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) . 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) ), 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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0.1.2 by Rainer Machne, 3 years ago

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Authors: Rainer Machne , Douglas B. Murray , Peter F. Stadler

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Suggests flowMerge, flowClust, flowCore, knitr, rmarkdown

Linking to Rcpp

See at CRAN