dna (version 2.1-2)

RRnet: Ridge Regression network

Description

Computes the connectivity scores for a network based on ridge regression.

Usage

RRnet(data,lambda=1,rescale.data=TRUE, symmetrize.scores=TRUE,
rescale.scores=FALSE)

Arguments

data

microarray dataset with genes in columns and samples in rows.

lambda

the ridge regression penalty parameter.

rescale.data

indicates whether data should be rescaled.

symmetrize.scores

indicates whether PLS scores should be made to be symmetric.

rescale.scores

indicates whether PLS scores should be rescaled so that the largest score for each gene should be 1 in magnitude.

Value

RRnet

a matrix of interactions between gene pairs based on ridge regression.

References

Gill, R., Datta, S., and Datta, S. (2010) A statistical framework for differential network analysis from microarray data. BMC Bioinformatics, 11, 95.

Hastie, T., Tibshirani, R., and Friedman, J. (2009) The Elements of Statistical Learning. Springer: New York.

Examples

# NOT RUN {
# small example using RRnet with penalty parameter 1,
# data rescaled, and scores symmetrized but not rescaled
X1=rbind(
c(2.5,6.7,4.5,2.3,8.4,3.1),
c(1.2,0.7,4.0,9.1,6.6,7.1),
c(4.3,-1.2,7.5,3.8,1.0,9.3),
c(9.5,7.6,5.4,2.3,1.1,0.2))
s=RRnet(X1)
print(round(s,4))

# small example using RRnet with penalty parameter 3,
# data rescaled, and scores symmetrized and rescaled
s2=RRnet(X1,lambda=3,rescale.data=TRUE,symmetrize.scores=TRUE,rescale.scores=TRUE)
print(round(s2,4))
# }