Washburne Alex D, Silverman Justin D, Leff Jonathan W, Bennett Dominic J, Darcy John L, Mukherjee Sayan, Fierer Noah, David Lawrence A
Nicholas School of the Environment, Duke University , Durham , NC , United States.
Program for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Medical Scientist Training Program, Duke University, Durham, NC, United States; Center for Genomic and Computational Biology, Duke University, Durham, NC, United States; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States.
PeerJ. 2017 Feb 9;5:e2969. doi: 10.7717/peerj.2969. eCollection 2017.
Marker gene sequencing of microbial communities has generated big datasets of microbial relative abundances varying across environmental conditions, sample sites and treatments. These data often come with putative phylogenies, providing unique opportunities to investigate how shared evolutionary history affects microbial abundance patterns. Here, we present a method to identify the phylogenetic factors driving patterns in microbial community composition. We use the method, "phylofactorization," to re-analyze datasets from the human body and soil microbial communities, demonstrating how phylofactorization is a dimensionality-reducing tool, an ordination-visualization tool, and an inferential tool for identifying edges in the phylogeny along which putative functional ecological traits may have arisen.
微生物群落的标记基因测序产生了大量微生物相对丰度数据集，这些数据集随环境条件、样本位点和处理方式的不同而变化。这些数据通常附带推测的系统发育关系，为研究共享的进化历史如何影响微生物丰度模式提供了独特的机会。在这里，我们提出了一种方法来识别驱动微生物群落组成模式的系统发育因素。我们使用“系统发育因子分解”方法重新分析来自人体和土壤微生物群落的数据集，证明了系统发育因子分解是一种降维工具、一种排序可视化工具，以及一种用于识别系统发育中假定的功能生态特征可能出现的边缘的推理工具。
