pint (version 1.22.0)

plot: Dependency score plotting

Description

Plot the contribution of the samples and variables to the dependency model or dependency model fitting scores of chromosome or genome.

Usage

"plot"(x, X, Y, ann.types = NULL, ann.cols = NULL, legend.x = 0,  legend.y = 1, legend.xjust = 0, legend.yjust = 1, order = FALSE,  cex.z = 0.6, cex.WX = 0.6, cex.WY = 0.6, ...)

"plot"(x, hilightGenes = NULL, showDensity = FALSE, showTop = 0,
	topName = FALSE, type = 'l', xlab = 'gene location', ylab = 'dependency score',
	main = NULL,
	pch = 20, cex = 0.75, tpch = 3, tcex = 1, xlim = NA, ylim = NA,...)
"plot"(x, hilightGenes = NULL, showDensity = FALSE, showTop = 0, 
	topName = FALSE, onePlot = FALSE, type = 'l', ylab = "Dependency Scores", 
	xlab = "Gene location (chromosome)", main = "Dependency Scores in All Chromosomes",
	pch = 20, cex = 0.75, tpch = 20, tcex = 0.7, mfrow = c(5,5), mar = c(3,2.5,1.3,0.5), 
	ps = 5, mgp = c(1.5,0.5,0),ylim=NA,...)

Arguments

x

GeneDependencyModel-class, ChromosomeModels-class, GenomeModels-class; models to be plotted.

X, Y

data sets used in dependency modeling.

ann.types

a factor for annotation types for samples. Each value corresponds one sample in datasets. Colors are used to indicate different types.

ann.cols

colors used to indicate different annotation types. Gray scale is used if 'NULL' given.

legend.x, legend.y

the x and y co-ordinates to be used to position the legend for annotation types.

legend.xjust, legend.yjust

how the legend is to be justified relative to the legend x and y location. A value of 0 means left or top justified, 0.5 means centered and 1 means right or bottom justified.

order

logical; if 'TRUE', values for sample contributions are ordered according to their values.

cex.z, cex.WX, cex.WY

Text size for variable names.

hilightGenes

vector of strings; Name of genes to be hilighted with dots.

showDensity

logical; if 'TRUE' small vertical lines are drwan in the bottom of the plot under each gene.

showTop

numeric; Number of models with highest dependencies to be hilighted. A horizontal dashed line is drawn to show threshold value. With 0 no line is drawn.

topName

logical; If TRUE, gene names are printed to hilighted models with highest dependecies. Otherwise hilighted models are numbered according to their rank in dependency score.

type, xlab, ylab, main

plot type and labels. See plot for details. A text for chromosome (and arm if only models from one arm is plotted) is used in main if NULL is given. In plot.GenomeModels, ylab and xlab affect only if onePlot is TRUE.

onePlot

If TRUE, all dependency scores are plotted in one plot window. Otherwise one plot window is used for each chromosome.

pch, cex

symbol type and size for hilightGenes. See points for details.

tpch, tcex

symbol type and size for genes with highest scores. See points for details.

ylim, xlim

axis limits. Default values are calculated from data. Lower limit for y is 0 and upper limit is either 1 or maximum score value. X limits are gene location range. See plot for details.

mfrow, mar, ps, mgp

chromosome plots' layout, marginals, text size and margin line. See par for details.

...

optional plotting parameters

Details

Function plots scores of each dependency model of a gene for the whole chromosome or genome according to used method. plot(x, cancerGenes = NULL, showDensity = FALSE, ...) is also usable and chosen according to class of models.

References

Dependency Detection with Similarity Constraints Lahti et al., MLSP'09. See http://www.cis.hut.fi/lmlahti/publications/mlsp09_preprint.pdf

See Also

DependencyModel-class, ChromosomeModels-class, GenomeModels-class, screen.cgh.mrna, screen.cgh.mir

Examples

data(chromosome17)

## pSimCCA model on chromosome 17p
models17ppSimCCA <- screen.cgh.mrna(geneExp, geneCopyNum, 10, 17, 'p')
plot(models17ppSimCCA,
     hilightGenes=c("ENSG00000108342", "ENSG00000108298"), showDensity = TRUE)

## Dependency model around 50th gene
model <- models17ppSimCCA[[50]]

## example annnotation types
ann.types <- factor(c(rep("Samples 1 - 10", 10), rep("Samples 11 - 51", 41)))
plot(model, geneExp, geneCopyNum, ann.types, legend.x = 40, legend.y = -4,
     order = TRUE)