The phylogenetic Kantorovich-Rubinstein metric for environmental sequence samples.
The phylogenetic Kantorovich-Rubinstein metric for environmental sequence samples.
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DOI：
10.1111/j.1467-9868.2011.01018.x
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发表时间：
2012-06-01
期刊：
影响因子：
--
通讯作者：
Matsen FA
中科院分区：
文献类型：
--
作者：
Evans SN;Matsen FA
现在，通过对从特定环境中大量提取的核酸物质进行测序来调查微生物群落是很常见的。在给定这类数据集的情况下，需要使用比较方法来显示两个社区的不同程度。UniFrac是这些分析最常用的工具之一，它在某种程度上给出了两个群落之间的特殊系统发育距离。我们通过建立这样的基础来为这种方法提供基础：如果我们将一个元基因组样本等同于它在参考系统发生树上的经验分布，那么两个样本之间的加权UniFrac距离就是对应的经验分布之间的经典Kantorovich-Rubinstein距离，或地球推动力距离。我们证明了这种Kantorovich-Rubinstein距离和包含样本位置不确定性的扩展可以写成树上易于计算的积分，我们发展了Lp Zolotarev型度量的推广，并且我们展示了如何通过使用高斯过程泛函来逼近零假设的自然置换检验的p值。我们将L2情形与方差分析类型的分解联系起来，发现其关联的高斯泛函的分布是独立随机变量的可计算线性组合的分布。
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 random variables.
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DOI：
10.1073/pnas.0807920105
发表时间：
2008-11-18
影响因子：
11.1
作者：
Fierer, Noah;Hamady, Micah;Knight, Rob
通讯作者：
Knight, Rob
影响因子：
56.9
作者：
Gill, Steven R.;Pop, Mihai;Nelson, Karen E.
通讯作者：
Nelson, Karen E.
影响因子：
48
作者：
Kuczynski, Justin;Liu, Zongzhi;Lozupone, Catherine;McDonald, Daniel;Fierer, Noah;Knight, Rob
通讯作者：
Knight, Rob
影响因子：
1.1
作者：
Billera, LJ;Holmes, SP;Vogtmann, K
通讯作者：
Vogtmann, K
影响因子：
--
作者：
KOTZ, S;JOHNSON, NL;BOYD, DW
通讯作者：
BOYD, DW