plot.timeseries

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>

a string vector indicating the values to be plotted;
"total": plot of the total signal, summed over
the time-points, and indicating the applied threshold <code>low.thresh</code>;
note that the total levels may have been transformed (e.g. by
<code><a rd-options="" href="/link/log_1?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::log_1">log_1</a></code> or <code><a rd-options="" href="/link/ash?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::ash">ash</a></code>) depending on the arguments
<code>trafo</code> and <code>dc.trafo</code> in <code><a rd-options="" href="/link/processTimeseries?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::processTimeseries">processTimeseries</a></code>;
"timeseries": plot the complete time-series as a heatmap, where time is
plotted bottom-up on the y-axis and segmentation coordinates on the x-axis;

plot

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

xaxis

ylabh

additional arguments to plot of total signal

plot the processed time-series object returned from
<code><a rd-options="" href="/link/processTimeseries?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::processTimeseries">processTimeseries</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.timeseries function

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

a string vector indicating the values to be plotted;
"total": plot of the total signal, summed over
the time-points, and indicating the applied threshold <code>low.thresh</code>;
note that the total levels may have been transformed (e.g. by
<code><a rd-options='' href='log_1'>log_1</a></code> or <code><a rd-options='' href='ash'>ash</a></code>) depending on the arguments
<code>trafo</code> and <code>dc.trafo</code> in <code><a rd-options='' href='processTimeseries'>processTimeseries</a></code>;
"timeseries": plot the complete time-series as a heatmap, where time is
plotted bottom-up on the y-axis and segmentation coordinates on the x-axis;

plot the processed time-series object returned from
<code><a rd-options='' href='processTimeseries'>processTimeseries</a></code>.

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

Description

Usage

Arguments