hypergate: Machine Learning of Hyperrectangular Gating Strategies for High-Dimensional Cytometry

Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.

Version:	0.8.3
Depends:	R (≥ 3.5.0)
Imports:	stats, grDevices, utils, graphics
Suggests:	knitr, rmarkdown, flowCore, sp, rgeos
Published:	2020-02-06
Author:	Etienne Becht [cre, aut], Samuel Granjeaud [ctb]
Maintainer:	Etienne Becht <etienne.becht at protonmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	hypergate results

Documentation:

Reference manual:	hypergate.pdf
Vignettes:	Hypergate

Downloads:

Package source:	hypergate_0.8.3.tar.gz
Windows binaries:	r-devel: hypergate_0.8.3.zip, r-release: hypergate_0.8.3.zip, r-oldrel: hypergate_0.8.3.zip
macOS binaries:	r-release (arm64): hypergate_0.8.3.tgz, r-oldrel (arm64): hypergate_0.8.3.tgz, r-release (x86_64): hypergate_0.8.3.tgz, r-oldrel (x86_64): hypergate_0.8.3.tgz
Old sources:	hypergate archive

Linking:

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