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. For more details see Etienne Becht, Yannick Simoni, Elaine Coustan-Smith, Maximilien Evrard, Yang Cheng, Lai Guan Ng, Dario Campana and Evan Newell (2018) <doi:10.1101/278796>.

Version: 0.7
Depends: R (≥ 3.0.0)
Imports: stats, grDevices, utils, graphics, lattice
Suggests: knitr, rmarkdown, flowCore, sp, rgeos
Published: 2018-05-14
Author: Etienne Becht [cre, aut]
Maintainer: Etienne Becht <etienne_becht at immunol.a-star.edu.sg>
License: GPL-3
NeedsCompilation: no
CRAN checks: hypergate results

Downloads:

Reference manual: hypergate.pdf
Vignettes: Hypergate
Package source: hypergate_0.7.tar.gz
Windows binaries: r-devel: hypergate_0.7.zip, r-release: hypergate_0.7.zip, r-oldrel: hypergate_0.7.zip
OS X binaries: r-release: hypergate_0.7.tgz, r-oldrel: hypergate_0.7.tgz

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