ProteinCluster-methods: Cluster proteins based on significant protein pairs

In PANDA: Preferential Attachment Based Common Neighbor Distribution Derived Functional Associations

Description

This function uses the p-values (or probabilities) derived from the PAND algorithm to perform agglomerative hierarchical clustering (using the unweighted group average) for proteins that form significant protein pairs.

Usage

ProteinCluster(Pfile, Plot=FALSE, TextScaler=50, height=10, width)

Arguments

Pfile	A data frame returned from the function SignificantPairs()
Plot	If FALSE, a dendrogram will NOT be generated
TextScaler	Scale the size of the label in the generated PDF file
height	The height of the generated PDF file
width	The width of the generated PDF file

Value

This function returns an object in the class "dendrogram". If the argument "Plot" is "TRUE", it will also plot the dendrogram.

See Also

SignificantPairs, KEGGpredict, GOpredict, SignificantSubcluster

Examples

## not run
## data(dfPPI)
## OrderAll=SignificantPairs(dfPPI)
## dendMap=ProteinCluster(Pfile=OrderAll, Plot=TRUE, TextScaler=30)

PANDA documentation built on May 2, 2019, 6:53 a.m.