fast.ssgsea is an R package (R Core Team 2024) for
fast Single-Sample Gene Set Enrichment Analysis (ssGSEA) and
Post-Translational Modification Signature Enrichment Analysis (PTM-SEA)
(Barbie et al. 2009; Krug et al.
2019).
In R (>= 4.0.0), run the following to install.
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("pnnl/fast.ssgsea")The package consists of a single user-facing function, fast_ssgsea,
that accepts a numeric matrix with genes or other molecules as rows and
either samples, contrasts, or some other meaningful representation of
the data as columns. A named list of gene sets (more generally,
molecular signatures) is also required. Other arguments control the
behavior of ssGSEA/PTM-SEA, and they are described in the function
documentation.
We will simulate a matrix with 10,000 genes as rows and 100 samples as columns. Then, we generate 20,000 gene sets by randomly sampling between 10 and 500 genes from the matrix row names.
n_genes <- 10000L # number of genes
n_samples <- 100L # number of samples
genes <- paste0("gene", seq_len(n_genes))
samples <- paste0("sample", seq_len(n_samples))
## Simulate matrix of sample gene expression values
set.seed(9001L)
X <- matrix(data = rnorm(n = n_genes * n_samples),
nrow = n_genes,
ncol = n_samples,
dimnames = list(genes, samples))
## Simulate list of gene sets
n_sets <- 20000L # number of gene sets
min_size <- 10L # size of smallest gene set
max_size <- 500L # size of largest gene set
size_range <- max_size - min_size + 1L
n_reps <- ceiling(n_sets / size_range)
set_sizes <- rep(max_size:min_size, times = n_reps)[seq_len(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 with the reference BLAS library
(single-threaded) running on an AMD Ryzen 5 7600X CPU with a clock speed
of 4.7 GHz.
library(fast.ssgsea)
# Runtime (elapsed time)
system.time({
res <- fast_ssgsea(
X = X,
gene_sets = gene_sets,
alpha = 1,
nperm = 1000L,
batch_size = 1000L,
adjust_globally = FALSE,
min_size = min_size,
sort = TRUE,
seed = 0L
)
})## user system elapsed
## 28.919 0.482 24.836
str(res)## 'data.frame': 2000000 obs. of 9 variables:
## $ sample : Factor w/ 100 levels "sample1","sample2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ set : chr "set3049" "set2620" "set8425" "set16760" ...
## $ set_size : int 398 336 423 435 391 301 301 458 440 454 ...
## $ ES : num 948 968 870 842 848 ...
## $ NES : num 4.41 4.09 4.13 4.14 3.89 ...
## $ n_same_sign : int 543 539 539 537 536 535 535 533 533 530 ...
## $ n_as_extreme: int 0 0 0 0 0 0 0 0 0 0 ...
## $ p_value : num 0.00184 0.00185 0.00185 0.00186 0.00186 ...
## $ adj_p_value : num 0.796 0.796 0.796 0.796 0.796 ...
print(sessionInfo(), locale = FALSE, tzone = FALSE)## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Linux Mint 22.1
##
## 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] fast.ssgsea_0.1.0
##
## loaded via a namespace (and not attached):
## [1] dqrng_0.4.1 digest_0.6.37 RcppArmadillo_14.6.0-1
## [4] fastmap_1.2.0 xfun_0.52 Matrix_1.7-3
## [7] lattice_0.22-5 knitr_1.50 htmltools_0.5.8.1
## [10] rmarkdown_2.29 cli_3.6.5 grid_4.5.1
## [13] data.table_1.17.8 compiler_4.5.1 rstudioapi_0.17.1
## [16] tools_4.5.1 evaluate_1.0.4 Rcpp_1.1.0
## [19] yaml_2.3.10 rlang_1.1.6
The fast.ssgsea R package utilizes linear algebra and ideas from Fast
Gene Set Enrichment Analysis (Korotkevich et al.
2021) to greatly reduce the runtime of
ssGSEA and PTM-SEA while also properly controlling the type I error
rate.
Tests were performed on a desktop computer with an AMD Ryzen 5 7600X CPU
(6 cores, 12 threads) at 4.7 GHz. Different combinations of the number
of samples, gene sets, maximum gene set size, number of permutations,
and value of the
Runtime of fast_ssgsea with A) 1,000 or B) 10,000 permutations. R was linked to the default reference BLAS library, so only a single thread was used.
Linking R to an optimized Basic Linear Algebra Subprograms (BLAS) library (Lawson et al. 1979), such as the open-source OpenBLAS library (Xianyi, Qian, and Yunquan 2012; Wang et al. 2013), can reduce the runtime even further:
Runtime of fast_ssgsea with A) 1,000 or B) 10,000 permutations. R was linked to the optimized OpenBLAS library, and all 12 threads were used.
Barbie, David A., Pablo Tamayo, Jesse S. Boehm, So Young Kim, Susan E. Moody, Ian F. Dunn, Anna C. Schinzel, 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, Peter Hornbeck, Rajesh Raju, Rushdy Ahmad, Matthew Szucs, 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.
Lawson, C. L., R. J. Hanson, D. R. Kincaid, and F. T. Krogh. 1979. “Basic Linear Algebra Subprograms for Fortran Usage.” ACM Trans. Math. Softw. 5 (3): 308–23. https://doi.org/10.1145/355841.355847.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wang, Qian, Xianyi Zhang, Yunquan Zhang, and Qing Yi. 2013. “AUGEM: Automatically Generate High Performance Dense Linear Algebra Kernels on X86 CPUs.” In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. SC ’13. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2503210.2503219.
Xianyi, Zhang, Wang Qian, and Zhang Yunquan. 2012. “Model-Driven Level 3 BLAS Performance Optimization on Loongson 3A Processor.” In 2012 IEEE 18th International Conference on Parallel and Distributed Systems, 684–91. https://doi.org/10.1109/ICPADS.2012.97.