Microbiome differential abundance methods produce different results across 38 datasets.
Microbiome differential abundance methods produce different results across 38 datasets.
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DOI：
10.1038/s41467-022-28034-z
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发表时间：
2022-01-17
影响因子：
16.6
通讯作者：
Langille MGI
中科院分区：
文献类型：
--
作者：
Nearing JT;Douglas GM;Hayes MG;MacDonald J;Desai DK;Allward N;Jones CMA;Wright RJ;Dhanani AS;Comeau AM;Langille MGI
鉴定差异丰富的微生物是微生物组研究的共同目标。在文献中，多种方法可互换地用于此目的。然而，很少有大规模的研究系统地探讨交替使用这些工具的适当性，以及它们之间的差异的规模和意义。在这里，我们比较了14种差异丰度测试方法在38个16 S rRNA基因数据集上的性能。我们测试这些组之间的扩增子序列变体和操作分类单位（ASV）的差异。我们的研究结果证实，这些工具确定了显著不同的ASV数量和集合，并且结果取决于数据预处理。对于许多工具，识别的特征的数量与数据的各个方面相关，例如样本大小，测序深度和社区差异的效应大小。ALDEx 2和ANCOM-II在不同研究中产生最一致的结果，并且与不同方法的结果交叉最一致。尽管如此，我们建议研究人员应该使用基于多种差异丰度方法的共识方法，以帮助确保可靠的生物学解释。微生物组丰度差异分析方法很多，但缺乏系统的比较。在这里，作者比较了14种差异丰度测试方法在38个16 S rRNA基因数据集上的性能，并显示ALDEx 2和ANCOM-II产生了最一致的结果。
Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations. Many microbiome differential abundance methods are available, but it lacks systematic comparison among them. Here, the authors compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups, and show ALDEx2 and ANCOM-II produce the most consistent results.
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DOI：
10.1016/j.cgh.2018.09.017
发表时间：
2019-01
期刊：
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影响因子：
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DOI：
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发表时间：
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期刊：
Bioinformatics (Oxford, England)
影响因子：
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通讯作者：
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影响因子：
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