Run a topological analysis on an expression dataset using clipper.

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Description

This function is deprecated and will be removed in a future release. You can use runClipper instead.

Usage

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  runClipperMulti(pathways, expr, classes, method, maxNodes=150, ...)

Arguments

pathways

a PathwayList object.

expr

a matrix (size: number p of genes x number n of samples) of gene expression.

classes

a vector (length: n) of class assignments.

method

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

maxNodes

ignore a pathway when it has more than this number of nodes.

...

Additional options; see for details easyClip.

Details

The expression data and the pathway have to be annotated in the same set of identifiers.

Value

A list with two elements:

  • results: a list with one entry for each successfully analyzed pathway;

  • errors: a vector containing the error messages of failed analyses.

References

Martini P, Sales G, Massa MS, Chiogna M, Romualdi C. Along signal paths: an empirical gene set approach exploiting pathway topology. Nucleic Acids Res. 2013 Jan 7;41(1):e19. doi: 10.1093/nar/gks866. Epub 2012 Sep 21. PubMed PMID: 23002139; PubMed Central PMCID: PMC3592432.

See Also

clipper

Examples

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if (require(clipper) & require(ALL)){
  k <- pathways("hsapiens", "kegg")
  paths <- convertIdentifiers(k[1:5], "entrez")
  genes <- unlist(lapply(paths, nodes))
  data(ALL)
  all <- as.matrix(exprs(ALL[1:length(genes),1:20]))
  classes <- c(rep(1,10), rep(2,10))
  rownames(all) <- genes
  runClipperMulti(paths, all, classes, "mean", pathThr=0.1)
}