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.
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