Boshuizen Hendriek C, Te Beest Dennis E
Biometris, Wageningen University and Research, Wageningen, The Netherlands.
Mol Ecol Resour. 2023 Apr;23(3):539-548. doi: 10.1111/1755-0998.13730. Epub 2022 Nov 27.
Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA-sequencing, while others are custom-made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data.
微生物组数据具有几个特点，这些特点使得对其进行统计分析具有挑战性：它们具有组成性、高维度且零值丰富。有大量的统计方法可用于分析这些数据。有些方法是从其他领域借鉴而来的，如生态学或RNA测序，而其他方法则是专门为微生物组数据定制的。可用方法的范围很广，而且还在不断扩大，这意味着研究人员必须投入大量精力来选择应用哪种方法。在本文中，我们列出了14种我们认为通常应避免使用的统计方法或途径。在几种情况下，这是因为我们认为该方法背后的假设不太可能适用于微生物组数据。在其他情况下，我们看到一些方法被以其并非预期的方式使用。我们认为，对现有方法进行更严格的评估将有助于研究人员，因为并非所有正在使用的方法都是合适的，或者都经过了充分的审查。我们希望本文有助于就微生物组数据分析中适合使用哪些方法展开批判性讨论。
