runClipper {graphite} | R Documentation

## 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.

If the option `Ncpus` is set to a value larger than 1 and the package `parallel` is installed, the conversion procedure will automatically use multiple cores.

### Usage

    runClipper(x, expr, classes, method, which = "proteins", seed = NULL, ...)

### 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"`.
`which` | the pathway variant you want. See `Pathway` documentation for a list of the supported variants.
`seed` | if not `NULL`, set the seed for the random number generator used by clipper.
`...` | additional options: see for details `easyClip`. When invoked on a `PathwayList`, you can use the named option `maxNodes` to limit the analysis to those pathways with at most a 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.

### See Also

`clipper`

### Examples

    if (require(clipper) & require(ALL) & require(a4Preproc)) {
      data(ALL)
      pheno <- as(phenoData(ALL), "data.frame")
      samples <- unlist(lapply(c("NEG", "BCR/ABL"), function(t) {
        which(grepl("^B\\d*", pheno$BT) & (pheno$mol.biol == t))[1:10]
      }))
      classes <- c(rep(1,10), rep(2,10))

      expr <- exprs(ALL)[,samples]
      rownames(expr) <- paste("ENTREZID", featureData(addGeneInfo(ALL))$ENTREZID,
                              sep = ":")

      k <- as.list(pathways("hsapiens", "kegg"))
      selected <- k[c("Bladder cancer", "Cytosolic DNA-sensing pathway")]

      runClipper(selected, expr, classes, "mean", pathThr = 0.1)
    }

[Package graphite version 1.26.3 Index]