GSRI (version 2.20.0)

GSRI-package: Gene Set Regulation Index (GSRI) package

Description

The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI).

Details

The GSRI approach estimates the number of differentially expressed genes in gene sets. It is independent of the underlying statistical test used for assessing the differential effect of genes and does not require any cut-off values for the distinction between regulated and unregulated genes. The approach is based on the fact that p-values obtained from a statistical test are uniformly distributed under the null hypothesis and are shifted towards zero in case of the alternative hypothesis.

Through non-parametric fitting of the uniform component of the p-value distribution, the fraction of regulated genes $\sQuote{r}$ in a gene set is estimated. The GSRI $\sQuote{eta}$ is then defined as the $\sQuote{alpha*100}$%-quantile of the distribution of $\sQuote{r}$, obtained from bootstrapping the samples within the groups. The index indicates that with a probability of $(1-\sQuote{alpha})$% more than a fraction of $\sQuote{eta}$ genes in the gene set is differentially expressed. It can also be employed to test the hypothesis whether at least one gene in a gene set is regulated. Further, different gene sets can be compared or ranked according to the estimated amount of regulation. For details of the method, an application to experimental data, and a comparison with related approaches, see Bartholome et al., 2009.

The package is published under the GPL-3 license.

References

Kilian Bartholome, Clemens Kreutz, and Jens Timmer: Estimation of gene induction enables a relevance-based ranking of gene sets, Journal of Computational Biology: A Journal of Computational Molecular Cell Biology 16, no. 7 (July 2009): 959-967. http://www.liebertonline.com/doi/abs/10.1089/cmb.2008.0226

The GSRI package uses the functionality of the following packages:

Julian Gehring, Clemens Kreutz, Jens Timmer: les: Identifying Loci of Enhanced Significance in Tiling Microarray Data http://bioconductor.org/help/bioc-views/release/bioc/html/les.html

Korbinian Strimmer: fdrtool: Estimation and Control of (Local) False Discovery Rates. http://CRAN.R-project.org/package=fdrtool

Robert Gentleman, Vincent J. Carey, Wolfgang Huber, Florian Hahne: genefilter: methods for filtering genes from microarray experiments. http://bioconductor.org/help/bioc-views/release/bioc/html/genefilter.html

See Also

Class: Gsri Methods: gsri getGsri getCdf getParms export sortGsri plot show summary readCls readGct

Examples

## Simulate expression data for a gene set of
## 100 genes, 20 samples (10 treatment, 10 control)
## and 30 regulated genes
set.seed(1)
exprs <- matrix(rnorm(100*20), 100)
exprs[1:30,1:10] <- rnorm(30*10, mean=2)
rownames(exprs) <- paste("g", 1:nrow(exprs), sep="")
groups <- factor(rep(1:2, each=10))

## Estimate the number of differentially expressed genes
res <- gsri(exprs, groups)
res

## Perform the analysis for different gene set
library(GSEABase)
gs1 <- GeneSet(paste("g", 25:40, sep=""), setName="set1")
gs2 <- GeneSet(paste("g", seq(1, nrow(exprs), by=5), sep=""), setName="set2")
gsc <- GeneSetCollection(gs1, gs2)

res2 <- gsri(exprs, groups, gs1)
res3 <- gsri(exprs, groups, gsc, verbose=TRUE)

summary(res2)