Description Details Author(s) References See Also Examples

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

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.

Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer

Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>

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

Class:
`Gsri`

Methods:
`gsri`

`getGsri`

`getCdf`

`getParms`

`export`

`sortGsri`

`plot`

`show`

`summary`

`readCls`

`readGct`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## 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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.