setVarySettings

exponent to scale similarity matrices

the scoring function to be used: "ccor", "icor" or "ccls"

segment length penalty. Note, that this is not a strict
cut-off but defined as a penalty that must be "overcome" by
good score.

segment length penalty for nuisance cluster. Mn&lt;M will
allow shorter distances between "real" segments; only used in
scoring functions "ccor" and "icor"

a cluster "dissimilarity" only used for pure cluster-based
scoring w/o cluster similarity measures in scoring function
"ccls".

the similarity score to be used for nuisance clusters
in the cluster similarity matrices

go backwards while score is increasing before
opening a new segment, default is TRUE

nextmax

handling of multiple k with max. score in forward
phase, either "min" (default) or "max"

multi

handling of multiple k with max. score in back-trace
phase, either "min" (default), "max" or "skip"

multib

Generates the parameter list (<code>varySettings</code>) for
<code><a rd-options="" href="/link/segmentCluster.batch?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::segmentCluster.batch">segmentCluster.batch</a></code>, using defaults
for all parameters not passed.

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

setVarySettings function

Generates the parameter list (<code>varySettings</code>) for
<code><a rd-options='' href='segmentCluster.batch'>segmentCluster.batch</a></code>, using defaults
for all parameters not passed.

Parameters for <code><a rd-options='' href='segmentCluster.batch'>segmentCluster.batch</a></code>. — setVarySettings

Parameters for <code><a rd-options='' href='segmentCluster.batch'>segmentCluster.batch</a></code>.

Parameters for <code><a rd-options="" href="/link/segmentCluster.batch?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::segmentCluster.batch">segmentCluster.batch</a></code>.

setVarySettings: Parameters for `segmentCluster.batch`.

Description

Usage

Arguments

Value