fit.byname: Fit dependency model around one gene between two data sets.
In pint: Pairwise INTegration of functional genomics data

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

Takes a window from two datasets around chosen gene and fits a selected dependency model between windows.

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fit.cgh.mir.byname(X, Y, geneName, windowSize, ...)
          
fit.cgh.mrna.byname(X, Y, geneName, windowSize, ...)

`X,Y`	Data sets. Lists containing the following items: `data` Data in a matrix form. Genes are in columns and samples in rows. e.g. gene copy number. `info` Data frame which contains following information about genes in data matrix. `chr` Factor indicating the chrosome for the gene: (1 to 23, or X or Y `arm` Factor indicating the chromosomal arm for the gene ('p' or 'q') `loc` Location of the gene in base pairs. `pint.data` can be used to create data sets in this format.
`geneName`	The dependency model is calculated around this gene.
`windowSize`	Size of the data window.
`...`	Arguments to be passed to function `fit.dependency.model`

See fit.dependency.model for details about dependency models and parameters.

DependencyModel

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, 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

EM Algorithms for ML Factorial Analysis, Rubin D. and Thayer D. 1982. Psychometrika, vol. 47, no. 1.

Reults from this function: DependencyModel. fit.dependency.model. Calculating dependency models to chromosomal arm, chromosome or genome screen.cgh.mrna. For calculation of latent variable z: link{z.expectation}.

data(chromosome17)

model <- fit.cgh.mrna.byname(geneExp,geneCopyNum,"ENSG00000132361",10)
## With different model parameters (pCCA)
model2 <- fit.cgh.mrna.byname(geneExp,geneCopyNum,"ENSG00000132361",10,zDimension=5,priors=list(Nm.wxwy.sigma = NULL))