Description Usage Arguments Value Author(s) References Examples
Computes the connectivity scores for a network based on principal components.
1 2 |
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, |
PCnet |
a matrix of interactions between gene pairs based on principal components regression. |
The authors are Ryan Gill, Somnath Datta, and Susmita Datta. The software is maintained by Ryan Gill rsgill01@louisville.edu.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # 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))
|
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
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