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 github.com/taowenmicro/EasyMicrobiomeR.

中文翻译：

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使用 R 进行微生物组分析的最佳实践。

随着测序技术的逐渐成熟，许多微生物组研究的发表，推动了相关分析工具的出现和进步。R语言是广泛使用的微生物组数据分析平台，具有强大的功能。然而，数以万计的R包和众多类似的分析工具给许多研究人员探索微生物组数据带来了重大挑战。如何从众多的R包中选择合适、高效、方便、易学的工具成为了许多微生物组研究人员面临的难题。我们整理了324个用于微生物组分析的常用R包，并根据应用类别（多样性、差异性、生物标志物、相关性和网络、功能预测等）进行分类，可以帮助研究人员快速找到相关的用于微生物组分析的R包。此外，我们系统地整理了用于微生物组分析的集成R包（phyloseq、microbiome、MicrobiomeAnalystR、Animalcules、microeco和amplicon），并总结了其优点和局限性，这将有助于研究人员选择合适的工具。最后，我们对微生物组分析的R包进行了全面的回顾，总结了微生物组中大部分常见的分析内容，形成了最适合微生物组分析的流程。本文在GitHub上附有数百个示例、10000行代码，可以帮助初学者学习，也可以帮助分析师比较和测试不同的工具。本文系统梳理了R在微生物组中的应用，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。所有代码均可在 GitHub github.com/taowenmicro/EasyMicrobiomeR 上获取。