RRnet: Ridge Regression network

In dna: Differential Network Analysis

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.

Author(s)

The authors are Ryan Gill, Somnath Datta, and Susmita Datta. The software is maintained by Ryan Gill [e-mail].

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

# 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))

dna documentation built on July 8, 2020, 7:26 p.m.