The \(p \times p\) target Gpos is computed from the \(n \times
p\) data matrix. It it a modified version of target G. In particular,
it completely ignores negative correlations and computes the mean
correlation \(\bar{r}\) using the positive ones only.
Usage
targetGpos(x, genegroups)
Value
A \(p \times p\) matrix.
Arguments
x
A \(n \times p\) data matrix.
genegroups
A list of genes obtained using the database KEGG, where
each entry itself is a list of pathway names this genes belongs to. If a
gene does not belong to any gene functional group, the entry is NA.
Author
Monika Jelizarow and Vincent Guillemot
References
J. Schaefer and K. Strimmer, 2005. A shrinkage
approach to large-scale covariance matrix estimation and implications for
functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32.
M.
Jelizarow, V. Guillemot, A. Tenenhaus, K. Strimmer, A.-L. Boulesteix, 2010.
Over-optimism in bioinformatics: an illustration. Bioinformatics. Accepted.
# A short example on a toy dataset# require(SHIP)data(expl)
attach(expl)
tar <- targetGpos(x,genegroups)
which(tar[upper.tri(tar)]!=0) # not many non zero coefficients !