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")data
info
chr
arm
loc
pint.data can be used to create data sets in this format.
15 if the ratio would be greater than 15
If anything else, the model is specified by the given parameters.
"models"
and "data.frame"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.
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.
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.
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)
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