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

1 2 |

`model` |
The fitted dependency model. |

`X, Y` |
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 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`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
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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