plot.pREC_S {RJaCGH} | R Documentation |
An image plot showing the results of pREC_S
on a
group of arrays.
## S3 method for class 'pREC_S': plot(x, array.labels = NULL, stats = TRUE, col = NULL, breaks = NULL, dend = TRUE, method = "single", Chrom = NULL, ...)
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 |
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
.
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. |
Oscar M. Rueda and Ramon Diaz Uriarte
Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122
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)