Contents

1 Introduction

In this vignette, we provide an overview of the basic functionality and usage of the scds package, which interfaces with SingleCellExperiment objects.

2 Installation

Install the scds package using Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("scds", version = "3.9")

Or from github:

library(devtools)
devtools::install_github('kostkalab/scds')

3 Quick start

scds takes as input a SingleCellExperiment object (see here SingleCellExperiment), where raw counts are stored in a counts assay, i.e. assay(sce,"counts"). An example dataset created by sub-sampling the cell-hashing cell-lines data set (see https://satijalab.org/seurat/hashing_vignette.html) is included with the package and accessible via data("sce").Note that scds is designed to workd with larger datasets, but for the purposes of this vignette, we work with a smaller example dataset. We apply scds to this data and compare/visualize reasults:

3.1 Example data set

Get example data set provided with the package.

library(scds)
library(scater)
library(rsvd)
library(Rtsne)
library(cowplot)
set.seed(30519)
data("sce_chcl")
sce = sce_chcl #- less typing
dim(sce)
## [1] 2000 2000

We see it contains 2,000 genes and 2,000 cells, 216 of which are identified as doublets:

table(sce$hto_classification_global)
## 
##  Doublet Negative  Singlet 
##      216       83     1701

We can visualize cells/doublets after projecting into two dimensions:

logcounts(sce) = log1p(counts(sce))
vrs            = apply(logcounts(sce),1,var)
pc             = rpca(t(logcounts(sce)[order(vrs,decreasing=TRUE)[1:100],]))
ts             = Rtsne(pc$x[,1:10],verb=FALSE)

reducedDim(sce,"tsne") = ts$Y; rm(ts,vrs,pc)
plotReducedDim(sce,"tsne",col="hto_classification_global")

3.2 Computational doublet annotation

We now run the scds doublet annotation approaches. Briefly, we identify doublets in two complementary ways: cxds is based on co-expression of gene pairs and works with absence/presence calls only, while bcds uses the full count information and a binary classification approach using artificially generated doublets. cxds_bcds_hybrid combines both approaches, for more details please consult (this manuscript). Each of the three methods returns a doublet score, with higher scores indicating more “doublet-like” barcodes.

#- Annotate doublet using co-expression based doublet scoring:
sce = cxds(sce,retRes = TRUE)
sce = bcds(sce,retRes = TRUE,verb=TRUE)
sce = cxds_bcds_hybrid(sce)
par(mfcol=c(1,3))
boxplot(sce$cxds_score   ~ sce$doublet_true_labels, main="cxds")
boxplot(sce$bcds_score   ~ sce$doublet_true_labels, main="bcds")
boxplot(sce$hybrid_score ~ sce$doublet_true_labels, main="hybrid")

3.3 Visualizing gene pairs

For cxds we can identify and visualize gene pairs driving doublet annoataions, with the expectation that the two genes in a pair might mark different types of cells (see manuscript). In the following we look at the top three pairs, each gene pair is a row in the plot below:

scds =
top3 = metadata(sce)$cxds$topPairs[1:3,]
rs   = rownames(sce)
hb   = rowData(sce)$cxds_hvg_bool
ho   = rowData(sce)$cxds_hvg_ordr[hb]
hgs  = rs[ho]

l1 =  ggdraw() + draw_text("Pair 1", x = 0.5, y = 0.5)
p1 = plotReducedDim(sce,"tsne",col=hgs[top3[1,1]])
p2 = plotReducedDim(sce,"tsne",col=hgs[top3[1,2]])

l2 =  ggdraw() + draw_text("Pair 2", x = 0.5, y = 0.5)
p3 = plotReducedDim(sce,"tsne",col=hgs[top3[2,1]])
p4 = plotReducedDim(sce,"tsne",col=hgs[top3[2,2]])

l3 = ggdraw() + draw_text("Pair 3", x = 0.5, y = 0.5)
p5 = plotReducedDim(sce,"tsne",col=hgs[top3[3,1]])
p6 = plotReducedDim(sce,"tsne",col=hgs[top3[3,2]])

plot_grid(l1,p1,p2,l2,p3,p4,l3,p5,p6,ncol=3, rel_widths = c(1,2,2))

4 Session Info

sessionInfo()
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] cowplot_1.1.1               Rtsne_0.16                 
##  [3] rsvd_1.0.5                  scater_1.24.0              
##  [5] ggplot2_3.3.5               scuttle_1.6.0              
##  [7] SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.0
##  [9] Biobase_2.56.0              GenomicRanges_1.48.0       
## [11] GenomeInfoDb_1.32.0         IRanges_2.30.0             
## [13] S4Vectors_0.34.0            BiocGenerics_0.42.0        
## [15] MatrixGenerics_1.8.0        matrixStats_0.62.0         
## [17] scds_1.12.0                 BiocStyle_2.24.0           
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7              tools_4.2.0              
##  [3] bslib_0.3.1               utf8_1.2.2               
##  [5] R6_2.5.1                  irlba_2.3.5              
##  [7] vipor_0.4.5               DBI_1.1.2                
##  [9] colorspace_2.0-3          withr_2.5.0              
## [11] tidyselect_1.1.2          gridExtra_2.3            
## [13] compiler_4.2.0            cli_3.3.0                
## [15] BiocNeighbors_1.14.0      DelayedArray_0.22.0      
## [17] labeling_0.4.2            bookdown_0.26            
## [19] sass_0.4.1                scales_1.2.0             
## [21] stringr_1.4.0             digest_0.6.29            
## [23] rmarkdown_2.14            XVector_0.36.0           
## [25] pkgconfig_2.0.3           htmltools_0.5.2          
## [27] sparseMatrixStats_1.8.0   highr_0.9                
## [29] fastmap_1.1.0             rlang_1.0.2              
## [31] DelayedMatrixStats_1.18.0 jquerylib_0.1.4          
## [33] generics_0.1.2            farver_2.1.0             
## [35] jsonlite_1.8.0            BiocParallel_1.30.0      
## [37] dplyr_1.0.8               RCurl_1.98-1.6           
## [39] magrittr_2.0.3            BiocSingular_1.12.0      
## [41] GenomeInfoDbData_1.2.8    Matrix_1.4-1             
## [43] Rcpp_1.0.8.3              ggbeeswarm_0.6.0         
## [45] munsell_0.5.0             fansi_1.0.3              
## [47] viridis_0.6.2             lifecycle_1.0.1          
## [49] stringi_1.7.6             pROC_1.18.0              
## [51] yaml_2.3.5                zlibbioc_1.42.0          
## [53] plyr_1.8.7                grid_4.2.0               
## [55] parallel_4.2.0            ggrepel_0.9.1            
## [57] crayon_1.5.1              lattice_0.20-45          
## [59] beachmat_2.12.0           magick_2.7.3             
## [61] knitr_1.38                pillar_1.7.0             
## [63] xgboost_1.6.0.1           ScaledMatrix_1.4.0       
## [65] glue_1.6.2                evaluate_0.15            
## [67] data.table_1.14.2         BiocManager_1.30.17      
## [69] vctrs_0.4.1               gtable_0.3.0             
## [71] purrr_0.3.4               assertthat_0.2.1         
## [73] xfun_0.30                 viridisLite_0.4.0        
## [75] tibble_3.1.6              beeswarm_0.4.0           
## [77] ellipsis_0.3.2