Package: ulrb 0.1.8

ulrb: Unsupervised Learning Based Definition of Microbial Rare Biosphere

A tool to define the rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing arbitrary thresholds 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 table. This method also works for non-microbiome data.

Authors:Francisco Pascoal [aut, cre], Paula Branco [aut], Luís Torgo [aut], Rodrigo Costa [aut], Catarina Magalhães [aut]

ulrb_0.1.8.tar.gz
ulrb_0.1.8.zip(r-4.7)ulrb_0.1.8.zip(r-4.6)ulrb_0.1.8.zip(r-4.5)
ulrb_0.1.8.tgz(r-4.6-any)ulrb_0.1.8.tgz(r-4.5-any)
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ulrb_0.1.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ulrb/json (API)

# Install 'ulrb' in R:
install.packages('ulrb', repos = c('https://pascoalf.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pascoalf/ulrb/issues

Pkgdown/docs site:https://pascoalf.github.io

Datasets:
  • nice - V4-V5 16S rRNA gene amplicons, clean OTU table
  • nice_env - Metadata of samples from OTU tables
  • nice_raw - V4-V5 16S rRNA gene amplicons, raw OTU table
  • nice_tidy - V4-V5 16S rRNA gene amplicons, clean OTU table in tidy/long format

On CRAN:

Conda:

5.53 score 6 stars 14 scripts 256 downloads 11 exports 42 dependencies

Last updated from:e8271ff0b9. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK188
source / vignettesOK275
linux-release-x86_64OK207
macos-release-arm64OK117
macos-oldrel-arm64OK159
windows-develOK155
windows-releaseOK156
windows-oldrelOK159
wasm-releaseOK151

Exports:check_avgSilcheck_CHcheck_DBdefine_rbevaluate_kevaluate_sample_kplot_ulrbplot_ulrb_clusteringplot_ulrb_silhouetteprepare_tidy_datasuggest_k

Dependencies:ade4classcliclusterclusterSimcpp11dplyre1071farvergenericsggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSpillarpixmappkgconfigproxypurrrR6RColorBrewerRcppRcppArmadillorlangS7scalesspstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Integration of ulrb in a simple microbial ecology workflow
Ecological analysis of microbial rare biosphere defined by ulrb | Quick overview of N-ICE dataset | (a) Load and clean OTU table | (b) Rarefy samples | (b) Classify OTUs into rare, undetermined or abundant (with define_rb() function); | (c) Merge OTU table with metadata information | (e) Calculate and plot diversity metrics against environmental variables. | Alpha diversity plots | Beta diversity | Final considerations | References
Rendered fromeco-analysis.Rmdusingknitr::rmarkdown

Last update: 2025-07-07
Started: 2023-04-12

Alternative classifications with ulrb
Explore alternative classifications | Index | Classical example | Prepare data | Default is k = 3 | Apply 2 classifications: Rare vs Abundant | Apply more complicated classification, k>3 | Why k = 1 is non-sense | What is the maximum value of k and why? | Approaches to evaluate k | Fine grained analysis | Automatic k selection | Everything automatic | How each index behaves across all possible values of k? | References
Rendered fromexplore-classifications.Rmdusingknitr::rmarkdown

Last update: 2025-05-14
Started: 2023-06-06

Tutorial to define rare biosphere with ulrb
Unsupervised Learning Based Definition Of Microbial Rare Biosphere | Brief note on nomenclature | Pre-processing of data prior to clustering algorithm | Load and clean abundance table | Transform abundance table into tidy/long format | Apply definition of rare biosphere with unsupervised learning | Fully automated version | Verify results | (1) Rank Abunddance Curve (RAC) to verify clustering | (2) Silhouette plots | Sanity check summary | References
Rendered fromulrb-vignet.Rmdusingknitr::rmarkdown

Last update: 2025-05-14
Started: 2023-01-10

Glossary
Why we made this tutorial | Phylogenetic units | Phylogenetic units translated to machine learning | Variables and features | Abundance classification | Summary
Rendered fromGlossary.Rmdusingknitr::rmarkdown

Last update: 2023-11-01
Started: 2023-09-15