PCnet: Principal Components network

Description

Computes the connectivity scores for a network based on principal components.

Usage

PCnet(data,ncom=3,rescale.data=TRUE, symmetrize.scores=TRUE,
rescale.scores=FALSE)

Arguments

data

microarray dataset with genes in columns and samples in rows.

ncom

the number of PLS components (latent variables) in PLS models.

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

PCnet

a matrix of interactions between gene pairs based on principal components 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

Run this code

# NOT RUN {
# small example using PCnet with 3 principal components,
# 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=PCnet(X1)
print(round(s,4))

# small example using PCnet with 2 principal components,
# data rescaled, and scores symmetrized and rescaled
s2=PCnet(X1,ncom=2,rescale.data=TRUE,symmetrize.scores=TRUE,rescale.scores=TRUE)
print(round(s2,4))
# }