标题： 边缘主成分和挤压聚类：利用系统发育定位数据的特殊结构进行样本比较 显示英文标题

标题： Edge principal components and squash clustering: using the special structure of phylogenetic placement data for sample comparison

摘要： 主成分分析（PCA）和层次聚类是分析从给定环境中采样的核酸序列样本之间差异的最常用技术之一。 然而，将这些技术应用于样本之间的距离的经典方法可能缺乏透明度，因为经典PCA图中的轴没有直接的解释，而且很难为基于距离的层次聚类方法（如UPGMA）生成的树的内部节点或边长赋予明确的直观意义。 我们表明，通过利用系统发育定位数据的特殊结构，两种新方法可以产生更有意思且可解释的结果。 边主成分分析能够检测包含密切相关的分类群的样本之间的显著差异。 每个主成分轴只是系统发育树边上的有符号权重的集合，这些权重可以通过对边进行适当的加粗和着色来轻松可视化。 压平聚类输出一个（根化的）聚类树，其中每个内部节点对应于该节点下方叶样本的适当“平均”。 此外，边的长度是与两个相邻节点相关联的平均样本之间的适当定义的距离，而不是UPGMA产生的距离的不太易解释的平均值。 我们介绍了这些方法，并用人类阴道微生物组的数据说明了它们的应用。 显示英文摘要

摘要： Principal components (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples sampled from a given environment. However, a classical application of these techniques to distances computed between samples can lack transparency because there is no ready interpretation of the axes of classical PCA plots, and it is difficult to assign any clear intuitive meaning to either the internal nodes or the edge lengths of trees produced by distance-based hierarchical clustering methods such as UPGMA. We show that more interesting and interpretable results are produced by two new methods that leverage the special structure of phylogenetic placement data. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is simply a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate "average" of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA. We present these methods and illustrate their use with data from the microbiome of the human vagina.