Maximizing Phylogenetic Signal in Cognate Data: Integrating Synonyms through Probabilistic Character Matrices

Core Concepts

Cognate data with synonyms can be effectively represented using probabilistic character matrices to maximize the phylogenetic signal during maximum likelihood tree inference.

Abstract

The authors investigate the impact of synonym selection on maximum likelihood (ML) phylogenetic tree inference using the RAxML-NG tool. They find that manually selecting synonyms can lead to substantially different tree topologies compared to using the full dataset with all synonyms. To address this issue, the authors introduce two types of probabilistic character matrices beyond the standard binary matrices: probabilistic binary and probabilistic multi-valued. The key highlights and insights are: Performing ML tree inference on the full dataset with all synonyms included is preferable to manual synonym selection, which can lead to up to 100% difference in tree topology. The authors introduce probabilistic binary and probabilistic multi-valued character matrices as alternatives to the standard binary matrices for representing cognate data with synonyms. It is dataset-dependent which character matrix type (deterministic binary, probabilistic binary, or probabilistic multi-valued) yields the ML tree closest to the gold standard reference tree. The rate heterogeneity and the difficulty of the phylogenetic inference task can indicate which character matrix type is best suited for a given dataset. The authors provide a Python interface for generating all the discussed character matrix types from cognate data in the Cross-Linguistic Data Format (CLDF). Overall, the study demonstrates that probabilistic character matrices can effectively capture the phylogenetic signal in cognate data with synonyms, outperforming the standard binary matrices in many cases.

Stats

The authors report the following key statistics: The average GQ distance between the best-scoring tree inferred on the deterministic binary character matrices and the gold standard is 0.22. The average GQ distance between the best-scoring trees inferred on the probabilistic character matrices and the gold standard is 0.23. The standard deviation of the GQ distances of the trees inferred on the sampled character matrices (with randomly selected synonyms) can exceed 0.2 for some datasets, indicating substantial variation in the inferred trees. For 5 datasets, the maximum RF distance between the tree inferred on the full dataset and the trees inferred on the sampled character matrices is 1.0, indicating completely different topologies.

Quotes

"Given these large potential discrepancies, we advise against manual selection." "We therefore strongly advise against manual synonym selection. Instead, we recommend to consider all known synonyms when inferring phylogenetic trees."

Key Insights Distilled From

Computational Approaches for Integrating out Subjectivity in Cognate Synonym Selection

by Luis... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19328.pdf

Deeper Inquiries

What are the implications of the authors' findings for other phylogenetic inference methods beyond maximum likelihood, such as Bayesian inference

The implications of the authors' findings for other phylogenetic inference methods, such as Bayesian inference, are significant. Bayesian methods rely on probabilistic models to estimate the posterior distribution of trees, parameters, and other variables. The authors' approach of using probabilistic character matrices could be directly applicable to Bayesian inference. By incorporating the probabilities of different symbols in the character matrices, Bayesian methods could better capture the uncertainty in the data and provide more accurate estimates of phylogenetic relationships. This would allow for a more nuanced analysis of the data, taking into account the variability and uncertainty in the linguistic information.

How could the authors' approach be extended to incorporate additional linguistic information beyond just cognate data, such as morphological or syntactic features, to further improve the phylogenetic signal

To extend the authors' approach to incorporate additional linguistic information beyond cognate data, such as morphological or syntactic features, several modifications and enhancements could be made. One way to integrate morphological data is to represent morphological features as additional characters in the character matrices. Each morphological feature could be assigned a specific symbol or probability, similar to how synonyms are handled in the probabilistic character matrices. By including morphological or syntactic information in the analysis, the phylogenetic signal could be enriched, leading to more robust and accurate language tree inferences.

Could the authors' probabilistic character matrix representations be adapted to handle other types of linguistic data beyond cognates, such as sound changes or grammatical features, to enable more comprehensive phylogenetic analyses of language evolution

The authors' probabilistic character matrix representations could be adapted to handle other types of linguistic data beyond cognates, such as sound changes or grammatical features, to enable more comprehensive phylogenetic analyses of language evolution. For sound changes, the probabilities in the character matrices could represent the likelihood of a particular sound change occurring between languages. Similarly, for grammatical features, the probabilities could indicate the presence or absence of specific grammatical structures. By incorporating these diverse linguistic data types into the probabilistic character matrices, researchers could conduct more holistic analyses of language evolution, capturing the complexities and nuances of linguistic change over time.