cliqueMeanTest

0th

Percentile

Mean test for cliques.

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

Usage
cliqueMeanTest(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).
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.
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.

See Also

cliqueVarianceTest.

Aliases
  • cliqueMeanTest
Examples
if (require(graphite) & require(ALL)){
  kegg  <- pathways("hsapiens", "kegg")
  graph <- pathwayGraph(convertIdentifiers(kegg$'Chronic myeloid leukemia', "entrez"))
  genes <- nodes(graph)
  data(ALL)
  all <- ALL[1:length(genes),1:20]
  classes <- c(rep(1,10), rep(2,10))
  featureNames(all@assayData)<- genes
  graph <- subGraph(genes, graph)
  cliqueMeanTest(all, classes, graph, nperm=100, permute=FALSE)$alpha
}
Documentation reproduced from package clipper, version 1.12.0, License: AGPL-3

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