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",color_by="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",color_by=hgs[top3[1,1]])
p2 = plotReducedDim(sce,"tsne",color_by=hgs[top3[1,2]])

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

l3 = ggdraw() + draw_text("Pair 3", x = 0.5, y = 0.5)
p5 = plotReducedDim(sce,"tsne",color_by=hgs[top3[3,1]])
p6 = plotReducedDim(sce,"tsne",color_by=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 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.22-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] cowplot_1.2.0               Rtsne_0.17                 
##  [3] rsvd_1.0.5                  scater_1.37.0              
##  [5] ggplot2_4.0.0               scuttle_1.19.0             
##  [7] SingleCellExperiment_1.31.1 SummarizedExperiment_1.39.2
##  [9] Biobase_2.69.1              GenomicRanges_1.61.5       
## [11] Seqinfo_0.99.2              IRanges_2.43.5             
## [13] S4Vectors_0.47.4            BiocGenerics_0.55.3        
## [15] generics_0.1.4              MatrixGenerics_1.21.0      
## [17] matrixStats_1.5.0           scds_1.25.1                
## [19] BiocStyle_2.37.1           
## 
## loaded via a namespace (and not attached):
##  [1] beeswarm_0.4.0      gtable_0.3.6        xfun_0.53          
##  [4] bslib_0.9.0         ggrepel_0.9.6       lattice_0.22-7     
##  [7] vctrs_0.6.5         tools_4.5.1         parallel_4.5.1     
## [10] tibble_3.3.0        pkgconfig_2.0.3     BiocNeighbors_2.3.1
## [13] Matrix_1.7-4        data.table_1.17.8   RColorBrewer_1.1-3 
## [16] S7_0.2.0            lifecycle_1.0.4     compiler_4.5.1     
## [19] farver_2.1.2        tinytex_0.57        codetools_0.2-20   
## [22] vipor_0.4.7         htmltools_0.5.8.1   sass_0.4.10        
## [25] yaml_2.3.10         pillar_1.11.1       crayon_1.5.3       
## [28] jquerylib_0.1.4     BiocParallel_1.43.4 DelayedArray_0.35.3
## [31] cachem_1.1.0        magick_2.9.0        viridis_0.6.5      
## [34] abind_1.4-8         tidyselect_1.2.1    digest_0.6.37      
## [37] BiocSingular_1.25.0 dplyr_1.1.4         bookdown_0.45      
## [40] labeling_0.4.3      fastmap_1.2.0       grid_4.5.1         
## [43] cli_3.6.5           SparseArray_1.9.1   magrittr_2.0.4     
## [46] S4Arrays_1.9.1      dichromat_2.0-0.1   withr_3.0.2        
## [49] scales_1.4.0        xgboost_1.7.11.1    ggbeeswarm_0.7.2   
## [52] rmarkdown_2.30      XVector_0.49.1      gridExtra_2.3      
## [55] ScaledMatrix_1.17.0 beachmat_2.25.5     evaluate_1.0.5     
## [58] knitr_1.50          viridisLite_0.4.2   irlba_2.3.5.1      
## [61] rlang_1.1.6         Rcpp_1.1.0          glue_1.8.0         
## [64] BiocManager_1.30.26 pROC_1.19.0.1       jsonlite_2.0.0     
## [67] R6_2.6.1