scRNA-Seq Embedding Methods

Introduction

This tutorial introduces the usage of several software implementations of embedding algorithms for high-dimensional gene expression data (Duò, Robinson, and Soneson 2018) that are often used for single cell RNA-Seq (scRNA-Seq) data. Many of them are available as R packages on CRAN, Bioconductor and/or GitHub. Examples include PCA, MDS, SC3 (Kiselev et al. 2017), isomap, t-SNE (Donaldson and Donaldson 2010), FIt-SNE (Linderman et al. 2019), and UMAP (McInnes, Healy, and Melville 2018). In addition, some packages such as Bioconductor’s scater package provide in a single environment access to a wide range of embedding methods that can be conveniently and uniformly applied to Bioconductor’s S4 object class called SingleCellExperiment for handling scRNA-Seq data (Senabouth et al. 2019; Amezquita et al. 2020). The performance of the different embedding methods for scRNA-Seq data has been intensively tested by several studies, including Sun et al. (2019; 2020).

For illustration purposes, the following example code first applies four widely used embedding methods to a bulk RNA-Seq data set (Howard et al. 2013), and then to a much more complex scRNA-Seq data set (Aztekin et al. 2019) obtained from the scRNAseq package.

Bulk RNA-Seq data

Generate SummarizedExperiment and SingleCellExperiment

The following loads the bulk RNA-Seq data from Howard et al. (2013) into SummarizedExperiment and SingleCellExperiment objects. This is done by first creating a SummarizedExperiment object and then coercing it to a SingleCellExperiment object, as well as intializing the SingleCellExperiment directly.

Create SummarizedExperiment and coerce to SingleCellExperiment

The required targetsPE.txt and countDFeByg.xls files can be downloaded from here.

library(SummarizedExperiment); library(SingleCellExperiment)                                                                                                                        
targetspath <- "results/targetsPE.txt"                                                                                                                                                      
countpath <- "results/countDFeByg.xls"                                                                                                                                              
targets <- read.delim(targetspath, comment.char = "#")                                                                                                                              
rownames(targets) <- targets$SampleName                                                                                                                                             
countDF <- read.delim(countpath, row.names=1, check.names=FALSE)                                                                                                                    
(se <- SummarizedExperiment(assays=list(counts=countDF), colData=targets))                                                                                                          

class: SummarizedExperiment 
dim: 29699 18 
metadata(0):
assays(1): counts
rownames(29699): AT1G01010 AT1G01020 ... ATMG01400 ATMG01410
rowData names(0):
colnames(18): M1A M1B ... V12A V12B
colData names(7): FileName1 FileName2 ... Experiment Date

(sce <- as(se, "SingleCellExperiment"))

class: SingleCellExperiment 
dim: 29699 18 
metadata(0):
assays(1): counts
rownames(29699): AT1G01010 AT1G01020 ... ATMG01400 ATMG01410
rowData names(0):
colnames(18): M1A M1B ... V12A V12B
colData names(7): FileName1 FileName2 ... Experiment Date
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

Create SingleCellExperiment directly

sce2 <- SingleCellExperiment(assays=list(counts=countDF), colData=targets)

Prepare data for plotting with embedding methods

The data are preprocessed (_e.g._normalized) to plot them with the run embedding functions from the scran and scater packages.

library(scran); library(scater)
sce <- logNormCounts(sce)
colLabels(sce) <- factor(colData(sce)$Factor) # This uses replicate info from above targets file as pseudo-clusters

Embed with different methods and plot results

Note, the embedding results are sequentially appended to the SingleCellExperiment object, meaning one can use the plot function whenever necessary.

(a) tSNE

sce <- runTSNE(sce)
reducedDimNames(sce)

[1] "TSNE"

plotTSNE(sce, colour_by="label", text_by="label")

(b) MDS

sce <- runMDS(sce)
reducedDimNames(sce)

[1] "TSNE" "MDS" 

plotMDS(sce, colour_by="label", text_by="label")

(c) UMAP

sce <- runUMAP(sce) 
reducedDimNames(sce)

[1] "TSNE" "MDS"  "UMAP"

plotUMAP(sce, colour_by="label", text_by="label")

(d) PCA

PCA plot for first two components.

sce <- runPCA(sce) # gives a warning due to small size of data set but it still works 
reducedDimNames(sce)

