# runClipper: Run a topological analysis on an expression dataset using clipper.

## 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.## Usage

runClipper(x, expr, classes, method, ...)

## Arguments

x

a `PathwayList`

, a list of `Pathway`

s
or a single `Pathway`

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"`

.

...

Additional options; see for details
`easyClip`

. When invoked on a `PathwayList`

, can use the named
option "maxNodes" to limit the analysis to those pathways having up to
this given number of nodes.

## 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.## Examples

if (require(clipper) & require(ALL)){
k <- pathways("hsapiens", "kegg")
path <- convertIdentifiers(k$'Chronic myeloid leukemia', "entrez")
genes <- nodes(path)
data(ALL)
all <- as.matrix(exprs(ALL[1:length(genes),1:20]))
classes <- c(rep(1,10), rep(2,10))
rownames(all) <- genes
runClipper(path, all, classes, "mean", pathThr=0.1)
}