Package: segmenTier 0.1.2

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.

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

segmenTier_0.1.2.tar.gz
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segmenTier_0.1.2.tgz(r-4.4-x86_64)segmenTier_0.1.2.tgz(r-4.4-arm64)segmenTier_0.1.2.tgz(r-4.3-x86_64)segmenTier_0.1.2.tgz(r-4.3-arm64)
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segmenTier.pdf |segmenTier.html✨
segmenTier/json (API)

Peer review:

Bug tracker:https://github.com/raim/segmentier/issues

On CRAN:

15 exports 3 stars 1.46 score 1 dependencies 2 mentions 8 scripts 114 downloads

Last updated 4 years agofrom:a639754d74. Checks:OK: 9. Indexed: yes.

Exports:ashbacktracecalculateScoreclusterCor_cclusterTimeseriescolorClustersflowclusterTimeserieslog_1plotdevplotSegmentationprocessTimeseriessegmentCluster.batchsegmentClusterssetVarySettingssortClusters

Dependencies:Rcpp