The microbiome R package facilitates phyloseq-based exploration and analysis of taxonomic profiling data. This vignette provides a brief overview with example data sets from published microbiome profiling studies. A more comprehensive tutorial is available on-line.
While we continue to maintain this R package, the development has been discontinued as we have shifted to supporting methods development based on the new TreeSummarizedExperiment data container, which provides added capabilities for multi-omics data analysis. Check the miaverse project for details.
In the microbiome R package, tools are provided for the manipulation, statistical analysis, and visualization of taxonomic profiling data. In addition to targeted case-control studies, the package facilitates scalable exploration of population cohorts. Whereas sample collections are rapidly accumulating for the human body and other environments, few general-purpose tools for targeted microbiome analysis are available in R. This package supports the independent phyloseq data format and expands the available toolkit in order to facilitate the standardization of the analyses and the development of best practices.
We welcome bug reports from the user community via the issue tracker and pull requests. See the Github site for source code and other details. These R tools are released under the Two-clause FreeBSD license.
Kindly cite the work as follows: “Leo Lahti et al. (Bioconductor, 2017-2020). Tools for microbiome analysis in R. Microbiome package version . URL: (http://microbiome.github.io/microbiome)
The on-line tutorial provides many additional tools and examples, with more thorough descriptions. This package and tutorials are work in progress. We welcome feedback, for instance via issue Tracker, pull requests, or via Gitter.
Thanks to all contributors. Financial support has been provided by Academy of Finland (grants 256950 and 295741), University of Turku.
This work relies heavily on the independent phyloseq package and data structures for R-based microbiome analysis developed by Paul McMurdie and Susan Holmes.