cliqueVarianceTest: Variance test for cliques.
In clipper: Gene Set Analysis Exploiting Pathway Topology

Description Usage Arguments Value References See Also Examples

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

1 2	cliqueVarianceTest(expr, classes, graph, nperm, alphaV=0.05, b=100, root=NULL, permute=TRUE, alwaysShrink=FALSE)

`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).
`graph`	a `graphNEL` object.
`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.

a list with alphas (vector of cliques pvalues based on the variance test) and cliques (list of the cliques and related elements).

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.

cliqueMeanTest.

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
}