easyClip: Easy clip analysis.
In clipper: Gene Set Analysis Exploiting Pathway Topology

Description Usage Arguments Value References See Also Examples

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.

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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)

`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.

a matrix with row as the different paths. Columns are organized 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)

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),1:24]
  classes <- c(rep(1,12), rep(2,12))
  featureNames(all@assayData)<- genes
  graph <- subGraph(genes, graph)
  easyClip(all, classes, graph, nperm=10)
}