Dataset Information

Spectral neighbor joining for reconstruction of latent tree Models.

ABSTRACT: A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges.

SUBMITTER: Jaffe A

PROVIDER: S-EPMC8194222 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Spectral neighbor joining for reconstruction of latent tree Models.

Jaffe Ariel A Amsel Noah N Aizenbud Yariv Y Nadler Boaz B Chang Joseph T JT Kluger Yuval Y

SIAM journal on mathematics of data science 20210201 1

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover th ...[more]

PMID: 34124606

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Project description:The neighbor-joining algorithm for phylogenetic inference (NJ) has been seen to have three specific properties when applied to distance matrices that contain an admixed taxon: (1) antecedence of clustering, in which the admixed taxon agglomerates with one of its source taxa before the two source taxa agglomerate with each other; (2) intermediacy of distances, in which the distance on an inferred NJ tree between an admixed taxon and either of its source taxa is smaller than the distance between the two source taxa; and (3) intermediacy of path lengths, in which the number of edges separating the admixed taxon and either of its source taxa is less than or equal to the number of edges between the source taxa. We examine the behavior of neighbor-joining on distance matrices containing an admixed group, investigating the occurrence of antecedence of clustering, intermediacy of distances, and intermediacy of path lengths. We first mathematically predict the frequency with which the properties are satisfied for a labeled unrooted binary tree selected uniformly at random in the absence of admixture. We then introduce a taxon constructed by a linear admixture of distances from two source taxa, examining three admixture scenarios by simulation: a model in which distance matrices are chosen at random, a model in which an admixed taxon is added to a set of taxa that reflect treelike evolution, and a model that introduces a perturbation of the treelike scenario. In contrast to previous conjectures, we observe that the three properties are sometimes violated by distance matrices that include an admixed taxon. However, we also find that they are satisfied more often than is expected by chance when the distance matrix contains an admixed taxon, especially when evolution among the non-admixed taxa is treelike. The results contribute to a deeper understanding of the nature of evolutionary trees constructed from data that do not necessarily reflect a treelike evolutionary process.

Dataset Information

Spectral neighbor joining for reconstruction of latent tree Models.

Publications

Spectral neighbor joining for reconstruction of latent tree Models.

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