viewNestedModules {PANR} | R Documentation

## 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.

### Author(s)

Xin Wang xw264@cam.ac.uk

### 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)

[Package PANR version 1.26.0 Index]