netClass (version 1.2.1)

cv.frsvm: Cross validation for FrSVM

Description

Cross validation for FrSVM, an R algorithm, which integrates protein-protein interaction network information into gene selection for microarry classification

Usage

cv.frsvm(x, y, folds = 10, Gsub = matrix(1, 100, 100), repeats  = 5, parallel = FALSE, cores = 2, DEBUG = FALSE, d = 0.85, top.uper = 10, top.lower = 50, seed = 1234, Cs = 10^c(-3:3))

Arguments

x

gene expression data

y

class labels

folds

number of -folds cross validation (CV)

Gsub

Adjacency matrix of Protein-protein intersction network

repeats

number of CV repeat times

parallel

paralle computing or not

cores

cores used in parallel computing

DEBUG

show more results or not

d

damping factor for GeneRank, defaults value is 0.5

top.uper

the uper bound of top ranked genes

top.lower

the lower bound of top ranked genes

seed

Seed for random sampling.

Cs

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

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

Yupeng Cun, Holger Frohlich (2012) Integrating Prior Knowledge Into Prognostic Biomarker Discovery Based on Network Structure, arXiv:1212.3214 Winter C, Kristiansen G, Kersting S, Roy J, Aust D, et al. (2012) Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Comput Biol 8(5): e1002511.

Examples

library(netClass)
data(expr)
data(ad.matrix)
x <- expr$genes
y <- expr$y
### 
r.frsvm <-cv.frsvm(x[,1:200], y, folds=3,Gsub=ad.matrix, repeats=1, parallel=FALSE,
	cores=2, DEBUG=TRUE,d=.85,top.uper=5,top.lower=15,seed=1234,Cs=10^c(-3:3))