segmenTier - Similarity-Based Segmentation of Multidimensional Signals
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.
