Assess the differential effect in gene expression between groups of microarray replicates.
1 2 3 
exprs 
A matrix of expression values of size n x m, with rows
representing the genes and columns representing the samples. The
structure is the same as for the 
groups 
A factor with the length m, specifying the groups
of the corresponding samples in 
id 
Index vector for the rows of 
index 
Index for the columns of 
testArgs 
Optional list with arguments passed to the

With the ttest and the Ftest, two widely used statistical tests are available in this package. To allow a fast computation the implementations from the genefilter package is used.
It is also possible to use custom test statistics for assessing the
differential effect between groups for each gene. In this case the
function is passed as the test
argument to the gsri
method, while
additional parameters for the function can be passed as a list via the
testArgs
argument. The defined function is required to be called as
function(exprs, groups, id, index, testArgs)
,
with exprs
the matrix of expression intensities of the microarray
and groups
the factor of group labels, with the same structure as
those passed initially to the gsri
method. The vector id
contains
the indices of the genes part of the current gene set and is used to
subset the expression intensities if necessary. The function has to
return one pvalue for each gene in the gene set indicating its
differential effect. The vector index
contains the indicies of the
samples for the bootstrapping. Applying index
on the expression
matrix in the form of exprs[ ,index]
generates the bootstrapped
data set.
For details on how to define and use your custom test functions, please refer to the ‘examples’ section or the vignette of this package.
A vector of pvalues, indicating the significance of the differential effect between groups for each gene.
Julian Gehring
Maintainer: Julian Gehring <julian.gehring@fdm.unifreiburg.de>
Package:
GSRIpackage
Class:
Gsri
Methods:
gsri
getGsri
getCdf
getParms
export
sortGsri
plot
show
summary
readCls
readGct
Statistical tests from the genefilter package:
rowFtests
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## Not run:
## A simple example for a custom test function using a linear model.
## Note that for two groups this is equivalent to a ttest with equal variances.
testFcn < function(exprs, groups, id, index, testArgs) {
stat < function(e, g, f) {
m < lm(f)
pval < summary(m)$coefficients[2,4]
}
pvals < apply(exprs[id,index], 1, stat, groups, testArgs$f)
return(pvals)
}
## Pass the definition of the linear model through 'testArgs'
f < formula(e ~ g)
res < gsri(exprs, groups, test=testFcn, testArgs=list(f=f))
## End(Not run)

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