The mosaic module provides the
definition of the
mosaic class.
See the bottom of the page for an example of how the class can be used.
hclust.hclust.mosaic
objects. The only required argument is data, which can
be a matrix, a data frame, or any object with an
as.data.frame method. If the optional argument
center has a true value, then the rows of the
data matrix will be shifted so the mean across each row
is zero; by default, the center argument is false. If
the optional argument usecor is true, then the rows
of the data matrix will be rescaled to have variance
one. By default, the usecor argument is false. The
sample.metric and gene.metric are strings
describing the distance metric to be used for clustering. These
argument must be valid inputs to the distance.matrix function. By default, both metric
arguments are "euclid", which means that the clustering algorithms
will use Euclidean distance. The final optional argument,
name, which defaults to the empty string, is a
character string describing the object, which will be used to
label plots.mosaic object that is its first argument. All
the remaining arguments are optional. The plot consists of two
primary parts. The top part is a dendrogram of the clustered genes
(rows) in the data of the object. The bottom part is a false color
"mosaic" plot similar to those introduced by Mike Eisen. The columns
in this image are sorted in the same order as the leaves in the
dendrogram. By default, the rows in the image are ordered by the
sample clustering that was computed when the mosaic object was
constructed. You can override this ordering by supplying the results
of a call to hclust as the optional
sample.clust argument, in which case the alternate
cluster order will be used. The optioal arguments
center and limits control the display of
the image. If the center argument has a true value,
then each gene row of the data matrix will be centered to have mean
zero for display purposes. If a limits argument is supplied, then it
should be a vector with two values, like c(-4,
4). These values are used to truncate the data values for
display purposes; most images can be improved by choosing
appropriate limits. The optional main argument is used
as a title for the entire plot; it defaults to the name
attribute of the mosaic object. The
other two arguments, gene.classes and
show.labels provide the option to add additional pieces
to the plot. The gene.classes argument should be a
positive integer. If supplied, then colored bars will be added
between the dendrogram and the mosaic image to highlight the number
of selected clusters. If the show.labels argument is
supplied as a positive integer, then it has the same effect for
clustered samples, putting colored bars along the left edge of the
image. You can also supply a vector as the show.labels
argument. In this case, the length of the vector should equal the
number of samples, and each entry in the vector should be an integer
representing a color to use for that sample in the left edge color
bar.object,
using the specified labels in the specified
colors.An object of the mosaic class
represents the results of two-way clustering similar to that used in
the plots introduced by Mike Eisen. We provide the ability to cluster
genes and samples using independent metrics, and also provide a fairly
flexible set of tools for producing the final plot. At present, we
only combine one dendrogram (for the genes) with a false color image,
primarily because we don't know how to rotate dendrograms in S-Plus.
data) and a string describing a distance metric and
returns the distance matrix whose entries are the distances between
columns in the data. Valid vaues for the metric
argument are
graphsheet(num.image.colors=3, num.image.shades='48,48',
image.color.table="0,255,0|0,0,0|255,0,0")
n.sample <- 10
n.gene <- 100
faker <- matrix(rnorm(n.sample*n.gene), n.gene, n.sample)
fake.mosaic <- mosaic(faker, 'pearson', 'spearman', name='pseudo')
plot(fake.mosaic)
plot(fake.mosaic, gene.classes=6, show.labels=7)