netClass (version 1.0)

aep.cv: Cross validation for aepSVM (aepSVM)

Description

Cross validation for aepSVM (aepSVM) using SAM to select significant differential expressed genes

Usage

aep.cv(x, y, folds = 10, repeats = 5, parallel = TRUE, cores = NULL, DEBUG = TRUE,
		Gsub =matrix(1,100,100), Cs = 10^(-3:3), seed = 1234)

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

Gsub

Adjacency matrix of Protein-protein interaction network

Cs

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

seed

seed for random sampling.

Value

• a LIST for Cross-Validation results
• auc
  The AUC values of each test fold
• fits
  The tranined models for traning folds
• feat
  The feature selected by each by the fits
• labels
  the original lables for training

References

Guo et al., Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58.

See Also

See Also as aep.cv

Examples

library(netClass)
data(expr)
data(ad.matrix)
x <- expr$genes
y <- expr$y

 library(KEGG.db)
r.aep <- aep.cv(x[,1:200], y, folds=3, repeats=1, parallel=FALSE,cores=2,Gsub=ad.matrix,
				Cs=10^(-3:3),seed=1234,DEBUG=TRUE)