# cliqueMeanTest

From clipper v1.12.0
by Paolo Martini

##### Mean test for cliques.

It decomposes the graph in cliques and performs the mean test in every one.

##### Usage

```
cliqueMeanTest(expr, classes, graph, nperm, alphaV=0.05, b=100,
root=NULL, permute=TRUE, alwaysShrink=FALSE)
```

##### 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. - nperm
- number of permutations.
- alphaV
- pvalue threshold for variance test to be used during mean test.
- b
- number of permutations for mean analysis.
- root
- nodes by which rip ordering is performed (as far as possible) on the variables using the maximum cardinality search algorithm.
- 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.
- alwaysShrink
- always perform the shrinkage estimates of variance.

##### Value

##### 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.

##### See Also

##### Examples

```
if (require(graphite) & require(ALL)){
kegg <- pathways("hsapiens", "kegg")
graph <- pathwayGraph(convertIdentifiers(kegg$'Chronic myeloid leukemia', "entrez"))
genes <- nodes(graph)
data(ALL)
all <- ALL[1:length(genes),1:20]
classes <- c(rep(1,10), rep(2,10))
featureNames(all@assayData)<- genes
graph <- subGraph(genes, graph)
cliqueMeanTest(all, classes, graph, nperm=100, permute=FALSE)$alpha
}
```

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

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