plot.clustering

a "clustering" object as returned by
<code><a rd-options="" href="/link/clusterTimeseries?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::clusterTimeseries">clusterTimeseries</a></code>

a numeric or string vector indicating the clusterings to be plotted;
specifically the column numbers or names in the matrix of clusterings
in <code>cset$clusters</code>; if missing all columns will be plotted
and the calling code must take care of properly assigning <code>par(mfcol)</code>
or <code>layout</code> for the plot

if <code>TRUE</code> and the clustering is yet unsorted a cluster
sorting will be calculated based on "ccor" cluster-cluster similarity
matrix <code>x$Ccc</code>; see <code><a rd-options="" href="/link/sortClusters?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::sortClusters">sortClusters</a></code>

sort

optionally x-values to use as x-axis (e.g. to reflect absolute
chromosomal coordinates)

xaxis

list of axes to plot, numbers as used as first argument
in function <code>axis</code>

axes

argument <code>pch</code> (symbol) for plot

plot "clustering" horizontally at y-axis

ylabh

additional arguments to plot, eg. to set point <code>cex</code>

plot the clustering object returned by <code><a rd-options="" href="/link/clusterTimeseries?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::clusterTimeseries">clusterTimeseries</a></code>

A dynamic programming solution to segmentation based on
maximization of arbitrary similarity measures within segments.
The general idea, theory and this implementation are described in
Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.
In addition to the core algorithm, the package provides time-series
processing and clustering functions as described in the publication.
These are generally applicable where a `k-means` clustering yields
meaningful results, and have been specifically developed for
clustering of the Discrete Fourier Transform of periodic gene
expression data (`circadian' or `yeast metabolic oscillations').
This clustering approach is outlined in the supplemental material of
Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here
is used as a basis of segment similarity measures. Notably, the
time-series processing and clustering functions can also be used as
stand-alone tools, independent of segmentation, e.g., for
transcriptome data already mapped to genes.

Rainer Machne

segmenTier

Similarity-Based Segmentation of Multidimensional Signals

plot.clustering function

a "clustering" object as returned by
<code><a rd-options='' href='clusterTimeseries'>clusterTimeseries</a></code>

if <code>TRUE</code> and the clustering is yet unsorted a cluster
sorting will be calculated based on "ccor" cluster-cluster similarity
matrix <code>x$Ccc</code>; see <code><a rd-options='' href='sortClusters'>sortClusters</a></code>

plot the clustering object returned by <code><a rd-options='' href='clusterTimeseries'>clusterTimeseries</a></code>

plot.clustering: Plot method for the "clustering" object.

Description

Usage

Arguments

Value