An R package for data mining in microbial community ecology
The increasing volume and complexity of microbiome omics data pose a challenge for the downstream data analysis. Although numerous R packages exist in this field, it remains difficult to perform data mining in an efficient and comprehensive manner. Therefore, we developed the R microeco package (abbreviated and pronounced as [miːkəu]).
- R6 Class to store and analyze data: flexible and modularized
- Data preprocessing and normalization
- Taxonomic abundance visualization
- Venn diagram
- Phylogenetic tree visualization
- Niche analysis
- Alpha diversity
- Beta diversity
- Differential abundance test
- Machine learning
- Null model analysis
- Network analysis
- Environmental data analysis
- Functional prediction
- Metabolites analysis
If you do not already have R/RStudio installed, follow these steps:
Open RStudio -> Tools -> Global Options -> Packages, select the appropriate mirror in Primary CRAN repository.
Install microeco package from CRAN.
install.packages("microeco")Or install the latest development version from Github.
# If devtools package is not installed, first install it
install.packages("devtools")
devtools::install_github("ChiLiubio/microeco")A detailed online tutorial (https://chiliubio.github.io/microeco_tutorial/) is provided with the package.
For offline use, the tutorial can also be downloaded and opened locally (https://github.com/ChiLiubio/microeco_tutorial/releases).
To view these links in an R session, run ?microeco.
Please use a class name to search for its help document (e.g., ?microtable) rather than searching for individual functions therein.
Before creating a new issue in the Issues,
please read the guideline (https://chiliubio.github.io/microeco_tutorial/notes.html#github-issues).
To create a basic microtable object directly from outputs of other tools/platforms (e.g. QIIME2, HUMAnN, Kraken2 and phyloseq),
please use the file2meco package (https://github.com/ChiLiubio/file2meco).
To help users familiarize themselves with the package using multi-omics data,
we also provide a systematic protocol (https://github.com/ChiLiubio/microeco_protocol_v1).
Chi Liu, Xiangzhen Li, Felipe R. P. Mansoldo, Tong Chen, Fanzheng Meng, Ruixiang Tang, Siyu Zhou, Qinghua Yang, Ruixin Shao & Minjie Yao. microeco 2: A comprehensive R package for downstream analysis of microbiome omics data. iMeta, 2026, 5: e70132. https://doi.org/10.1002/imt2.70132
Chi Liu, Felipe R. P. Mansoldo, Hankang Li, Alane Beatriz Vermelho, Raymond Jianxiong Zeng, Xiangzhen Li & Minjie Yao. A workflow for statistical analysis and visualization of microbiome omics data using the R microeco package. Nature Protocols, 2026, 21: 1300–1324. https://doi.org/10.1038/s41596-025-01239-4
Chi Liu, Yaoming Cui, Xiangzhen Li & Minjie Yao. microeco: an R package for data mining in microbial community ecology. FEMS Microbiology Ecology, 2021, 97(2): fiaa255. https://doi.org/10.1093/femsec/fiaa255
We welcome any contribution, including but not limited to code, idea and tutorial. Please report errors and questions on Github Issues. Any contribution via Pull requests will be appreciated. By participating in this project you agree to abide by the terms outlined in the Contributor Code of Conduct.