screen: Fits dependency models to chromosomal arm, chromosome or the...
In pint: Pairwise INTegration of functional genomics data

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

Fits dependency models for whole chromosomal arm, chromosome or genome depending on arguments with chosen window size between two data sets.

screen.cgh.mrna(X, Y, windowSize = NULL, chromosome, arm, method =
"pSimCCA", params =
list(), max.dist = 1e7, outputType = "models", useSegmentedData =
TRUE, match.probes = TRUE, regularized = FALSE)

screen.cgh.mir(X, Y, windowSize, chromosome, arm, method = "", params = list(),
outputType = "models")

`X,Y`	Data sets. It is recommended to place gene/mirna expression data in X and copy number data in Y. Each is a list with the following items: `data` Data in a matrix form. Genes are in rows and samples in columnss. e.g. gene copy number. `info` Data frame which contains following information about genes in data matrix. `chr` Number indicating the chrosome for the gene: (1 to 24). Characters 'X' or 'Y' can be used also. `arm` Character 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.
`chromosome`	Specify the chromosome for model fitting. If missing, whole genome is screened.
`arm`	Specify chromosomal arm for model fitting. If missing, both arms are modeled.
`windowSize`	Determine the window size. This specifies the number of nearest genes to be included in the chromosomal window of the model, and therefore the scale of the investigated chromosomal region. If not specified, using the default ratio of 1/3 between features and samples or `15` if the ratio would be greater than 15
`method`	Dependency screening can utilize any of the functions from the package dmt (at CRAN). Particular options include 'pSimCCA' probabilistic similarity constrained canonical correlation analysis Lahti et al. 2009. This is the default method. 'pCCA' probabilistic canonical correlation analysis Bach & Jordan 2005 'pPCA' probabilistic principal component analysis Tipping & Bishop 1999 'pFA' probabilistic factor analysis Rubin & Thayer 1982 'TPriorpSimCCA' probabilistic similarity constrained canonical correlation analysis with possibility to tune T prior (Lahti et al. 2009) If anything else, the model is specified by the given parameters.
`params`	List of parameters for the dependency model. sigmas Variance parameter for the matrix normal prior distribution of the transformation matrix T. This describes the deviation of T from H H Mean parameter for the matrix normal prior distribution prior of transformation matrix T zDimension Dimensionality of the latent variable mySeed Random seed. covLimit Convergence limit. Default depends on the selected method: 1e-3 for pSimCCA with full marginal covariances and 1e-6 for pSimCCA in other cases.
`max.dist`	Maximum allowed distance between probes. Used in automated matching of the probes between the two data sets based on chromosomal location information.
`outputType`	Specifies the output type of the function. possible values are `"models"` and `"data.frame"`
`useSegmentedData`	Logical. Determines the useage of the method for segmented data
`match.probes`	To be used with segmented data, or nonmatched probes in general. Using nonmatched features (probes) between the data sets. Development feature, to be documented later.
`regularized`	Regularization by nonnegativity constraints on the projections. Development feature, to be documented later.

Function screen.cgh.mrna assumes that data is already paired. This can be done with pint.match. It takes sliding gene windows with fixed.window and fits dependency models to each window with fit.dependency.model function. If the window exceeds start or end location (last probe) in the chromosome in the fixed.window function, the last window containing the given probe and not exceeding the chromosomal boundaries is used. In practice, this means that dependency score for the last n/2 probes in each end of the chromosome (arm) will be calculated with an identical window, which gives identical scores for these end position probes. This is necessary since the window size has to be fixed to allow direct comparability of the dependency scores across chromosomal windows.

Function screen.cgh.mir calculates dependencies around a chromosomal window in each sample in X; only one sample from X will be used. Data sets do not have to be of the same size andX can be considerably smaller. This is used with e.g. miRNA data.

If method name is specified, this overrides the corresponding model parameters, corresponding to the modeling assumptions of the specified model. Otherwise method for dependency models is determined by parameters.

Dependency scores are plotted with dependency score plotting.

The type of the return value is defined by the the function argument outputType.

With the argument outputType = "models", the return value depends on the other arguments; returns a ChromosomeModels which contains all the models for dependencies in chromosome or a GenomeModels which contains all the models for dependencies in genome.

With the argument outputType = "data.frame", the function returns a data frame with eachs row representing a dependency model for one gene. The columns are: geneName,dependencyScore,chr,arm,loc.

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

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

To fit a dependency model: fit.dependency.model. ChromosomeModels holds dependency models for chromosome, GenomeModels holds dependency models for genome. For plotting, see: dependency score plotting

data(chromosome17)

## pSimCCA model on chromosome 17

models17pSimCCA <- screen.cgh.mrna(geneExp, geneCopyNum,
                                     windowSize = 10, chr = 17)
                                    
plot(models17pSimCCA)

## pCCA model on chromosome 17p with 3-dimensional latent variable z
models17ppCCA <- screen.cgh.mrna(geneExp, geneCopyNum,
                                   windowSize = 10,
                                   chromosome = 17, arm = 'p',method="pCCA", 
	      	 	           params = list(zDimension = 3))
plot(models17ppCCA)