clipper: Dissect the pathway to find the path with the greatest...
In clipper: Gene Set Analysis Exploiting Pathway Topology

Description Usage Arguments Details Value References See Also Examples

Basing on either variance or mean clique test, this function identifies the paths that are mostly related with the phenotype under study.

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clipper(expr, classes, graph, method=c("variance","mean", "both",
"paired"), nperm=100, alphaV=0.05, b=100, root=NULL, trZero=0.001, signThr=0.05,
maxGap=1, 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.
`method`	the kind of test to perform on the cliques. It could be mean, variance, mixed (the best between variance and mean) or paired mean.
`nperm`	number of permutations. Default = 100.
`alphaV`	pvalue threshold for variance test to be used during mean test. Default = 0.05.
`b`	number of permutations for mean analysis. Default = 100.
`root`	nodes by which rip ordering is performed (as far as possible) on the variables using the maximum cardinality search algorithm.
`trZero`	lowest pvalue detectable. This threshold avoids that -log(p) goes infinite.
`signThr`	significance threshold for clique pvalues.
`maxGap`	allow up to maxGap gaps in the best path computation. Default = 1.
`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.

The both method combines the results obtained from the mean and variance test. In particular it assign to the cliques the minimum of mean and variance p-values.

A matrix with a row for each paths. Columns are organized as follows:

Index of the starting clique
Index of the ending clique
Index of the clique where the maximum value is reached
Length of the path
Maximum score of the path
Average score along the path
Percentage of path activation
Impact of the path on the entire pathway
Cliques involved and significant
Cliques forming the path
Genes forming the significant cliques
Genes forming the path

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.

cliqueVarianceTest, cliqueMeanTest, getJunctionTreePaths

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)
  clipped <- clipper(all, classes, graph, "var", trZero=0.01, permute=FALSE)
  clipped[,1:5]
}