Department of Biostatistics, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA, 15261, USA.
Biostatistics and Bioinformatics Branch, NICHD, NIH, Bethesda, MD, USA.
NPJ Biofilms Microbiomes. 2020 Dec 2;6(1):60. doi: 10.1038/s41522-020-00160-w.
Increasingly, researchers are discovering associations between microbiome and a wide range of human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. The first step towards microbiome wide association studies is the characterization of the composition of human microbiome under different conditions. Determination of differentially abundant microbes between two or more environments, known as differential abundance (DA) analysis, is a challenging and an important problem that has received considerable interest during the past decade. It is well documented in the literature that the observed microbiome data (OTU/SV table) are relative abundances with an excess of zeros. Since relative abundances sum to a constant, these data are necessarily compositional. In this article we review some recent methods for DA analysis and describe their strengths and weaknesses.
越来越多的研究人员发现，微生物组与人类的各种疾病之间存在关联，如肥胖症、炎症性肠病、艾滋病等。进行微生物组广泛关联研究的第一步是在不同条件下对人类微生物组的组成进行特征描述。在两个或多个环境之间确定丰度不同的微生物，即差异丰度（DA）分析，是一个具有挑战性的重要问题，在过去十年中受到了相当大的关注。文献中已有充分的记载，所观察到的微生物组数据（OTU/SV 表）是具有过多零值的相对丰度。由于相对丰度总和为常数，因此这些数据必然是组成性的。本文综述了一些最近用于 DA 分析的方法，并描述了它们的优缺点。
