easyClip

0th

Percentile

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 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.

cliqueVarianceTest, cliqueMeanTest, getJunctionTreePaths

• easyClip
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]
}

Documentation reproduced from package clipper, version 1.12.0, License: AGPL-3

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