PCnet: Principal Components network

In dna: Differential Network Analysis

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.

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 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))

Example output

dna 1.1-1 loaded
        Gene 1  Gene 2  Gene 3  Gene 4  Gene 5  Gene 6
Gene 1  1.0000  0.2183  0.0675  0.0706 -0.8274 -0.4448
Gene 2  0.2183  1.0000 -0.3140 -0.6345  0.2418 -0.5375
Gene 3  0.0675 -0.3140  1.0000 -1.0032 -0.4588  0.4224
Gene 4  0.0706 -0.6345 -1.0032  1.0000 -0.3830  0.1748
Gene 5 -0.8274  0.2418 -0.4588 -0.3830  1.0000  0.0200
Gene 6 -0.4448 -0.5375  0.4224  0.1748  0.0200  1.0000
        Gene 1  Gene 2  Gene 3  Gene 4  Gene 5  Gene 6
Gene 1  1.0000  0.4270  0.4875 -0.6257 -0.5171 -0.4339
Gene 2  0.4270  1.0000 -0.5587 -0.4244  0.2439 -1.0000
Gene 3  0.4875 -0.5587  1.0000 -0.1300 -0.9700  0.4109
Gene 4 -0.6257 -0.4244 -0.1300  1.0000  0.4455  0.5027
Gene 5 -0.5171  0.2439 -0.9700  0.4455  1.0000 -0.2015
Gene 6 -0.4339 -1.0000  0.4109  0.5027 -0.2015  1.0000

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