[1] "TSNE" "MDS"  "UMAP" "PCA" 

plotPCA(sce, colour_by="label", text_by="label")

Multiple components can be plotted in a series of pairwise plots. When more than two components are plotted, the diagonal boxes in the scatter plot matrix show the density for each component.

sce <- runPCA(sce, ncomponents=20) # gives a warning due to small size of data set but it still works 
reducedDimNames(sce)

[1] "TSNE" "MDS"  "UMAP" "PCA" 

plotPCA(sce, colour_by="label", text_by="label", ncomponents = 4)

scRNA-Seq data

Load scRNA-Seq data

The scRNAseq package is used to load the scRNA-Seq data set from Xenopus tail directly into a SingleCellExperiment object (Aztekin et al. 2019).

library(scRNAseq)
sce <- AztekinTailData()

Prepare data for plotting with embedding methods

Similarly as above, the data are preprocessed (_e.g._normalized) to plot them with the run embedding functions from the scran package. In addition, the data is clustered with the quickCluster function.

library(scran); library(scater)
sce <- logNormCounts(sce)
clusters <- quickCluster(sce)
# sce <- computeSumFactors(sce, clusters=clusters)
colLabels(sce) <- factor(clusters)
table(colLabels(sce))

To acclerate the testing performance of the following code, the size of the expression matrix is reduced to cell types with values \(\ge10^4\).

filter <- colSums(assays(sce)$counts) >= 10^4
sce <- sce[, filter]

To color items in the downstream dot plots by cell type instead of the above clustering result, one can use the cell type info under colData(). Note, this step is not evaluated here.

# colLabels(sce) <- colData(sce)$cluster

Embed with different methods and plot results

As under the bulk RNA-Seq section, the embedding results are sequentially appended to the SingleCellExperiment object, meaning one can use the plot function whenever necessary.

(a) tSNE

sce <- runTSNE(sce)
reducedDimNames(sce)
plotTSNE(sce, colour_by="label", text_by="label")

tSNE embedding of scRNA-Seq data.

(b) MDS

sce <- runMDS(sce)
reducedDimNames(sce)
plotMDS(sce, colour_by="label", text_by="label")

MDS embedding of scRNA-Seq data.

(c) UMAP

sce <- runUMAP(sce) # Note, the UMAP embedding is already stored in downloaded SingleCellExperiment object by authers. So one can just use this one or recompute it. 
reducedDimNames(sce)
plotUMAP(sce, colour_by="label", text_by="label")

UMAP embedding of scRNA-Seq data.

(d) PCA

PCA result plotted for first two components.

sce <- runPCA(sce) 
reducedDimNames(sce)
plotPCA(sce, colour_by="label", text_by="label")

PCA embedding of scRNA-Seq data.

Multiple components can be plotted in a series of pairwise plots. When more than two components are plotted, the diagonal boxes in the scatter plot matrix show the density for each component.

sce <- runPCA(sce, ncomponents=20) 
reducedDimNames(sce)
plotPCA(sce, colour_by="label", text_by="label", ncomponents = 4)

PCA embedding of scRNA-Seq data for multiple components.

Version Information

sessionInfo()

R version 4.5.3 (2026-03-11)
Platform: x86_64-pc-linux-gnu
Running under: Debian GNU/Linux 12 (bookworm)

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Los_Angeles
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] scater_1.38.1               ggplot2_4.0.2               scran_1.38.1                scuttle_1.20.0              SingleCellExperiment_1.32.0 SummarizedExperiment_1.40.0 Biobase_2.70.0             
 [8] GenomicRanges_1.62.1        Seqinfo_1.0.0               IRanges_2.44.0              S4Vectors_0.48.0            BiocGenerics_0.56.0         generics_0.1.4              MatrixGenerics_1.22.0      
[15] matrixStats_1.5.0          

