# cliqueVarianceTest: Variance test for cliques.

## Description

It decomposes the graph in cliques and performs the variance test in every one.## Usage

cliqueVarianceTest(expr, classes, graph, nperm, alphaV=0.05,
b=100, root=NULL, permute=TRUE, alwaysShrink=FALSE)

## Arguments

expr

an expression matrix or ExpressionSet with colnames for
samples and row name for genes.

classes

vector of 1,2 indicating the classes of samples (columns).

nperm

number of permutations.

alphaV

pvalue threshold for variance test to be used during mean test.

b

number of permutations for mean analysis.

root

nodes by which rip ordering is performed (as far as
possible) on the variables using the maximum cardinality search
algorithm.

permute

always performs permutations in the concentration
matrix test. If FALSE, the test is made using the asymptotic
distribution of the log-likelihood ratio. This option should be use
only if samples size is >=40 per class.

alwaysShrink

always perform the shrinkage estimates of
variance.

## References

Martini P, Sales G, Massa MS, Chiogna M, Romualdi C. Along signal
paths: an empirical gene set approach exploiting pathway
topology. NAR. 2012 Sep. Massa MS, Chiogna M, Romualdi C. Gene set analysis exploiting the
topology of a pathway. BMC System Biol. 2010 Sep 1;4:121.

## Examples

if (require(graphite) & require(ALL)){
kegg <- pathways("hsapiens", "kegg")
graph <- pathwayGraph(convertIdentifiers(kegg$'Chronic myeloid leukemia', "entrez"))
genes <- nodes(graph)
data(ALL)
all <- ALL[1:length(genes),1:20]
classes <- c(rep(1,10), rep(2,10))
featureNames(all@assayData)<- genes
graph <- subGraph(genes, graph)
cliqueVarianceTest(all, classes, graph, nperm=100, permute=FALSE)$alpha
}