graphite (version 1.12.0)

runClipperMulti: Run topological analyses on an expression dataset, calling clipper over all the pathways in a list.

Description

clipper is a package for topological gene set analysis. It implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. This function easily provide pathway analysis using clipper on an entire pathway database.

Usage

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

Arguments

pathways

A list of pathways, such as biocarta, kegg,nci, reactome or spike.

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 pathways with more than "maxNodes" nodes. Set to "NULL" to disable the filter.

...

Additional options for details see clipper.

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.

Details

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

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

if (require(clipper) & require(ALL)){
  paths <- lapply(kegg[1:5], function(x) convertIdentifiers(x, "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
  clipped <- runClipperMulti(paths, all, classes, "mean", pathThr=0.1)
}