刘永鑫+袁军等：如何用好R进行微生物组分析？（综述）
- ①共整理324个用于微生物组分析的常用R包，并按照应用类别（多样性、差异性、生物标志物、相关性网络、功能预测等）进行分类；
- ②对用于微生物组分析的集成R包（phyloseq, microbiome、MicrobiomeAnalystR、Animalcules、microeco、amplicon）进行了系统地分类，并总结了其优点和局限性；
- ③全面回顾了用于微生物组分析的R包，总结了大部分常见的微生物组分析内容，并形成了最适合微生物组分析的管线，所涉及代码已附于GitHub。
主编推荐语
如何从众多R包中选择合适、高效、便捷、易学的工具，是微生物组研究者面临的难题。中国农科院刘永鑫、南京工业大学袁军与团队在Protein & Cell 2023肠道大会微生物组专刊中发表综述文章，系统地梳理了R包在微生物组研究中的应用，为今后开发更好的微生物组工具提供了重要的理论基础和实践参考。本文干货很多，推荐专业人士学习参考。
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The best practice for microbiome analysis using R
用R语言进行微生物组分析的最佳实践
10.1093/procel/pwad024
05-02, Review
Abstract & Authors:展开
Abstract:收起
With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub.
First Authors:
Tao Wen,Guoqing Niu
Correspondence Authors:
Jun Yuan,Yong-Xin Liu
All Authors:
Tao Wen,Guoqing Niu,Tong Chen,Qirong Shen,Jun Yuan,Yong-Xin Liu
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