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Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny

机译:经方差调整的加权UniFrac:一种功能强大的Beta多样性度量,用于比较基于系统发育的社区

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Background Beta diversity, which involves the assessment of differences between communities, is an important problem in ecological studies. Many statistical methods have been developed to quantify beta diversity, and among them, UniFrac and weighted-UniFrac (W-UniFrac) are widely used. The W-UniFrac is a weighted sum of branch lengths in a phylogenetic tree of the sequences from the communities. However, W-UniFrac does not consider the variation of the weights under random sampling resulting in less power detecting the differences between communities. Results We develop a new statistic termed variance adjusted weighted UniFrac (VAW-UniFrac) to compare two communities based on the phylogenetic relationships of the individuals. The VAW-UniFrac is used to test if the two communities are different. To test the power of VAW-UniFrac, we first ran a series of simulations which revealed that it always outperforms W-UniFrac, as well as UniFrac when the individuals are not uniformly distributed. Next, all three methods were applied to analyze three large 16S rRNA sequence collections, including human skin bacteria, mouse gut microbial communities, microbial communities from hypersaline soil and sediments, and a tropical forest census data. Both simulations and applications to real data show that VAW-UniFrac can satisfactorily measure differences between communities, considering not only the species composition but also abundance information. Conclusions VAW-UniFrac can recover biological insights that cannot be revealed by other beta diversity measures, and it provides a novel alternative for comparing communities.
机译:背景技术Beta多样性涉及评估社区之间的差异,是生态学研究中的一个重要问题。已经开发出许多统计方法来量化beta多样性,其中UniFrac和加权UniFrac(W-UniFrac)被广泛使用。 W-UniFrac是来自社区的序列的系统树中分支长度的加权总和。但是,W-UniFrac并未考虑随机抽样下权重的变化,从而导致检测群体之间差异的能力降低。结果我们开发了一种新的统计数据,称为方差调整加权UniFrac(VAW-UniFrac),用于根据个体的系统发育关系比较两个社区。 VAW-UniFrac用于测试两个社区是否不同。为了测试VAW-UniFrac的功能,我们首先进行了一系列模拟,结果表明,在个体分布不均匀的情况下,它始终优于W-UniFrac和UniFrac。接下来,将所有三种方法应用于分析三个大型16S rRNA序列集合,包括人类皮肤细菌,小鼠肠道微生物群落,高盐度土壤和沉积物的微生物群落以及热带森林普查数据。模拟和对实际数据的应用均表明,VAW-UniFrac不仅可以考虑物种组成,还可以考虑丰度信息,因此可以令人满意地测量群落之间的差异。结论VAW-UniFrac可以恢复其他β多样性测量方法无法揭示的生物学见解,并且为比较群落提供了一种新颖的选择。

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