plotSegmentation

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

tset

a clusterings 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>

cset

a segmentation object as returned by
<code><a rd-options="" href="/link/segmentClusters?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::segmentClusters">segmentClusters</a></code> and <code><a rd-options="" href="/link/segmentCluster.batch?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::segmentCluster.batch">segmentCluster.batch</a></code>

sset

split segment plots by clustering plots

split

include the internal scoring matrices in the plot

plot.matrix

margins of individual plots, see <code>par</code>

further arguments to
<code><a rd-options="" href="/link/plot.clustering?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::plot.clustering">plot.clustering</a></code> (<code>cset</code>) and
<code><a rd-options="" href="/link/plot.segments?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::plot.segments">plot.segments</a></code> (<code>sset</code>). Note: these may
conflict and cause errors, but eg. a combination of <code>cex=0.5, lwd=3</code>
works, affecting cluster point size and segment line width, respectively.

Plot all objects from the segmentation pipeline, i.e. the processed
time-series, the clustering, the internal scoring matrices and
the final segments.

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

plotSegmentation function

a time-series object as returned by
<code><a rd-options='' href='processTimeseries'>processTimeseries</a></code>

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

a segmentation object as returned by
<code><a rd-options='' href='segmentClusters'>segmentClusters</a></code> and <code><a rd-options='' href='segmentCluster.batch'>segmentCluster.batch</a></code>

further arguments to
<code><a rd-options='' href='plot.clustering'>plot.clustering</a></code> (<code>cset</code>) and
<code><a rd-options='' href='plot.segments'>plot.segments</a></code> (<code>sset</code>). Note: these may
conflict and cause errors, but eg. a combination of <code>cex=0.5, lwd=3</code>
works, affecting cluster point size and segment line width, respectively.

plotSegmentation: Summary plot for the `segmenTier` pipeline.

Description

Usage

Arguments