ulrb: Unsupervised Learning Based Definition of Microbial Rare Biosphere

A tool to define rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing human decision with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is an abundance score for each phylogenetic unit. For validation of this method to several kinds of molecular data and environments, please see Pascoal et al, 2023 (in preparation). Preliminary data suggest this method also works well for non-microbiome data, if there is a species abundance table.

Version: 0.1.3
Depends: R (≥ 2.10)
Imports: cluster, dplyr, ggplot2, purrr, rlang, stats, tidyr, clusterSim, gridExtra
Suggests: knitr, rmarkdown, stringr, testthat (≥ 3.0.0), vegan
Published: 2023-11-17
Author: Francisco Pascoal ORCID iD [aut, cre], Paula Branco ORCID iD [aut], Luís Torgo ORCID iD [aut], Rodrigo Costa ORCID iD [aut], Catarina Magalhães ORCID iD [aut]
Maintainer: Francisco Pascoal <fpascoal1996 at gmail.com>
BugReports: https://github.com/pascoalf/ulrb/issues
License: GPL (≥ 3)
URL: https://pascoalf.github.io/ulrb/
NeedsCompilation: no
Citation: ulrb citation info
Materials: README
CRAN checks: ulrb results

Documentation:

Reference manual: ulrb.pdf
Vignettes: Glossary
Integration of ulrb in a simple microbial ecology workflow
Alternative classifications with ulrb
Tutorial to define rare biosphere with ulrb

Downloads:

Package source: ulrb_0.1.3.tar.gz
Windows binaries: r-devel: ulrb_0.1.3.zip, r-release: ulrb_0.1.3.zip, r-oldrel: ulrb_0.1.3.zip
macOS binaries: r-release (arm64): ulrb_0.1.3.tgz, r-oldrel (arm64): ulrb_0.1.3.tgz, r-release (x86_64): ulrb_0.1.3.tgz

Linking:

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