Contribution of each sample to a dependency model, and contribution of each variable.
The fitted dependency model.
Data sets used in fitting the dependency modeling functions
z.effects gives the contribution of each sample to the dependency
score. This is approximated by projecting original data to first principal
Wz. This is possible only when the data window is
smaller than half the number of samples.
W.effects gives the contribution of each variable to the observed
dependency. This is approximated with the loadings of the first principal
Original data can be retrieved by locating the row in
which has the same variable (gene) name than
z.effects gives a projection vector over the samples and
W.effects gives a projection vector over the variables.
Dependency Detection with Similarity Constraints, Lahti et al., 2009 Proc. MLSP'09 IEEE International Workshop on Machine Learning for Signal Processing, See http://www.cis.hut.fi/lmlahti/publications/mlsp09_preprint.pdf
A Probabilistic Interpretation of Canonical Correlation Analysis, Bach Francis R. and Jordan Michael I. 2005 Technical Report 688. Department of Statistics, University of California, Berkley. http://www.di.ens.fr/~fbach/probacca.pdf
Probabilistic Principal Component Analysis, Tipping Michael E. and Bishop Christopher M. 1999. Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611–622. http://research.microsoft.com/en-us/um/people/cmbishop/downloads/Bishop-PPCA-JRSS.pdf
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#data(chromosome17) #window <- fixed.window(geneExp, geneCopyNum, 150, 10) ### pSimCCA model around one gene #depmodel <- fit.dependency.model(window$X, window$Y) ## Conversion from DependencyModel to GeneDependencyModel so that ## gene name and location can be stored ##depmodel <- as(depmodel,"GeneDependencyModel") #setGeneName(depmodel) <- window$geneName #setLoc(depmodel) <- window$loc #barplot(z.effects(depmodel, geneExp, geneCopyNum)) ## Plot the contribution of each genes to the model. ## Only the X component is plotted ## here since Wx = Wy (in SimCCA) #barplot(W.effects(depmodel, geneExp, geneCopyNum)$X) ## plot.DpenendencyModel shows also sample and variable effects #plot(depmodel,geneExp,geneCopyNum)
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