.0 IRanges_2.34.1
## [193] cluster_2.1.6 timechange_0.3.0
## [195] globals_0.16.2 caret_6.0-94