backtrace

matrix S, containing the local scores

matrix K, containing the position k used for score maximization

if multiple k produce the maximal score, take either the
shortest k ("max") or the longest k ("min"); if <code>multib</code> is set to
"skip" the next unique k will be searched

multib

proceed backwards while score is increasing before
opening a new segment

nextmax

verb

back-tracing step: collect clustered segments from the scoring matrix
<code>S(i,c)</code> by back-tracing the position <code>j=k</code> which delivered
the maximal score at position <code>i</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.

backtrace: Back-tracing step of the `segmenTier` algorithm.

Description

Usage

Arguments