Numero: Statistical Framework to Define Subgroups in Complex Datasets

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

Downloads:

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 binaries: r-release: Numero_1.0.3.tgz, r-oldrel: Numero_1.0.3.tgz

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