High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues [Makinen V-P et al. (2011) J Proteome Res 11:1782-1790, <doi:10.1021/pr201036j>]. The framework includes the necessary functions to import large data files, to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results in scalable vector graphics.
Version: | 1.0.3 |
Imports: | Rcpp (≥ 0.11.4) |
LinkingTo: | Rcpp |
Suggests: | knitr, rmarkdown |
Published: | 2017-12-01 |
Author: | Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre] |
Maintainer: | Ville-Petteri Makinen <vpmakine at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | Numero results |
Reference manual: | Numero.pdf |
Vignettes: |
A practical guide to Numero |
Package source: | Numero_1.0.3.tar.gz |
Windows binaries: | r-devel: Numero_1.0.3.zip, r-release: Numero_1.0.3.zip, r-oldrel: Numero_1.0.3.zip |
OS X El Capitan binaries: | r-release: Numero_1.0.3.tgz |
OS X Mavericks binaries: | r-oldrel: Numero_1.0.3.tgz |
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