dna (version 2.1-2)

gennet: General Regression network

Description

Computes the connectivity scores for a network based on a specified regression method.

Usage

gennet(data, f, recenter.data=FALSE, rescale.data=FALSE, 
symmetrize.scores=FALSE, rescale.scores = FALSE, ...)

Arguments

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.

Value

gennet

a matrix of interactions between gene pairs based on the regression method supplied by the user.

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

# NOT RUN {
# 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)
# }