Edge principal components and squash clustering: using the special structure of phylogenetic placement data for sample comparison.
Edge principal components and squash clustering: using the special structure of phylogenetic placement data for sample comparison.
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DOI：
10.1371/journal.pone.0056859
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Evans SN
中科院分区：
文献类型：
--
作者：
Matsen FA 4th;Evans SN
主成分分析（PCA）和层次聚类是两种最常用的技术，用于分析从给定环境中采集的核酸序列样本之间的差异。它们使我们对微生物群落的结构有了许多认识。我们开发了两种新的互补方法，利用这些微生物群落数据在系统发育树上的位置。边缘主成分分析能够检测包含密切相关分类群的样本之间的重要差异。每个主成分轴是系统发育树边缘上有符号权值的集合，这些权值可以通过适当的加厚和着色来很容易地可视化。壁球聚类输出一个（有根的）聚类树，其中每个内部节点对应于节点下方叶子处原始样本的适当“平均值”。此外，边缘的长度是与两个事件节点相关的平均样本之间适当定义的距离，而不是UPGMA产生的距离的不可解释的平均值，UPGMA是在这种情况下使用最广泛的分层聚类方法。我们提出这些方法，并说明他们的使用数据从人类微生物组。
Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is 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, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome.
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影响因子：
3.7
作者：
Srinivasan S;Hoffman NG;Morgan MT;Matsen FA;Fiedler TL;Hall RW;Ross FJ;McCoy CO;Bumgarner R;Marrazzo JM;Fredricks DN
通讯作者：
Fredricks DN
影响因子：
5.1
作者：
Nemergut DR;Costello EK;Hamady M;Lozupone C;Jiang L;Schmidt SK;Fierer N;Townsend AR;Cleveland CC;Stanish L;Knight R
通讯作者：
Knight R
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
14.9
作者：
Cole JR;Wang Q;Cardenas E;Fish J;Chai B;Farris RJ;Kulam-Syed-Mohideen AS;McGarrell DM;Marsh T;Garrity GM;Tiedje JM
通讯作者：
Tiedje JM
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