GSVA on single-cell RNA-seq data
28 July 2026
Abstract
Here we illustrate how to use GSVA with single-cell RNA sequencing (scRNA-seq) data.
Package
GSVA 2.6.5
License: Artistic-2.0
1 Introduction
GSVA provides now specific support for single-cell data in the algorithm
that runs through the gsvaParam() parameter constructor, and originally
described in the publication by Hänzelmann, Castelo, and Guinney (2013). At the moment, this
specific support consists of the following features:
- The input expression data can be stored in different types of data
containers prepared to store sparse single-cell data. These types of
sparse data containers can be broadly categorized in those that only
store the expression values, and those that may store additional row
and column metadata. The currently available value-only containers for
input are
dgCMatrix,SVT_SparseArray, andDelayedMatrix. The currently available container for single-cell data that allows one to input additional row and column metadata is aSingleCellExperimentobject. - While the input single-cell data is always sparse, the output of enrichment
scores will be always dense, and therefore, the container storing those
scores will be different from the input data, typically a
matrixor a denseDelayedMatrixobject. The latter will be particularly used when the total number of values exceeds 2^31, which is the largest 32-bit standard integer value in R. - By default, when the input expression data is stored in a sparse data
container, as it typically happens with single-cell data, then a slightly
a slightly modified GSVA algorithm will run, if GSVA is the choice of
algorithm, by which nonzero values are treated differently from zero values,
leading to slightly different results than those obtained by applying the
classical GSVA algorithm. If we set the parameter
sparse=FALSEin the call togsvaParam(), the classical GSVA algorithm will be used, which for a typical single-cell data set will result in longer running times and larger memory consumption than running it in the default sparse regime for this type of data. - The GSVA algorithm can be run either at once through a call to
gsva()with a parameter object or in two steps: (1) row normalization and column rank transformation withgsvaRanks(); and (2) column enrichment scores calculation withgsvaScores(). Splitting the GSVA algorithm into these two steps allows one to reuse the output of the first step, which is independent of the gene sets, to calculate enrichment scores for different collections of gene sets, without having to repeat the first step.
In what follows, we will illustrate the use of GSVA on a publicly available single-cell transcriptomics data set of peripheral blood mononuclear cells (PBMCs) published by Zheng et al. (2017).
2 Import data
We import the PBMC data using the TENxPBMCData package, as a
SingleCellExperiment object, defined in the
SingleCellExperiment package.
library(SingleCellExperiment)
library(TENxPBMCData)
sce <- TENxPBMCData(dataset="pbmc4k")
sce
class: SingleCellExperiment
dim: 33694 4340
metadata(0):
assays(1): counts
rownames(33694): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
ENSG00000268674
rowData names(3): ENSEMBL_ID Symbol_TENx Symbol
colnames: NULL
colData names(11): Sample Barcode ... Individual Date_published
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):3 Quality assessment and pre-processing
Here, we perform a quality assessment and pre-processing steps using the package scuttle (McCarthy et al. 2017). We start identifying mitochondrial genes.
library(scrapper)
is_mito <- grepl("^MT-", rowData(sce)$Symbol_TENx)
table(is_mito)
is_mito
FALSE TRUE
33681 13 Calculate quality control (QC) metrics and filter out low-quality cells.
sce <- quickRnaQc.se(sce, subsets=list(mito=is_mito))
sce <- sce[, sce$keep]
dim(sce)
[1] 33694 4147We filter out genes that are expressed in less than 1% of the cells.
cellsxgene <- rowSums(counts(sce) > 0)
sce <- sce[cellsxgene > floor(ncol(sce)*0.01), ]
dim(sce)
[1] 10799 4147Calculate library size factors and normalized units of expression in logarithmic scale.
sce <- normalizeRnaCounts.se(sce, size.factors=sce$sum)
assayNames(sce)
[1] "counts" "logcounts"4 Annotate cell types using GSVA
Here, we illustrate how to annotate cell types in the PBMC data using GSVA.
