Description Usage Arguments Value Author(s) References Examples
Computes the connectivity scores for a network based on a specified regression method.
1 2 |
data |
microarray dataset with genes in columns and samples in rows. |
f |
regression method. |
recenter.data |
indicates whether data should be recentered. |
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. |
... |
Any additional arguments for f. |
gennet |
a matrix of interactions between gene pairs based on the regression method supplied by the user. |
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 15 | # small example using gennet with a user-defined ridge regression
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))
## Not run: ourRR=function(X,y,lambda=1){
## solve(t(X)%*%X+diag(ncol(X)))%*%t(X)%*%y}
## Not run: gennet(X1,f=ourRR,recenter.data=
## TRUE,rescale.data=TRUE,symmetrize.scores=
## TRUE,rescale.scores=FALSE)
# compare results with RRnet
RRnet(X1)
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