# scds:single cell doublet scoring: In-silico doublet annotation for single cell RNA sequencing data

#### 30 October 2024

# 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 4.4.1 (2024-06-14 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.utf8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## 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_1.34.0
    ##  [5] ggplot2_3.5.1               scuttle_1.16.0
    ##  [7] SingleCellExperiment_1.28.0 SummarizedExperiment_1.36.0
    ##  [9] Biobase_2.66.0              GenomicRanges_1.58.0
    ## [11] GenomeInfoDb_1.42.0         IRanges_2.40.0
    ## [13] S4Vectors_0.44.0            BiocGenerics_0.52.0
    ## [15] MatrixGenerics_1.18.0       matrixStats_1.4.1
    ## [17] scds_1.22.0                 BiocStyle_2.34.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.2.1        viridisLite_0.4.2       farver_2.1.2
    ##  [4] dplyr_1.1.4             vipor_0.4.7             viridis_0.6.5
    ##  [7] fastmap_1.2.0           pROC_1.18.5             digest_0.6.37
    ## [10] lifecycle_1.0.4         magrittr_2.0.3          compiler_4.4.1
    ## [13] rlang_1.1.4             sass_0.4.9              tools_4.4.1
    ## [16] utf8_1.2.4              yaml_2.3.10             data.table_1.16.2
    ## [19] knitr_1.48              labeling_0.4.3          S4Arrays_1.6.0
    ## [22] xgboost_1.7.8.1         DelayedArray_0.32.0     plyr_1.8.9
    ## [25] abind_1.4-8             BiocParallel_1.40.0     withr_3.0.2
    ## [28] grid_4.4.1              fansi_1.0.6             beachmat_2.22.0
    ## [31] colorspace_2.1-1        scales_1.3.0            tinytex_0.53
    ## [34] cli_3.6.3               rmarkdown_2.28          crayon_1.5.3
    ## [37] generics_0.1.3          httr_1.4.7              ggbeeswarm_0.7.2
    ## [40] cachem_1.1.0            zlibbioc_1.52.0         parallel_4.4.1
    ## [43] BiocManager_1.30.25     XVector_0.46.0          vctrs_0.6.5
    ## [46] Matrix_1.7-1            jsonlite_1.8.9          bookdown_0.41
    ## [49] BiocSingular_1.22.0     BiocNeighbors_2.0.0     ggrepel_0.9.6
    ## [52] irlba_2.3.5.1           beeswarm_0.4.0          magick_2.8.5
    ## [55] jquerylib_0.1.4         glue_1.8.0              codetools_0.2-20
    ## [58] gtable_0.3.6            UCSC.utils_1.2.0        ScaledMatrix_1.14.0
    ## [61] munsell_0.5.1           tibble_3.2.1            pillar_1.9.0
    ## [64] htmltools_0.5.8.1       GenomeInfoDbData_1.2.13 R6_2.5.1
    ## [67] evaluate_1.0.1          lattice_0.22-6          highr_0.11
    ## [70] bslib_0.8.0             Rcpp_1.0.13             gridExtra_2.3
    ## [73] SparseArray_1.6.0       xfun_0.48               pkgconfig_2.0.3