NOTICE: While this R package was based on the ssGSEA2.0 repository, neither perform single-sample Gene Set Enrichment Analysis (ssGSEA) as originally described by Barbie, et al. (Barbie et al. 2009). They are instead modifications of pre-ranked GSEA that calculate the enrichment score (ES) differently and support testing directional gene sets (details below). The package and fast-ssGSEA name will be changed in the future.
fast.ssgsea is an R package (R Core Team 2026) for
a highly optimized variant of pre-ranked Gene Set Enrichment Analysis
(GSEA) (Subramanian et al. 2005). Unlike
standard GSEA, fast-ssGSEA is capable of testing gene sets where each
gene has an expected direction of change (up- or down-regulation;
indicated by appending a “;u” or “;d” to the end of every gene in a set)
from a prior experiment.
fast-ssGSEA is based on Post-Translational Modification Signature Enrichment Analysis (PTM-SEA) (Krug et al. 2019), and it borrows optimization techniques from the simple implementation of Fast Gene Set Enrichment Analysis (FGSEA-simple) (Korotkevich et al. 2021).
The primary function, fast_ssgsea, accepts a vector of signed
statistics with genes or other molecules as names. The values must be
approximately symmetric around zero, with more extreme values indicating
greater importance. A named list of gene sets (more generally, molecular
signatures) is also required. Other arguments control the behavior of
fast-ssGSEA, and they are described in the function documentation.
The package also contains a read_gmt function, which reads a Gene
Matrix Transposed (GMT) file to construct a named list of gene sets for
use with fast_ssgsea.
R version 4.0.0 or greater is required to install fast.ssgsea.
A macOS binary is provided in the latest
release. Users looking to
build and install the development version of fast.ssgsea must have the
Xcode developer tools from Apple. See https://mac.r-project.org/tools/
for instructions.
No Windows binary is available, so
Rtools must be
installed to compile C and C++ code. Then, the development version of
fast.ssgsea can be installed with the code below.
Most Linux distributions come pre-packaged with tools to compile C and
C++ code, so no extra work is needed. Users can install the development
version of fast.ssgsea on Linux by running the code below.
The development version of fast.ssgsea can be installed with either of
the following
# install.packages("pak")
pak::pak("pnnl/fast.ssgsea")# install.packages("renv")
renv::install("pnnl/fast.ssgsea")We will simulate a vector of 10,000 signed gene-level statistics. We will also simulate 20,000 gene sets by randomly sampling between 5 and 1,000 genes.
n_genes <- 10000L # number of genes
genes <- paste0("gene", seq_len(n_genes))
# Simulate named vector of gene-level values
set.seed(9001L)
stats <- rnorm(n = n_genes)
names(stats) <- genes
# Simulate list of gene sets
n_sets <- 20000L
min_size <- 5L
max_size <- 1000L
set_sizes <- rep(max_size:min_size, length.out = n_sets)
gene_sets <- lapply(seq_len(n_sets), function(i) {
set.seed(i)
sample(x = genes, size = set_sizes[i])
})
names(gene_sets) <- paste0("set", seq_along(gene_sets))This shows the runtime of fast_ssgsea on an AMD Ryzen 5 7600X CPU with
a clock speed of 4.7 GHz. A total of 100,000 permutations were used to
calculate P-values and normalized enrichment scores (NES).
library(fast.ssgsea)
# Runtime (in seconds)
system.time({
res <- fast_ssgsea(
stats = stats,
gene_sets = gene_sets,
alpha = 1,
nperm = 1e5L,
min_size = min_size,
seed = 0L
)
})## user system elapsed
## 0.972 0.083 0.978
str(res)## 'data.frame': 20000 obs. of 8 variables:
## $ set : chr "set18791" "set2830" "set19084" "set18223" ...
## $ set_size : int 138 163 841 706 801 87 503 409 320 450 ...
## $ ES : num -1866 1584 698 759 709 ...
## $ NES : num -5.34 4.78 4.66 4.67 4.62 ...
## $ n_same_sign : int 49235 51108 52907 52785 52814 50462 52351 51847 51728 47860 ...
## $ n_as_extreme: int 1 3 8 9 12 12 16 19 19 19 ...
## $ p_value : num 4.06e-05 7.83e-05 1.70e-04 1.89e-04 2.46e-04 ...
## $ adj_p_value : num 0.783 0.783 0.836 0.836 0.836 ...
print(sessionInfo(), locale = FALSE, tzone = FALSE)## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Linux Mint 22.3
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dqrng_0.4.1 fast.ssgsea_0.1.0.9035
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.39 collapse_2.1.7 fastmap_1.2.0 xfun_0.57
## [5] parallel_4.6.0 knitr_1.51 htmltools_0.5.9 rmarkdown_2.31
## [9] cli_3.6.6 data.table_1.18.4 compiler_4.6.0 rstudioapi_0.18.0
## [13] tools_4.6.0 evaluate_1.0.5 Rcpp_1.1.1-1.1 yaml_2.3.12
## [17] otel_0.2.0 rlang_1.2.0
Benchmarking was performed on a desktop computer with an AMD Ryzen 5
7600X CPU (4.7 GHz), single threaded, to measure the runtime of
fast-ssGSEA (fast.ssgsea::fast_ssgsea) and FGSEA-simple
(fgsea::fgseaSimple). Different combinations of the number of gene
sets, maximum gene set size, and the number of permutations (
Runtime of fast_ssgsea with 10,000, 100,000, or 1,000,000 permutations.
Like fast-ssGSEA, FGSEA-simple relies purely on the number of permutations to calculate p-values, which limits how small they can become. While FGSEA-simple is meant to be run with a smaller number of permutations and followed up by FGSEA-multilevel (the method capable of calculating arbitrarily small p-values) (Korotkevich et al. 2021), these results serve to illustrate the extreme difference in runtime between the two approaches. This difference is largely the result of changes to how the ES is defined.
Runtime of fgsea::fgseaSimple with 10,000, 100,000, or 1,000,000 permutations.
Barbie, David A., Pablo Tamayo, Jesse S. Boehm, et al. 2009. “Systematic RNA Interference Reveals That Oncogenic KRAS-Driven Cancers Require TBK1.” Nature 462 (7269): 108–12. https://doi.org/10.1038/nature08460.
Korotkevich, Gennady, Vladimir Sukhov, Nikolay Budin, Boris Shpak, Maxim N. Artyomov, and Alexey Sergushichev. 2021. Fast Gene Set Enrichment Analysis. bioRxiv. https://doi.org/10.1101/060012.
Krug, Karsten, Philipp Mertins, Bin Zhang, et al. 2019. “A Curated Resource for Phosphosite-Specific Signature Analysis.” Molecular & Cellular Proteomics 18 (3): 576–93. https://doi.org/10.1074/mcp.TIR118.000943.
R Core Team. 2026. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://doi.org/10.32614/R.manuals.
Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.