netClass (version 1.0)

hubc.cv: Cross validation for hub nodes classification

Description

Cross validation for hub nodes classification, which described in Taylor et al.(2009).

Usage

hubc.cv(x, y, folds=10, repeats=5, parallel = TRUE, cores = NULL, DEBUG=TRUE, nperm=500,
		node.ct=0.98, Gsub=matrix(1,100,100), Gs=Gs,seed=1234,Cs=10^c(-3:3))

Arguments

x

a p x n matrix of expression measurements with p samples and n genes.

y

a factor of length p comprising the class labels.

folds

number of -folds cross validation (CV)

repeats

number of CV repeat times

parallel

paralle computing or not

cores

cores used in parallel computing

DEBUG

show more results or not

nperm

number of permutation test steps

node.ct

cut off value for select highly quantile nodes in a nwtwork. Defaults to 0.98).

Gsub

an adjacency matrix that represents the underlying biological network.

Gs

Undirected of graph with adjacency matrix Gsub.

seed

Seed for random sampling.

Cs

Soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).

Value

• auc
  The AUC values of each test fold
• fits
  The tranined models for traning folds
• feat
  The selected features of each training folds
• labels
  the original lables for training

References

Taylor et al.(2009)Dynamic modularity in protein interaction networks predicts breast cancer outcome, Nat. Biotech.: doi: 10.1038/nbt.1522

Examples

library(netClass)
 data(expr)
 data(Gs2)
 data(ad.matrix)
 x <- expr$genes
 y <- expr$y
r.hubC <- hubc.cv(x=x, y=y, folds=3, repeats=1, parallel=FALSE, cores=2, DEBUG=TRUE,
				nperm=2, Gsub=ad.matrix,Gs=Gs2,node.ct=0.6,Cs=10^(-3:3))