Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

被引:47
作者
Cao, Hong-Tai [1 ,2 ,3 ]
Gibson, Travis E. [1 ]
Bashan, Amir [1 ,4 ]
Liu, Yang-Yu [1 ,5 ]
机构
[1] Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA 02115 USA
[2] Univ Southern Calif, Dept Elect Engn, Los Angeles, CA 90089 USA
[3] Zhejiang Univ, Chu Kochen Honors Coll, Coll Elect Engn, Hangzhou, Zhejiang, Peoples R China
[4] Bar Ilan Univ, Dept Phys, Ramat Gan, Israel
[5] Dana Farber Canc Inst, Ctr Canc Syst Biol, Boston, MA 02115 USA
关键词
dynamics inference; ecological modeling; human microbiome; temporal metagenomics; LOTKA-VOLTERRA EQUATION; REPLICATOR DYNAMICS; VARIABLE SELECTION; REGULARIZATION; REGRESSION; MATLAB;
D O I
10.1002/bies.201600188
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
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页数:12
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