z.effects: The model parameters z and W
In antagomir/pint: Pairwise INTegration of functional genomics data

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/z.effects.R

Contribution of each sample to a dependency model, and contribution of each variable.

1	z.effects(model, X, Y = NULL)

`model`	The fitted dependency model.
`X, Y`	Data sets used in fitting the dependency modeling functions (`screen.cgh.mrna` or `link{fit.dependency.model}`). Note: Arguments must be given in the same order as in `fit.dependency.model` or `screen.cgh.mrna`. Only `X` is needed for dependency model for one data set.

z.effects gives the contribution of each sample to the dependency score. This is approximated by projecting original data to first principal component of 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 component of Wz

Original data can be retrieved by locating the row in X (or Y) which has the same variable (gene) name than model.

z.effects gives a projection vector over the samples and W.effects gives a projection vector over the variables.

Olli-Pekka Huovilainen ohuovila@gmail.com and Leo Lahti leo.lahti@iki.fi

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

DependencyModel-class, screen.cgh.mrna

#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)