clipper (version 1.12.0)

pathwayTest: Whole pathway test using qpipf.

Description

Performs variance and mean test using qpipf on the whole pathway.

Usage

pathQ(expr, classes, graph, nperm=100, alphaV=0.05, b=100,
permute=TRUE, paired=FALSE, alwaysShrink=FALSE)

Arguments

expr

an expression matrix or ExpressionSet with colnames for samples and rownames for expression features.

classes

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

graph

a graphNEL object.

nperm

number of permutations. Default = 100.

alphaV

pvalue significance threshold for variance test to be used during mean test. Default = 0.05.

b

number of permutations for mean analysis. Default = 100.

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.

paired

perform the test for paired sample. It assumes 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.

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

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)
  pathQ(all, classes, graph, nperm=100, permute=FALSE)
}