An alternating least squares approach to inferring phylogenies from pairwise distances.
An alternating least squares approach to inferring phylogenies from pairwise distances.
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DOI：
10.1093/sysbio/46.1.101
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发表时间：
1997-03
影响因子：
6.5
通讯作者：
J. Felsenstein
中科院分区：
文献类型：
--
作者：
J. Felsenstein
提出了一种计算方法，用于最小化物种之间的观测和期望的成对距离之间的差异的加权平方和，其中的期望是由一个加性树模型产生的。Fitch和Margoliash（1967，Science 155：279-284）以及Cavalli-Sforza和Edwards（1967，Evolution 21：550-570）的准则都是加权最小二乘，具有不同的权重。该方法提出了迭代长度的相邻分支在树的三个时间。在迭代过程中，加权平方和不增加，迭代次数逼近平方和曲面上的一个驻点。这种迭代方法使得特别容易保持分支长度永远不会变为负的约束，尽管也可以允许负的分支长度。该方法在计算机程序FITCH中实现，FITCH自1982年以来一直作为用于推断遗传学的PHYLIP程序包的一部分分发，并且也在PAUP* 中实现。本方法比较，使用一些模拟的数据集，与De Soete（1983，Psychometrika 48：621-626）的方法的实现;它比De Soete的方法慢，但更有效地找到最小二乘树。本文还讨论了这种方法与相邻方法的关系。
A computational method is presented for minimizing the weighted sum of squares of the differences between observed and expected pairwise distances between species, where the expectations are generated by an additive tree model. The criteria of Fitch and Margoliash (1967, Science 155:279-284) and Cavalli-Sforza and Edwards (1967, Evolution 21:550-570) are both weighted least squares, with different weights. The method presented iterates lengths of adjacent branches in the tree three at a time. The weighted sum of squares never increases during the process of iteration, and the iterates approach a stationary point on the surface of the sum of squares. This iterative approach makes it particularly easy to maintain the constraint that branch lengths never become negative, although negative branch lengths can also be allowed. The method is implemented in a computer program, FITCH, which has been distributed since 1982 as part of the PHYLIP package of programs for inferring phylogenies, and is also implemented in PAUP*. The present method is compared, using some simulated data sets, with an implementation of the method of De Soete (1983, Psychometrika 48:621-626); it is slower than De Soete's method but more effective at finding the least squares tree. The relationship of this method to the neighbor-joining method is also discussed.