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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | segmenTier_0.1.2.tar.gz |
647.7 KiB |
0.1.2 |
rolling linux/noble R-4.5 | segmenTier_0.1.2.tar.gz |
649.7 KiB |
0.1.2 |
rolling source/ R- | segmenTier_0.1.2.tar.gz |
692.1 KiB |
0.1.2 |
latest linux/jammy R-4.5 | segmenTier_0.1.2.tar.gz |
647.7 KiB |
0.1.2 |
latest linux/noble R-4.5 | segmenTier_0.1.2.tar.gz |
649.7 KiB |
0.1.2 |
latest source/ R- | segmenTier_0.1.2.tar.gz |
692.1 KiB |
0.1.2 |
2026-04-26 source/ R- | segmenTier_0.1.2.tar.gz |
692.1 KiB |
0.1.2 |
2026-04-23 source/ R- | segmenTier_0.1.2.tar.gz |
692.1 KiB |
0.1.2 |
2026-04-09 windows/windows R-4.5 | segmenTier_0.1.2.zip |
969.2 KiB |
0.1.2 |
2025-04-20 source/ R- | segmenTier_0.1.2.tar.gz |
692.1 KiB |