cv.pac: Cross validation for Pathway Activities Classification(PAC)

In netClass: netClass: An R Package for Network-Based Biomarker Discovery

Description

Cross validation for Pathway Activities Classification(PAC) using Logistic regression model for classification. Implementation of the Pathway Activities Classification by CROG algorithm.

Usage

cv.pac(x=x, y=y, folds=10, repeats=5, parallel = TRUE, cores = NULL, 
		DEBUG=TRUE, Gsub=matrix(1,100,100), 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 debugging information in screen or not.
Gsub	Adjacency matrix of Protein-protein intersction network
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

Author(s)

Yupeng Cun [e-mail]

References

Lee E, Chuang H-Y, Kim J-W, Ideker T, Lee D (2008) Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput Biol 4(11): e1000217.

Examples

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

library(KEGG.db)
r.pac <- cv.pac(x=x, y=y, folds=3, repeats=1, parallel=FALSE, cores=2, DEBUG=TRUE,
				Gsub=ad.matrix,seed=1234)

netClass documentation built on May 29, 2017, 7:18 p.m.