Cao Hong-Tai, Gibson Travis E, Bashan Amir, Liu Yang-Yu
Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA.
Bioessays. 2017 Feb;39(2). doi: 10.1002/bies.201600188. Epub 2016 Dec 21.
The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.
人类肠道微生物群是一个非常复杂且动态的生态系统，对健康和福祉起着至关重要的作用。直接从时间分辨宏基因组学数据推断微生物群落结构和动态是理解群落生态学和预测其时间行为的关键。已经提出了许多方法来进行这种推断。然而，正如我们在本综述中所指出的，在这个过程中存在几个陷阱。确实，无信息的时间测量以及相对丰度数据的组成性质在推断中提出了严峻挑战。此外，当仅通过忽略或对低丰度物种进行分组而只关注高丰度物种时，推断结果可能会被极大地扭曲。最后，各种正则化方法中的隐含假设可能并不反映实际情况。在人类肠道微生物群的生态建模中必须认真考虑这些问题。
