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SAGx (version 1.46.0)

GSEA.mean.t: Gene Set Enrichment Analysis using output from samroc

Description

Based on a list of gene sets, e.g. pathways, in terms Affymtrix identifiers, these sets are ranked with respect to regulation as measured by an effect in a linear model using the SAM statistic. Typical applications include two-group comparisons or simple linear regression to clinical variable or gene expression of a given gene.

Usage

GSEA.mean.t(samroc = samroc.res, probeset = probeset, 
pway = kegg, type = c("original","absolute", "maxmean"), two.side = FALSE, cutoff = c(10,Inf), restand = TRUE)

Arguments

samroc
an object of class samroc.result
probeset
the Affymetrix identifiers
pway
a list of pathways or gene sets
type
if "absolute" value of the absolute value of the samroc test statistic is used. If "original" no transformation. "maxmean" not available.
two.side
if TRUE a two-sided test is performed. Currently only two-sided test when type = "original" and else one-sided
cutoff
Gene sets with the number of members not falling within the interval given by cutoff are excluded
restand
if TRUE a 'restandardization' following Efron and Tibshirani (2006) is performed

Value

  • A matrix with columns normal approximation p-values, mean statistic, median statistic, and if type = "original", also Wilcoxon signed ranks statistic based p-value.

Details

Restandardization based on Efron and Tibshirani (2006) introduced. For normal approximation of the gene set statistic both the mean of the statstic, or the variance (and likewise for the Wilcoxon statistic), are obtained from the permutation distribution included in the samroc.result object. Note that this will account for the dependency between genes.

References

Tian, Lu and Greenberg, Steven A. and Kong, Sek Won and Altschuler, Josiah and Kohane, Isaac S. and Park, Peter J. (2005) Discovering statistically significant pathways in expression profiling studies, PNAS Vol. 102, nr. 38, pp. 13544-13549 Bradley Efron and Robert Tibshirani (2006) On testing of the significance of sets of genes, Technical report, Stanford