University of Technology Sydney, Australian Institute for Microbiology & Infection, Sydney, Australia.
Illumina Australia Pty Ltd, Sydney, Australia.
PLoS Comput Biol. 2023 Apr 26;19(4):e1011084. doi: 10.1371/journal.pcbi.1011084. eCollection 2023 Apr.
Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. However, Bayesian phylogenetics faces the challenging computational problem of moving throughout the high-dimensional space of trees. Fortunately, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences as points in hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference in this space. The posterior probability of an embedding is computed by decoding a neighbour-joining tree from the embedding locations of the sequences. We empirically demonstrate the fidelity of this method on eight data sets. We systematically investigated the effect of embedding dimension and hyperbolic curvature on the performance in these data sets. The sampled posterior distribution recovers the splits and branch lengths to a high degree over a range of curvatures and dimensions. We systematically investigated the effects of the embedding space's curvature and dimension on the Markov Chain's performance, demonstrating the suitability of hyperbolic space for phylogenetic inference.
贝叶斯系统发育推断是计算系统发育分布的黄金标准。然而，贝叶斯系统发育学面临着在树的高维空间中移动的具有挑战性的计算问题。幸运的是，双曲空间为树状数据提供了低维表示。在本文中，我们将基因组序列嵌入双曲空间作为点，并在该空间中执行双曲马尔可夫链蒙特卡罗贝叶斯推断。通过从序列的嵌入位置解码邻接法树，计算出嵌入的后验概率。我们在八个数据集上对该方法的准确性进行了实证验证。我们系统地研究了嵌入维度和双曲曲率对这些数据集上性能的影响。在一系列曲率和维度下，采样的后验分布能够高度恢复分裂和分支长度。我们系统地研究了嵌入空间曲率和维度对马尔可夫链性能的影响，证明了双曲空间在系统发育推断中的适用性。
