scds:single cell doublet scoring: In-silico doublet annotation for single cell RNA sequencing data
2 May 2019
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 2000We see it contains 2,000 genes and 2,000 cells, 216 of which are identified as doublets:
table(sce$hto_classification_global)##
## Doublet Negative Singlet
## 216 83 1701We can visualize cells/doublets after projecting into two dimensions:
logcounts(sce) = log1p(counts(sce))
vrs = apply(logcounts(sce),1,var)
pc = rpca(t(logcounts(sce)[order(vrs,decreasing=TRUE)[1:100],]))
ts = Rtsne(pc$x[,1:10],verb=FALSE)
reducedDim(sce,"tsne") = ts$Y; rm(ts,vrs,pc)
plotReducedDim(sce,"tsne",col="hto_classification_global")
3.2 Computational doublet annotation
We now run the scds doublet annotation approaches. Briefly, we identify doublets in two complementary ways: cxds is based on co-expression of gene pairs and works with absence/presence calls only, while bcds uses the full count information and a binary classification approach using artificially generated doublets. cxds_bcds_hybrid combines both approaches, for more details please consult (this manuscript). Each of the three methods returns a doublet score, with higher scores indicating more “doublet-like” barcodes.
#- Annotate doublet using co-expression based doublet scoring:
sce = cxds(sce,retRes = TRUE)
sce = bcds(sce,retRes = TRUE,verb=TRUE)## [1] train-error:0.154125+0.007228 test-error:0.216500+0.005557
## Multiple eval metrics are present. Will use test_error for early stopping.
## Will train until test_error hasn't improved in 2 rounds.
##
## [2] train-error:0.140375+0.001816 test-error:0.201500+0.004430
## [3] train-error:0.119938+0.004390 test-error:0.190250+0.009533
## [4] train-error:0.112688+0.003110 test-error:0.183000+0.006964
## [5] train-error:0.102875+0.003684 test-error:0.175000+0.007542
## [6] train-error:0.095625+0.003325 test-error:0.176000+0.008958
## [7] train-error:0.087875+0.004939 test-error:0.174250+0.012811
## [8] train-error:0.084438+0.003652 test-error:0.173000+0.008573
## [9] train-error:0.079250+0.003421 test-error:0.171250+0.007027
## [10] train-error:0.073250+0.002707 test-error:0.172500+0.006937
## [11] train-error:0.067937+0.002489 test-error:0.171250+0.004472
## Stopping. Best iteration:
## [9] train-error:0.079250+0.003421 test-error:0.171250+0.007027
##
## [1] train-error:0.162500
## Will train until train_error hasn't improved in 2 rounds.
##
## [2] train-error:0.137500
## [3] train-error:0.118250
## [4] train-error:0.106750
## [5] train-error:0.103500sce = cxds_bcds_hybrid(sce)
par(mfcol=c(1,3))
boxplot(sce$cxds_score ~ sce$doublet_true_labels, main="cxds")
boxplot(sce$bcds_score ~ sce$doublet_true_labels, main="bcds")
boxplot(sce$hybrid_score ~ sce$doublet_true_labels, main="hybrid")
3.3 Visualizing gene pairs
For cxds we can identify and visualize gene pairs driving doublet annoataions, with the expectation that the two genes in a pair might mark different types of cells (see manuscript). In the following we look at the top three pairs, each gene pair is a row in the plot below:
top3 = metadata(sce)$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 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] cowplot_0.9.4 Rtsne_0.15
## [3] rsvd_1.0.0 scater_1.12.0
## [5] ggplot2_3.1.1 SingleCellExperiment_1.6.0
## [7] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
## [9] BiocParallel_1.18.0 matrixStats_0.54.0
## [11] Biobase_2.44.0 GenomicRanges_1.36.0
## [13] GenomeInfoDb_1.20.0 IRanges_2.18.0
## [15] S4Vectors_0.22.0 BiocGenerics_0.30.0
## [17] scds_1.0.0 BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 lattice_0.20-38
## [3] assertthat_0.2.1 digest_0.6.18
## [5] R6_2.4.0 plyr_1.8.4
## [7] evaluate_0.13 pillar_1.3.1
## [9] zlibbioc_1.30.0 rlang_0.3.4
## [11] lazyeval_0.2.2 data.table_1.12.2
## [13] irlba_2.3.3 Matrix_1.2-17
## [15] rmarkdown_1.12 labeling_0.3
## [17] BiocNeighbors_1.2.0 stringr_1.4.0
## [19] RCurl_1.95-4.12 munsell_0.5.0
## [21] compiler_3.6.0 vipor_0.4.5
## [23] BiocSingular_1.0.0 xfun_0.6
## [25] pkgconfig_2.0.2 ggbeeswarm_0.6.0
## [27] htmltools_0.3.6 tidyselect_0.2.5
## [29] tibble_2.1.1 gridExtra_2.3
## [31] GenomeInfoDbData_1.2.1 bookdown_0.9
## [33] viridisLite_0.3.0 crayon_1.3.4
## [35] dplyr_0.8.0.1 withr_2.1.2
## [37] bitops_1.0-6 grid_3.6.0
## [39] gtable_0.3.0 magrittr_1.5
## [41] scales_1.0.0 stringi_1.4.3
## [43] XVector_0.24.0 viridis_0.5.1
## [45] DelayedMatrixStats_1.6.0 xgboost_0.82.1
## [47] tools_3.6.0 glue_1.3.1
## [49] beeswarm_0.2.3 purrr_0.3.2
## [51] yaml_2.2.0 colorspace_1.4-1
## [53] BiocManager_1.30.4 knitr_1.22