pint (version 1.22.0)

fit.byname: Fit dependency model around one gene between two data sets.

Description

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

Usage

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

Arguments

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

Value

DependencyModel

Details

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

References

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.

See Also

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}.

Examples

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))