cliquePairedTest: Paired mean test for cliques.

Description

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

Usage

cliquePairedTest(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). It is assumed that class labels are ordered so that the first occurrence of class 2 is paired with the first occurrence of class 1 and so on.

graph

a graphNEL object.

nperm

number of permutations.

alphaV

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

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.

Value

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

Run this code

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