clipper (version 1.12.0)

easyClip: Easy clip analysis.

Description

Easy clip function allows the full exploitation of Clipper Package features in a unique and easy to use function. Starting from an expression matrix and a pathway, these function extact the most transcriptionally altered portions of the graph.

Usage

easyClip(expr, classes, graph, method=c("variance","mean"),
pathThr=0.05, pruneLevel=0.2, nperm=100, alphaV=0.05, b=100,
root=NULL, trZero=0.001, signThr=0.05, maxGap=1, permute=TRUE)

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.

method

the kind of test to perform on the cliques. It could be either mean or variance.

pathThr

The significance threshold of the whole pathway test. Deafault = 0.05

pruneLevel

a dissimilarity threshold. NULL means no pruning.

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.

Value

as follwes: 1 - Index of the starting clique 2 - Index of the ending clique 3 - Index of the clique where the maximum value is reached 4 - length of the path 5 - maximum score of the path 6 - average score along the path 7 - percentage of path activation 8 - impact of the path on the entire pathway 9 - clique involved and significant 10 - clique forming the path 11 - genes forming the significant cliques 12 - genes forming the path)

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, cliqueMeanTest, getJunctionTreePaths

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)
  clipped <- easyClip(all, classes, graph, nperm=10)
  clipped[,1:5]
}