This function estimates gene-set enrichment scores across all samples using various methods.
Usage
RunGSVA(
exp,
gene.list,
min.size = 3,
max.size = 1000,
method = "ssgsea",
ssgsea.normalize = TRUE,
ssgsea.alpha = 0.25,
gsva.kcdf = "Gaussian",
gsva.tau = 1,
gsva.maxDiff = TRUE,
gsva.absRanking = FALSE,
verbose = TRUE,
nCores = parallel::detectCores() - 3
)Arguments
- exp
Numeric matrix containing the expression data or gene expression signatures, with samples in columns and genes in rows.
- gene.list
Gene sets provided either as a list object or as a GeneSetCollection object.
- min.size
Minimum size of the gene sets to be considered in the analysis. Default is 3.
- max.size
Maximum size of the gene sets to be considered in the analysis. Default is 1000.
- method
Method to employ in the estimation of gene-set enrichment scores per sample. Options are "gsva" (default), "ssgsea", "zscore", or "plage".
- ssgsea.normalize
Logical vector of length 1; if TRUE runs the ssGSEA method from Barbie et al. (2009) normalizing the scores by the absolute difference between the minimum and the maximum, as described in their paper. Otherwise this last normalization step is skipped.
- ssgsea.alpha
Numeric vector of length 1. The exponent defining the weight of the tail in the random walk performed by the ssGSEA (Barbie et al., 2009) method. The default value is 0.25 as described in the paper.
- gsva.kcdf
Character vector of length 1 denoting the kernel to use during the non-parametric estimation of the cumulative distribution function of expression levels across samples. By default, kcdf="Gaussian" which is suitable when input expression values are continuous, such as microarray fluorescent units in logarithmic scale, RNA-seq log-CPMs, log-RPKMs or log-TPMs. When input expression values are integer counts, such as those derived from RNA-seq experiments, then this argument should be set to kcdf="Poisson".
- gsva.tau
Numeric vector of length 1. The exponent defining the weight of the tail in the random walk performed by the GSVA (Hänzelmann et al., 2013) method. The default value is 1 as described in the paper.
- gsva.maxDiff
Logical vector of length 1 which offers two approaches to calculate the enrichment statistic (ES) from the KS random walk statistic. FALSE: ES is calculated as the maximum distance of the random walk from 0. TRUE (the default): ES is calculated as the magnitude difference between the largest positive and negative random walk deviations.
- gsva.absRanking
Logical vector of length 1 used only when maxDiff=TRUE. When absRanking=FALSE (default) a modified Kuiper statistic is used to calculate enrichment scores, taking the magnitude difference between the largest positive and negative random walk deviations. When absRanking=TRUE the original Kuiper statistic that sums the largest positive and negative random walk deviations, is used. In this latter case, gene sets with genes enriched on either extreme (high or low) will be regarded as ’highly’ activated.
- verbose
Logical indicating whether to print progress messages. Default is TRUE.
- nCores
The number of cores to use for parallel computation. Default is `parallel::detectCores() - 2`, which detects the number of cores available on the system and reserves 2 cores for other tasks.
Details
This function supports multiple methods for estimating gene-set enrichment scores, including ssGSEA, GSVA, zscore, and plage. The scores are calculated for each gene set across all samples. The `ssGSES` function is flexible and allows for customization of the minimum and maximum size of gene sets considered in the analysis. By providing different methods, the function can adapt to various types of gene-set enrichment analysis, each having its own strengths and suitable applications.
"gsva": Gene Set Variation Analysis, suitable for detecting subtle changes in pathway activity."ssgsea": Single-Sample Gene Set Enrichment Analysis, useful for individual sample analysis."zscore": Z-score transformation, a simpler approach to standardize expression values."plage": Pathway Level Analysis of Gene Expression, which focuses on correlating pathway components.