pathwayTest: Whole pathway test using qpipf.

In clipper: Gene Set Analysis Exploiting Pathway Topology

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

a list with alphaVar (pvalue for the variance test) and alphaMean (pvalue for mean test).

Note

This function is based on the Gaussian Graphical Models and to use it in a proper way it is necessary that the graph is an Direct Acyclic Graph. Please check any graph in input using isAcyclic from ggm package.

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),1:24]
  classes <- c(rep(1,12), rep(2,12))
  featureNames(all@assayData)<- genes
  graph <- subGraph(genes, graph)
  pathQ(all, classes, graph, nperm=100, permute=FALSE)
}

clipper documentation built on Nov. 8, 2020, 6:18 p.m.