PANR (version 1.18.0)

viewNestedModules: View the nested modules in a posterior association network in RedeR

Description

The function displays the nested enriched functional gene modules found by pvclust in a powerful graphic visualization software RedeR.

Usage

viewNestedModules(object, pValCutoff=0.01, minSize=3, maxSize=100, 
verbose=TRUE, ...)

Arguments

object

an object of S4 class PAN.

pValCutoff

a numeric value specifying the p-value cutoff to tell the significance of a gene module.

minSize

a numeric or integer value giving the minimal size of gene modules.

maxSize

a numeric or integer value giving the maximal size of gene modules.

verbose

a logical value to switch on (if TRUE) or off if FALSE detailed run-time message.

...

not in use, but only for further extension.

Details

This function presents the searched enriched functional modules in RedeR - a bioconductor package for network visualization.

Please note that the user is expected to run buildPAN to build a graph and search modules using pvclustModule prior to visualize using this function.

Please also note that if `RedeR' is selected as the graphics engine, it is suggested to manually organise the sizes and positions of containers (for nesting gene modules) run a dynamic layout to obtain the best structure for the network.

References

Xin Wang, Roland F. Schwarz, Mauro Castro, Klaas W. Mulder and Florian Markowetz, Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, in preparation.

See Also

addGraph, nestNodes, viewPAN, buildPAN

Examples

## Not run: 
# data(bm, package="PANR")
# pan<-new("PAN", bm1=bm1)
# pan<-infer(pan, para=list(type="SNR", log=TRUE, sign=TRUE, cutoff=log(5)),
# filter=FALSE, verbose=TRUE)
# data(Bakal2007Cluster, package="PANR")
# pan<-buildPAN(pan, engine="igraph", para=list(nodeColor=nodeColor, 
# hideNeg=TRUE), verbose=TRUE)
# ##need pvclust to search modules
# library(pvclust)
# pan<-pvclustModule(pan, nboot=10000, metric="cosine2",
# hclustMethod="average", filter=TRUE, verbose=TRUE, r=c(5:12/7))
# viewNestedModules(pan, pValCutoff=0.05, minSize=5, maxSize=100)
# ## End(Not run)