4.1 Read gene sets in GMT format
First, we fetch a collection of 22 leukocyte gene set signatures, containing
a total 547 genes, which should help to distinguish among 22 mature human
hematopoietic cell type populations isolated from peripheral blood or
in vitro culture conditions, including seven T cell types: naïve and memory B
cells, plasma cells, NK cell, and myeloid subsets. These gene sets have been
used in the benchmarking publication by Diaz-Mejia et al. (2019), and were
originally compiled by the CIBERSORT
developers, where they called it the LM22 signature (Newman et al. 2015). The
LM22 signature is stored in the GSVAdata experiment data
package as a compressed text file in
GMT format, which can
be read into R using the readGMT() function from the GSVA
package, and will return the gene sets into a GeneSetCollection object,
defined in the GSEABase package.
library(GSEABase)
library(GSVA)
fname <- file.path(system.file("extdata", package="GSVAdata"),
"pbmc_cell_type_gene_set_signatures.gmt.gz")
gsets <- readGMT(fname)
gsets
GeneSetCollection
names: B_CELLS_MEMORY, B_CELLS_NAIVE, ..., T_CELLS_REGULATORY_TREGS (22 total)
unique identifiers: AIM2, BANK1, ..., SKAP1 (248 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: NullCollection (1 total)4.2 Add gene identifier type metadata
Note that while gene identifers in the sce object correspond to
Ensembl stable identifiers
(ENSG...), the gene identifiers in the gene sets are
HGNC gene symbols. This, in principle, precludes
matching directly what gene in the single-cell data object sce corresponds to
what gene set in the GeneSetCollection object gsets. However, the
GSVA package can do that matching as long as the appropriate
metadata is present in both objects.
In the case of a GeneSetCollection object, its geneIdType metadata slot
stores the type of gene identifier. In the case of a SingleCellExperiment
object, such as the previous sce object, such metadata is not present.
However, using the function gsvaAnnotation() from the GSVA
package, and the helper function ENSEMBLIdentifier() from the
GSEABase package, we add such metadata to the sce object as
follows.
gsvaAnnotation(sce) <- ENSEMBLIdentifier("org.Hs.eg.db")
gsvaAnnotation(sce)
geneIdType: ENSEMBL (org.Hs.eg.db)4.3 Build parameter object
We first build a parameter object using the function gsvaParam(). By
default, the expression values in the logocounts assay will be selected for
downstream analysis.
gsvapar <- gsvaParam(sce, gsets)
gsvapar
A GSVA::gsvaParam object
expression data:
class: SingleCellExperiment
dim: 10799 4147
metadata(2): qc annotation
assays(2): counts logcounts
rownames(10799): ENSG00000279457 ENSG00000228463 ... ENSG00000273748
ENSG00000278817
rowData names(3): ENSEMBL_ID Symbol_TENx Symbol
colnames: NULL
colData names(16): Sample Barcode ... keep sizeFactor
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
using assay: logcounts
using annotation:
geneIdType: ENSEMBL (org.Hs.eg.db)
gene sets:
GeneSetCollection
names: B_CELLS_MEMORY, B_CELLS_NAIVE, ..., T_CELLS_REGULATORY_TREGS (22 total)
unique identifiers: AIM2, BANK1, ..., SKAP1 (248 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: NullCollection (1 total)
gene set size: [1, Inf]
nonzero values: less than 2^31 (INT_MAX)
ondisk: auto
kcdf: auto
kcdfNoneMinSampleSize: 200
tau: 1
maxDiff: TRUE
absRanking: FALSE
sparse: TRUE
checkNA: auto
missing data: didn't check
filterRows: TRUE 4.4 Calculate GSVA scores
While at this point, we could already run the entire GSVA algorithm with a call
to the gsva(gsvapar) function. We show here how to do it in two steps.
