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Last data update: 2014.03.03

R: Infer a posterior association network given a fitted...

Arora2010.module.visualize

R Documentation

Infer a posterior association network given a fitted BetaMixture object

Description

The function represent inferred significant functional modules in RedeR, which is a bioconductor package for network visualization.

Usage

Arora2010.module.visualize(rdp, pan, mod.pval.cutoff=0.05, mod.size.cutoff=4, 
avg.degree.cutoff=0.5, filter.effects=TRUE)

Arguments

`rdp`	an object of class `RedPort`, which builds the interface between R and RedeR Java server.
`pan`	an object of class `PAN`, which includes the inferred posterior association network and functional modules.
`mod.pval.cutoff`	a numeric value specifying the p-value cutoff for selecting significant functional modules.
`mod.size.cutoff`	an integer value specifying the minimal size of functional modules.
`avg.degree.cutoff`	a numeric value specifying the cutoff of module density, which is the ratio of predicted significant associations to all possible associations.
`filter.effects`	a logical value specifying whether or not to filter out modules that are not of interest. In this application to Ewing's sarcoma, modules associated with positive loss-of-function can be filtered out because we are only interested in modules related to inhibiting growth and proliferation of cancer cells.

Details

The inferred functional modules are represented in a very compact layout in RedeR, as many of them are nested to each other due to the nature of hierarchical clustering. Four arguments can be specified to filter modules. More details about module filtering procedures can be found in the Methods section of Wang X et al., 2012.

Author(s)

Xin Wang xw264@cam.ac.uk

References

Arora S, Gonzales IM, Hagelstrom RT, et al. (2010). RNAi phenotype profiling of kinases identifies potential therapeutic targets in Ewing's sarcoma. Molecular Cancer, 9(1), 218.

Wang X, Castro MA, Mulder KW and Markowetz F (2012), Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, PLoS Computational Biology, doi:10.1371/journal.pcbi.1002566.

Examples

## Not run: 
library(RedeR)
rdp <- RedPort('MyPort')
calld(rdp)
data(pan_Arora2010, package="Mulder2012")
Arora2010.module.visualize(rdp, pan_Arora2010, mod.pval.cutoff=0.05, 
mod.size.cutoff=4, avg.degree.cutoff=0.5)

## End(Not run)