RJaCGH (version 2.0.4)

plot.pREC_S: Plot number of probes shared by pairs of arrays

Description

An image plot showing the results of pREC_S on a group of arrays.

Usage

"plot"(x, array.labels = NULL,  stats = TRUE, col = NULL, breaks = NULL, dend = TRUE, method = "single", Chrom = NULL, ...)

Arguments

x

An object of class pREC_S

array.labels

A vector for alternative labels for the arrays.

Chrom

Chromosome to plot. If NULL, all chromosomes are plotted.

stats

Logical. If TRUE, over every cell the number of common probes and the mean length is printed.

col

A vector of color codes for the image plot.

breaks

Breakpoints for the code color. Must be a vector of length length(col) + 1

dend

Logical. If TRUE, a clustering of arrays is performed with hclust and arrays reordered.

method

Clustering method to apply. See hclust. Default is 'single'.

...

Additional arguments passed to image

Value

A list with elements

probes

Matrix with the number of probes shared by every pair of arrays.

length

Matrix with the mean length of probe shared by every pair of arrays.

Details

First, the number of probes shared by every pair of arrays and their mean length is computed. The plot consists of a square with as many rows and as many columns as the number of arrays are. The more altered probes two arrays share the brighter the color is. The diagonals are turned off to improve the visibility of the groups. If dend is TRUE, a hierarchical clustering (method method) on arrays is performed based on the dissimilarity measure defined as: $1 - (inc.mat / max(inc.mat))$ where inc.mat is the matrix with the number of arrays shared by every pair of arrays. Then a dendrogram is plotted and the arrays are reordered. The diagonals of the plot are turned off to improve the perception of the relationships between arrays. Note that the number of probes shared depends on the parameters passed to pREC_S, such as the probability threshold p and the minimum number of arrays requiered to form a region freq.array.

References

Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122

See Also

pREC_S, hclust, image

Examples

y <- c(rnorm(100, 0, 1), rnorm(10, -3, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
Pos <- sample(x=1:500, size=230, replace=TRUE)
Pos <- cumsum(Pos)
Chrom <- rep(1:23, rep(10, 23))

jp <- list(sigma.tau.mu=rep(0.05, 4), sigma.tau.sigma.2=rep(0.03, 4),
           sigma.tau.beta=rep(0.07, 4), tau.split.mu=0.1, tau.split.beta=0.1)

z <- c(rnorm(110, 0, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
zz <- c(rnorm(90, 0, 1), rnorm(40, 3, 1),
       rnorm(100,0, 1)) 

fit.array.genome <- RJaCGH(y=cbind(y,z,zz),
Pos=Pos, Chrom=Chrom, model="Genome",
burnin=1000, TOT=1000, jump.parameters=jp, k.max = 4)
Reg1 <- pREC_S(fit.array.genome, p=0.4, freq.array=2,
alteration="Gain")
plot(Reg1)