A Primer for Microbiome Time-Series Analysis

Frontiers in Genetics

Elaine Luo¹  Daniel Muratore²  Joshua S. Weitz³  Ashley R. Coenen⁴  Sarah K. Hu⁵ 

[1] Daniel K. Inouye Center for Microbial Oceanography: Research and Education, University of Hawaii, Honolulu, HI, United States;Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, United States;School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States;School of Physics, Georgia Institute of Technology, Atlanta, GA, United States;Woods Hole Oceanographic Institution, Marine Chemistry and Geochemistry, Woods Hole, MA, United States;

关键词: microbial ecology;    time-series analysis;    marine microbiology;    inference;    clustering;    periodicity;   

DOI  :  10.3389/fgene.2020.00310

来源: DOAJ

【 摘 要 】

Time-series can provide critical insights into the structure and function of microbial communities. The analysis of temporal data warrants statistical considerations, distinct from comparative microbiome studies, to address ecological questions. This primer identifies unique challenges and approaches for analyzing microbiome time-series. In doing so, we focus on (1) identifying compositionally similar samples, (2) inferring putative interactions among populations, and (3) detecting periodic signals. We connect theory, code and data via a series of hands-on modules with a motivating biological question centered on marine microbial ecology. The topics of the modules include characterizing shifts in community structure and activity, identifying expression levels with a diel periodic signal, and identifying putative interactions within a complex community. Modules are presented as self-contained, open-access, interactive tutorials in R and Matlab. Throughout, we highlight statistical considerations for dealing with autocorrelated and compositional data, with an eye to improving the robustness of inferences from microbiome time-series. In doing so, we hope that this primer helps to broaden the use of time-series analytic methods within the microbial ecology research community.

【 授权许可】

Unknown