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    viridisLite_0.4.3   dplyr_1.2.0         vipor_0.4.7         farver_2.1.2        viridis_0.6.5       S7_0.2.1            fastmap_1.2.0       bluster_1.20.0      digest_0.6.39      
[11] rsvd_1.0.5          lifecycle_1.0.5     cluster_2.1.8.2     statmod_1.5.1       magrittr_2.0.4      compiler_4.5.3      rlang_1.1.7         tools_4.5.3         igraph_2.2.2        yaml_2.3.12        
[21] knitr_1.51          FNN_1.1.4.1         S4Arrays_1.10.1     labeling_0.4.3      dqrng_0.4.1         htmlwidgets_1.6.4   DelayedArray_0.36.0 RColorBrewer_1.1-3  abind_1.4-8         BiocParallel_1.44.0
[31] Rtsne_0.17          withr_3.0.2         grid_4.5.3          beachmat_2.26.0     edgeR_4.8.2         scales_1.4.0        dichromat_2.0-0.1   cli_3.6.5           rmarkdown_2.30      otel_0.2.0         
[41] metapod_1.18.0      RSpectra_0.16-2     ggbeeswarm_0.7.3    parallel_4.5.3      XVector_0.50.0      vctrs_0.7.1         Matrix_1.7-4        jsonlite_2.0.0      BiocSingular_1.26.1 BiocNeighbors_2.4.0
[51] ggrepel_0.9.6       irlba_2.3.7         beeswarm_0.4.0      locfit_1.5-9.12     limma_3.66.0        glue_1.8.0          codetools_0.2-20    cowplot_1.2.0       uwot_0.2.4          gtable_0.3.6       
[61] ScaledMatrix_1.18.0 tibble_3.3.1        pillar_1.11.1       htmltools_0.5.9     R6_2.6.1            evaluate_1.0.5      lattice_0.22-9      Rcpp_1.1.1          gridExtra_2.3       SparseArray_1.10.8 
[71] xfun_0.56           pkgconfig_2.0.3    

References

Amezquita, Robert A, Aaron T L Lun, Etienne Becht, Vince J Carey, Lindsay N Carpp, Ludwig Geistlinger, Federico Marini, et al. 2020. “Orchestrating single-cell analysis with Bioconductor.” Nat. Methods 17 (2): 137–45. https://doi.org/10.1038/s41592-019-0654-x.

Aztekin, C, T W Hiscock, J C Marioni, J B Gurdon, B D Simons, and J Jullien. 2019. “Identification of a regeneration-organizing cell in the Xenopus tail.” Science 364 (6441): 653–58. https://doi.org/10.1126/science.aav9996.

Donaldson, Justin, and Maintainer Justin Donaldson. 2010. “Package ‘Tsne’.” CRAN Repository.

Duò, Angelo, Mark D Robinson, and Charlotte Soneson. 2018. “A systematic performance evaluation of clustering methods for single-cell RNA-seq data.” F1000Res. 7 (July): 1141. https://doi.org/10.12688/f1000research.15666.3.

Howard, Brian E, Qiwen Hu, Ahmet Can Babaoglu, Manan Chandra, Monica Borghi, Xiaoping Tan, Luyan He, et al. 2013. “High-Throughput RNA Sequencing of Pseudomonas-Infected Arabidopsis Reveals Hidden Transcriptome Complexity and Novel Splice Variants.” PLoS One 8 (10): e74183. https://doi.org/10.1371/journal.pone.0074183.

Kiselev, Vladimir Yu, Kristina Kirschner, Michael T Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar N Natarajan, et al. 2017. “SC3: consensus clustering of single-cell RNA-seq data.” Nat. Methods 14 (5): 483–86. https://doi.org/10.1038/nmeth.4236.

Linderman, George C, Manas Rachh, Jeremy G Hoskins, Stefan Steinerberger, and Yuval Kluger. 2019. “Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data.” Nat. Methods 16 (3): 243–45. https://doi.org/10.1038/s41592-018-0308-4.

McInnes, Leland, John Healy, and James Melville. 2018. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction,” February. http://arxiv.org/abs/1802.03426.

Senabouth, Anne, Samuel W Lukowski, Jose Alquicira Hernandez, Stacey B Andersen, Xin Mei, Quan H Nguyen, and Joseph E Powell. 2019. “ascend: R package for analysis of single-cell RNA-seq data.” Gigascience 8 (8). https://doi.org/10.1093/gigascience/giz087.

Sun, Shiquan, Jiaqiang Zhu, Ying Ma, and Xiang Zhou. 2019. “Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis.” Genome Biol. 20 (1): 269. https://doi.org/10.1186/s13059-019-1898-6.

Sun, Shiquan, Jiaqiang Zhu, and Xiang Zhou. 2020. “Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies.” Nat. Methods, January. https://doi.org/10.1038/s41592-019-0701-7.