Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods have been applied for this purpose, which are largely used interchangeably in the literature. Although it has been observed that these tools can produce different results, there have been very few large-scale comparisons to describe the scale and significance of these differences. In addition, it is challenging for microbiome researchers to know which differential abundance tools are appropriate for their study and how these tools compare to one another. Here, we have investigated these questions by analyzing 38 16S rRNA gene datasets with two sample groups for differential abundance testing. We tested for differences in amplicon sequence variants and operational taxonomic units (referred to as ASVs for simplicity) between these groups with 14 commonly used differential abundance tools. Our findings confirmed that these tools identified drastically different numbers and sets of significant ASVs, however, for many tools the number of features identified correlated with aspects of the tested study data, such as sample size, sequencing depth, and effect size of community differences. We also found that the ASVs identified by each method were dependent on whether the abundance tables were prevalence-filtered before testing. ALDEx2 and ANCOM produced the most consistent results across studies and agreed best with the intersect of results from different approaches. In contrast, several methods, such as LEfSe, limma voom, and edgeR, produced inconsistent results and in some cases were unable to control the false discovery rate. In addition to these observations, we were unable to find supporting evidence for a recent recommendation that limma voom, corncob, and DESeq2 are more reliable overall compared with other methods. Although ALDEx2 and ANCOM are two promising conservative methods, we argue that those researchers requiring more sensitive methods should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

中文翻译：

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微生物组差异丰度方法在38个数据集上产生令人不安的不同结果

鉴定差异丰富的微生物是微生物组研究的共同目标。为此已经应用了多种方法，这些方法在文献中被大量地互换使用。尽管已经观察到这些工具可以产生不同的结果，但是很少有大规模的比较来描述这些差异的规模和重要性。此外，微生物组研究人员要知道哪种差异丰度工具适合他们的研究以及这些工具之间的比较是具有挑战性的。在这里，我们通过分析38个16S rRNA基因数据集和两个样本组以进行差异丰度测试，对这些问题进行了调查。我们使用14种常用的差异丰度工具测试了这些组之间扩增子序列变体和操作分类单位（为简单起见，称为ASV）之间的差异。我们的发现证实，这些工具识别出的数量和数量明显不同的重要ASV，但是，对于许多工具而言，识别出的特征数量与测试研究数据的各个方面相关，例如样本量，测序深度和社区差异的影响大小。我们还发现，每种方法确定的ASV取决于测试前是否对丰度表进行了流行度过滤。ALDEx2和ANCOM在研究中产生的结果最一致，并且与不同方法的结果相吻合得最好。相反，几种方法，例如LEfSe，limma voom和edgeR，产生不一致的结果，在某些情况下无法控制错误的发现率。除了这些观察结果外，我们无法找到最新建议的支持证据，即与其他方法相比，limma voom，corncob和DESeq2总体上更可靠。尽管ALDEx2和ANCOM是两种有前途的保守方法，但我们认为那些需要更灵敏方法的研究人员应使用基于多种微分丰度方法的共识方法来帮助确保可靠的生物学解释。