easyClip
From clipper v1.12.0
by Paolo Martini
Easy clip analysis.
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 loglikelihood 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
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]
}
Community examples
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