SHIP-package: SHrinkage covariance Incorporating Prior knowledge

Description

The SHIP-package implements the shrinkage estimator of a covariance matrix given any covariance target, such as described by Schaefer and Strimmer in 2005. In addition, it proposes several targets based on biological knowledge extracted from the public database KEGG.

Arguments

Author

Monika Jelizarow and Vincent Guillemot

Details

To use the shrinkage estimator, one should just have at hand a data set in the form of a \(n \times p\) matrix, and a covariance target.

If one wishes to use the proposed targets, the data set should be compatible with KEGG, i.e. it should be possible to extract for each gene the pathways it belongs to. This information, for example, can be found in libraries such as hgu133plus2.db.

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.

Examples

Run this code


# A short example on a toy dataset
# require(SHIP)

data(expl)
attach(expl)

sig1 <- shrink.estim(x,targetD(x))
sig2 <- shrink.estim(x,targetF(x))
sig3 <- shrink.estim(x,targetCor(x,genegroups))
sig4 <- shrink.estim(x,targetG(x,genegroups))

paste(sig1[[2]],collapse=" ")
paste(sig2[[2]],collapse=" ")
paste(sig3[[2]],collapse=" ")
paste(sig4[[2]],collapse=" ")