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 inMachne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.In addition to the core algorithm, the package provides time-seriesprocessing and clustering functions as described in the publication.These are generally applicable where a ‘k-means' clustering yieldsmeaningful results, and have been specifically developed forclustering of the Discrete Fourier Transform of periodic geneexpression data ('circadian’ or ‘yeast metabolic oscillations’).This clustering approach is outlined in the supplemental material ofMachne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and hereis used as a basis of segment similarity measures. Notably, thetime-series processing and clustering functions can also be used asstand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes.
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