First we calculate GSVA rank values using the function gsvaRanks().
gsvaranks <- gsvaRanks(gsvapar)
gsvaranks
A GSVA::gsvaRanksParam object
expression data:
class: SingleCellExperiment
dim: 10799 4147
metadata(2): qc annotation
assays(3): counts logcounts gsvaranks
rownames(10799): ENSG00000279457 ENSG00000228463 ... ENSG00000273748
ENSG00000278817
rowData names(3): ENSEMBL_ID Symbol_TENx Symbol
colnames: NULL
colData names(16): Sample Barcode ... keep sizeFactor
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
using assay: gsvaranks
using annotation:
geneIdType: ENSEMBL (org.Hs.eg.db)
gene sets:
GeneSetCollection
names: B_CELLS_MEMORY, B_CELLS_NAIVE, ..., T_CELLS_REGULATORY_TREGS (22 total)
unique identifiers: AIM2, BANK1, ..., SKAP1 (248 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: NullCollection (1 total)
gene set size: [1, Inf]
nonzero values: less than 2^31 (INT_MAX)
ondisk: auto
kcdf: auto
kcdfNoneMinSampleSize: 200
tau: 1
maxDiff: TRUE
absRanking: FALSE
sparse: TRUE
checkNA: auto
missing data: didn't check
filterRows: TRUE Second, we calculate the GSVA scores using the output of gsvaRanks() as input
to the function gsvaScores(). By default, this function will calculate the
scores for all gene sets specified in the input parameter object.
es <- gsvaScores(gsvaranks)
es
class: SingleCellExperiment
dim: 22 4147
metadata(1): qc
assays(1): es
rownames(22): B_CELLS_MEMORY B_CELLS_NAIVE ... T_CELLS_GAMMA_DELTA
T_CELLS_REGULATORY_TREGS
rowData names(1): gs
colnames: NULL
colData names(16): Sample Barcode ... keep sizeFactor
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):However, we could calculate the scores for another collection of gene sets by
updating them in the gsvaranks object as follows.
geneSets(gsvaranks) <- geneSets(gsvapar)[1:2]
es2 <- gsvaScores(gsvaranks)4.5 Using GSVA scores to assign cell types
Following Amezquita et al. (2020), and some of the steps described in
“Chapter 5 Clustering” of the first version of the
OSCA book,
we use GSVA scores to build a nearest-neighbor graph of the cells using the
function buildSNNGraph() from the scran
package (Lun, McCarthy, and Marioni 2016). The parameter k in the call to buildSNNGraph()
specifies the number of nearest neighbors to consider during graph
construction, and here we set k=20 because it leads to a number of clusters
close to the expected number of cell types.
library(bluster)
g <- makeSNNGraph(t(assay(es)), k=20)Second, we use the function cluster_walktrap() from the igraph
package (Csardi and Nepusz 2006), to cluster cells by finding densely connected
subgraphs. We store the resulting vector of cluster indicator values into the
sce object using the function colLabels().
library(igraph)
colLabels(es) <- factor(cluster_walktrap(g)$membership)
table(colLabels(es))
1 2 3 4 5 6 7 8
485 345 714 1047 600 220 197 539 Similarly to Diaz-Mejia et al. (2019), we apply a simple cell type assignment
algorithm, which consists of selecting at each cell the gene set with highest
GSVA score, tallying the selected gene sets per cluster, and assigning to the
cluster the most frequent gene set, storing that assignment into the sce
object with the function colLabels().
whmax <- apply(assay(es), 2, which.max)
gsxlab <- split(rownames(es)[whmax], colLabels(es))
gsxlab <- names(sapply(sapply(gsxlab, table), which.max))
colLabels(es) <- factor(gsub("[0-9]\\.", "", gsxlab))[colLabels(es)]
table(colLabels(es))
B_CELLS_MEMORY MONOCYTES NK_CELLS_RESTING T_CELLS_CD4_NAIVE
600 1059 220 1783
T_CELLS_CD8
485 We can visualize the cell type assignments by projecting cells dissimilarity in two dimensions with a principal components analysis (PCA) on the GSVA scores, and coloring cells using the previously assigned clusters.
