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arXiv 1003.5964q-bio.PEcs.DS
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基于同源数据的近缘物种系统发育重建MCMC算法的快速收敛性

Fast Convergence of MCMC Algorithms for Phylogenetic Reconstruction with Homogeneous Data on Closely Related Species

Daniel Stefankovic, Eric Vigoda

摘要:

本文研究了马尔可夫链的系统发育重建,它使用了一个流行的树拓扑结构之间的过渡称为子树修剪和重嫁接(SPR)。我们分析马尔可夫链在更简单的设置,生成树由非常短的边长,足够短,使每个样本从生成树(或字符在系统发育术语)可能只有一个突变,并有足够的样本,使数据看起来像生成分布。我们证明在这种情况下,马尔可夫链是快速混合,即,它迅速收敛到其平稳分布,该平稳分布是树拓扑上的后验分布。我们的证明使用树T的最大似然函数的前导项是最大简约得分,这是T中实现生成树的单边切割所需的最小切割的大小。我们的主要贡献是一个组合证明,在我们的简化设置,SPR移动保证快速收敛到最大简约树。我们的研究结果是在最近的作品显示的例子与异构数据(即,数据是从混合分布),许多自然马尔可夫链是指数缓慢收敛到平稳分布。

英文摘要:

This paper studies a Markov chain for phylogenetic reconstruction which uses a popular transition between tree topologies known as subtree pruning-and-regrafting (SPR). We analyze the Markov chain in the simpler setting that the generating tree consists of very short edge lengths, short enough so that each sample from the generating tree (or character in phylogenetic terminology) is likely to have only one mutation, and that there enough samples so that the data looks like the generating distribution. We prove in this setting that the Markov chain is rapidly mixing, i.e., it quickly converges to its stationary distribution, which is the posterior distribution over tree topologies. Our proofs use that the leading term of the maximum likelihood function of a tree T is the maximum parsimony score, which is the size of the minimum cut in T needed to realize single edge cuts of the generating tree. Our main contribution is a combinatorial proof that in our simplified setting, SPR moves are guaranteed to converge quickly to the maximum parsimony tree. Our results are in contrast to recent works showing examples with heterogeneous data (namely, the data is generated from a mixture distribution) where many natural Markov chains are exponentially slow to converge to the stationary distribution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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