Similarity-Based Segmentation of Multidimensional Signals
asinh data transformation
Back-tracing step of the segmenTier algorithm.
segmenTier's core dynamic programming routine in Rcpp
Calculates position-cluster correlations for scoring function "icor".
Cluster a processed time-series with k-means.
Assign colors to clusters.
Cluster a processed time-series with flowClust & flowMerge.
log transformation handling zeros by adding 1
Experimental: AIC/BIC for kmeans
Pearson product-moment correlation coefficient
Plot method for the "clustering" object.
Plot method for the "segments" object.
Plot method for the "timeseries" object.
Switch between plot devices.
Summary plot for the segmenTier pipeline.
Print method for segmentation result from segmentClusters.
Process a time-series for clustering and segmentation.
Batch wrapper for segmentClusters.
Run the segmenTier algorithm.
segmenTier : cluster-based segmentation from a sequential clustering
Parameters for segmentCluster.batch.
Sort clusters by similarity.
Transcriptome time-series from budding yeast.
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.