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 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-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_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [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_1.1.3               Rtsne_0.17                 
##  [3] rsvd_1.0.5                  scater_1.32.0              
##  [5] ggplot2_3.5.1               scuttle_1.14.0             
##  [7] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
##  [9] Biobase_2.64.0              GenomicRanges_1.56.0       
## [11] GenomeInfoDb_1.40.0         IRanges_2.38.0             
## [13] S4Vectors_0.42.0            BiocGenerics_0.50.0        
## [15] MatrixGenerics_1.16.0       matrixStats_1.3.0          
## [17] scds_1.20.0                 BiocStyle_2.32.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1          viridisLite_0.4.2        
##  [3] farver_2.1.1              dplyr_1.1.4              
##  [5] vipor_0.4.7               viridis_0.6.5            
##  [7] fastmap_1.1.1             pROC_1.18.5              
##  [9] digest_0.6.35             lifecycle_1.0.4          
## [11] magrittr_2.0.3            compiler_4.4.0           
## [13] rlang_1.1.3               sass_0.4.9               
## [15] tools_4.4.0               utf8_1.2.4               
## [17] yaml_2.3.8                data.table_1.15.4        
## [19] knitr_1.46                labeling_0.4.3           
## [21] S4Arrays_1.4.0            xgboost_1.7.7.1          
## [23] DelayedArray_0.30.0       plyr_1.8.9               
## [25] abind_1.4-5               BiocParallel_1.38.0      
## [27] withr_3.0.0               grid_4.4.0               
## [29] fansi_1.0.6               beachmat_2.20.0          
## [31] colorspace_2.1-0          scales_1.3.0             
## [33] tinytex_0.50              cli_3.6.2                
## [35] rmarkdown_2.26            crayon_1.5.2             
## [37] generics_0.1.3            httr_1.4.7               
## [39] DelayedMatrixStats_1.26.0 ggbeeswarm_0.7.2         
## [41] cachem_1.0.8              zlibbioc_1.50.0          
## [43] parallel_4.4.0            BiocManager_1.30.22      
## [45] XVector_0.44.0            vctrs_0.6.5              
## [47] Matrix_1.7-0              jsonlite_1.8.8           
## [49] bookdown_0.39             BiocSingular_1.20.0      
## [51] BiocNeighbors_1.22.0      ggrepel_0.9.5            
## [53] irlba_2.3.5.1             beeswarm_0.4.0           
## [55] magick_2.8.3              jquerylib_0.1.4          
## [57] glue_1.7.0                codetools_0.2-20         
## [59] gtable_0.3.5              UCSC.utils_1.0.0         
## [61] ScaledMatrix_1.12.0       munsell_0.5.1            
## [63] tibble_3.2.1              pillar_1.9.0             
## [65] htmltools_0.5.8.1         GenomeInfoDbData_1.2.12  
## [67] R6_2.5.1                  sparseMatrixStats_1.16.0 
## [69] evaluate_0.23             lattice_0.22-6           
## [71] highr_0.10                bslib_0.7.0              
## [73] Rcpp_1.0.12               gridExtra_2.3            
## [75] SparseArray_1.4.0         xfun_0.43                
## [77] pkgconfig_2.0.3