PLSnet: Partial Least Squares network

In dna: Differential Network Analysis

Description

Computes the connectivity scores for a network based on partial least squares (PLS).

Usage

PLSnet(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

PLSnet

a matrix of interactions between gene pairs based on partial least squares.

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.

Pihur, V., Datta, S., and Datta, S. (2008) Reconstruction of genetic association networks from microarray data: a partial least squares approach. Bioinformatics, 24(4), 561–568.

Examples

# small example using PLSnet with 3 latent PLS 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=PLSnet(X1)
print(round(s,4))

# small example using PLSnet with 2 latent PLS components,
# data rescaled, and scores symmetrized and rescaled
s2=PLSnet(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  0.2233  0.0760  0.0684 -0.6426 -0.4136
Gene 2  0.2233  1.0000 -0.2730 -0.4897  0.1553 -0.5104
Gene 3  0.0760 -0.2730  1.0000 -0.7279 -0.3966  0.3394
Gene 4  0.0684 -0.4897 -0.7279  1.0000 -0.2712  0.1311
Gene 5 -0.6426  0.1553 -0.3966 -0.2712  1.0000  0.0702
Gene 6 -0.4136 -0.5104  0.3394  0.1311  0.0702  1.0000
        Gene 1  Gene 2  Gene 3  Gene 4  Gene 5  Gene 6
Gene 1  1.0000  0.2571  0.1775  0.0016 -0.7549 -0.4211
Gene 2  0.2571  1.0000 -0.4443 -0.5476  0.2803 -0.7164
Gene 3  0.1775 -0.4443  1.0000 -1.0000 -0.6288  0.5013
Gene 4  0.0016 -0.5476 -1.0000  1.0000 -0.2044  0.1672
Gene 5 -0.7549  0.2803 -0.6288 -0.2044  1.0000 -0.1049
Gene 6 -0.4211 -0.7164  0.5013  0.1672 -0.1049  1.0000

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