Spectrum: Fast Adaptive Spectral Clustering for Single and Multi-View Data

A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. 'Spectrum' was developed for clustering complex single and multi-omic data. However, the method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.

Version: 0.6
Depends: R (≥ 3.5.0)
Imports: ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest
Suggests: knitr
Published: 2019-05-28
Author: Christopher R John, David Watson
Maintainer: Christopher R John <chris.r.john86 at gmail.com>
License: AGPL-3
NeedsCompilation: no
CRAN checks: Spectrum results

Downloads:

Reference manual: Spectrum.pdf
Vignettes: Spectrum
Package source: Spectrum_0.6.tar.gz
Windows binaries: r-devel: Spectrum_0.6.zip, r-release: Spectrum_0.6.zip, r-oldrel: Spectrum_0.6.zip
OS X binaries: r-release: Spectrum_0.6.tgz, r-oldrel: Spectrum_0.6.tgz
Old sources: Spectrum archive

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