library(RColorBrewer)
res <- prcomp(assay(es))
varexp <- res$sdev^2 / sum(res$sdev^2)
nclusters <- nlevels(colLabels(es))
hmcol <- colorRampPalette(brewer.pal(nclusters, "Set1"))(nclusters)
par(mar=c(4, 5, 1, 1))
plot(res$rotation[, 1], res$rotation[, 2], col=hmcol[colLabels(es)], pch=19,
xlab=sprintf("PCA 1 (%.0f%%)", varexp[1]*100),
ylab=sprintf("PCA 2 (%.0f%%)", varexp[2]*100),
las=1, cex.axis=1.2, cex.lab=1.5)
legend("topright", gsub("_", " ", levels(colLabels(es))), fill=hmcol, inset=0.01)
Figure 1: Cell type assignments of PBMC scRNA-seq data, based on GSVA scores
Finally, if we want to better understand why a specific cell type is annotated
to a given cell, we can use the gsvaEnrichment() function, which will show
a GSEA enrichment plot. This function takes as input the output of
gsvaRanks(), a given column (cell) in the input singl-cell data, and a given
gene set. In Figure 2 below, we show such a plot for the
first cell annotated to the monocytes cell type.
firstmonocytecell <- which(colLabels(es) == "MONOCYTES")[1]
par(mar=c(4, 5, 1, 1))
gsvaEnrichment(gsvaranks, column=firstmonocytecell, geneSet="MONOCYTES",
cex.axis=1.2, cex.lab=1.5, plot="ggplot")
Figure 2: GSVA enrichment plot of the MONOCYTES gene set in the expression profile of the first cell annotated to that cell type
In the previous call to gsvaEnrichment() we used the argument plot="ggplot"
to produce a plot with the ggplot2
package. By default, if we call gsvaEnrichment() interactively, it will
produce a plot using “base R”, but either when we do it non-interactively, or
when we set plot="no" it will return a data.frame object with the enrichment
data.
5 Stability of GSVA scores with sparse data
This version of GSVA (>= 2.6.4) fixes a known problem with the stability of GSVA scores with sparse data. More concretely, for a given gene set, its genes with a zero count expression value in a given column/cell, would occupy arbitrary positions in the gene ranking of that column/cell. This, and newer versions of GSVA, place those genes evenly distributed after the nonzerovalues along the ranking. We can see this happening with the MONOCYTES gene set in Figure 2, where the genes of this gene set with zero expression values are evenly distributed after the left vertical dashed line that in this plot indicates the largest positive leading edge of the enrichment score. We can also verify that those genes have zero expression values in the first cell annotated to the MONOCYTES cell type, as follows.
edat <- gsvaEnrichment(gsvaranks, column=firstmonocytecell,
geneSet="MONOCYTES", plot="no")
unname(counts(sce)[edat$gsetidx, firstmonocytecell][order(edat$gsetrnk)])
[1] 4 5 6 6 6 1 1 5 2 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0Note that the nonzero count values are not perfectly sorted in decreasing order,
because rankings are calculated on a expression statistic after column and row
normalization. Using the diff() function, which calculates differences between
consecutive elements of a vector, we can also verify how exactly these zeros of
the gene set are evenly spaced.
diff(sort(edat$gsetrnk))
[1] 83 27 148 62 197 48 8 109 36 66 59 3 133 887 888 887 887 887 888
[20] 887 887 887 888 887Finally, we can verify that this strategy results in stable GSVA scores, by
running the GSVA algorithm on a random permutation of the rows of the sce
object, and checking that the resulting GSVA scores are nearly identical to
those obtained with the original sce object.
gsvapar <- gsvaParam(sce[sample(1:nrow(sce)), ], gsets, verbose=FALSE)
es2 <- gsva(gsvapar, verbose=FALSE)
all.equal(assay(es), assay(es2))
[1] "Mean relative difference: 0.0006654944"The tiny differences in the enrichment scores (less than 1e-3 in these data) are due to the nonzero values in gene sets that are tied in the ranking of the internally calculated expression statistic.
