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分类依据与纠错

AI 根据所提供的论文材料归类:摘要讨论系统发育树上的群落距离与统计检验,未提供所列计量细分方向的直接依据。

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关注这篇论文的作者
Steven N. Evans

环境序列样本的系统发育 Kantorovich–Rubinstein 距离

研究问题
如何依据环境测序样本比较两个微生物群落,并为常用的系统发育距离及群落差异检验提供数学基础?
方法与思路
作者将样本视为参考系统发育树上的经验分布,把距离写成树上积分,并用高斯过程泛函近似置换检验的显著性概率。
材料陈述的贡献
摘要称加权 UniFrac 等于相应经验分布间的经典运输距离,并提出纳入样本位置不确定性的扩展及 Lp 型推广。
阅读关注点
应核对树上积分表示的适用条件、位置不确定性的处理方式,以及置换检验高斯近似和 L2 方差分析式分解的依据。
阅读边界
仅据 Crossref 摘要,无法判断近似精度与检验适用范围;末尾“X12”随机变量符号不清,需查阅原PDF,不能据此重构分布公式。

原文摘要

Summary It is now common to survey microbial communities by sequencing nucleic acid material extracted in bulk from a given environment. Comparative methods are needed that indicate the extent to which two communities differ given data sets of this type. UniFrac, which gives a somewhat ad hoc phylogenetics-based distance between two communities, is one of the most commonly used tools for these analyses. We provide a foundation for such methods by establishing that, if we equate a metagenomic sample with its empirical distribution on a reference phylogenetic tree, then the weighted UniFrac distance between two samples is just the classical Kantorovich–Rubinstein, or earth mover’s, distance between the corresponding empirical distributions. We demonstrate that this Kantorovich–Rubinstein distance and extensions incorporating uncertainty in the sample locations can be written as a readily computable integral over the tree, we develop Lp Zolotarev-type generalizations of the metric, and we show how the p-value of the resulting natural permutation test of the null hypothesis ‘no difference between two communities’ can be approximated by using a Gaussian process functional. We relate the L2-case to an analysis-of-variance type of decomposition, finding that the distribution of its associated Gaussian functional is that of a computable linear combination of independent X12 random variables.

笔记与分组