6 Benchmarking
We are still benchmarking and testing this version of GSVA for single-cell data. If you encounter problems or have suggestions, do not hesitate to contact us by opening an issue in the GSVA GitHub repo.
Session information
Here is the output of sessionInfo() on the system on which this document was
compiled running pandoc 2.7.3:
sessionInfo()
R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.23-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
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[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] igraph_2.3.3 bluster_1.22.0
[3] scrapper_1.6.3 TENxPBMCData_1.30.0
[5] HDF5Array_1.40.0 h5mread_1.4.0
[7] rhdf5_2.56.0 DelayedArray_0.38.2
[9] SparseArray_1.12.2 S4Arrays_1.12.0
[11] abind_1.4-8 Matrix_1.7-6
[13] SingleCellExperiment_1.34.0 sva_3.60.0
[15] BiocParallel_1.46.0 genefilter_1.94.0
[17] mgcv_1.9-4 nlme_3.1-170
[19] RColorBrewer_1.1-3 edgeR_4.10.1
[21] limma_3.68.4 GSVAdata_1.48.1
[23] SummarizedExperiment_1.42.0 GenomicRanges_1.64.0
[25] Seqinfo_1.2.0 MatrixGenerics_1.24.0
[27] matrixStats_1.5.0 GSEABase_1.74.0
[29] graph_1.90.0 annotate_1.90.0
[31] XML_3.99-0.23 org.Hs.eg.db_3.23.1
[33] AnnotationDbi_1.74.0 IRanges_2.46.0
[35] S4Vectors_0.50.1 Biobase_2.72.0
[37] BiocGenerics_0.58.1 generics_0.1.4
[39] GSVA_2.6.5 BiocStyle_2.40.0
loaded via a namespace (and not attached):
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[3] rlang_1.3.0 magrittr_2.0.5
[5] otel_0.2.0 compiler_4.6.1
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[69] ScaledMatrix_1.20.0 locfit_1.5-9.12
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[83] htmltools_0.5.9 lifecycle_1.0.5
[85] httr_1.4.8 statmod_1.5.2
[87] bit64_4.8.2 References
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Diaz-Mejia, J Javier, Elaine C Meng, Alexander R Pico, Sonya A MacParland, Troy Ketela, Trevor J Pugh, Gary D Bader, and John H Morris. 2019. “Evaluation of Methods to Assign Cell Type Labels to Cell Clusters from Single-Cell Rna-Sequencing Data.” F1000Research 8: ISCB–Comm.
Hänzelmann, Sonja, Robert Castelo, and Justin Guinney. 2013. “GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data.” BMC Bioinformatics 14: 7. https://doi.org/10.1186/1471-2105-14-7.
Lun, Aaron TL, Davis J McCarthy, and John C Marioni. 2016. “A Step-by-Step Workflow for Low-Level Analysis of Single-Cell Rna-Seq Data with Bioconductor.” F1000Research 5: 2122.
McCarthy, Davis J, Kieran R Campbell, Aaron TL Lun, and Quin F Wills. 2017. “Scater: Pre-Processing, Quality Control, Normalization and Visualization of Single-Cell Rna-Seq Data in R.” Bioinformatics 33 (8): 1179–86.
Newman, Aaron M, Chih Long Liu, Michael R Green, Andrew J Gentles, Weiguo Feng, Yue Xu, Chuong D Hoang, Maximilian Diehn, and Ash A Alizadeh. 2015. “Robust Enumeration of Cell Subsets from Tissue Expression Profiles.” Nature Methods 12 (5): 453–57.
Zheng, Grace XY, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W Bent, Ryan Wilson, Solongo B Ziraldo, et al. 2017. “Massively Parallel Digital Transcriptional Profiling of Single Cells.” Nature Communications 8 (1): 14049.