一种用于比较协变量与模型结构间推断的进化积累动态的方法
A method for comparing inferred evolutionary accumulation dynamics across covariates and model structures
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中文总结 AI 辅助
本研究提出一种聚焦排序矩阵的方法，可比较不同算法、数据集和协变量间EvAM的推断动态，应用于肿瘤染色体畸变与耐药细菌演化数据，能捕捉统计稳健性与科学意义差异。
中文摘要 AI 辅助
在本注记中，我们描述了一种用于比较进化积累模型（EvAMs）中推断动态的方法。这些模型涉及随时间推移获取多个可能相互依赖的二元特征，例如癌症发展中的突变或进化生物学中的表型。随着推断EvAM动态的方法不断增多，需要有方法来比较不同算法、数据集和协变量间的推断结果。尤其需要建立一种比较方法，该方法需支持可逆随机动态、特征集间的相互作用以及可能非独立的样本（以及更简单的情况）。由于类似“移码”的差异，具有相似相对特征顺序的EvAM可能会产生完全不同的观测状态集，因此也需要能区分状态和转移相似性的方法。在此，我们提出一种聚焦于排序矩阵的方法，该矩阵描述了在其他特征的不同条件下某一特征被获取的概率，因此通常可在不同方法和数据集间进行比较。我们展示了该方法如何同时捕捉统计稳健性（考虑EvAM不确定性）和推断动态中具有科学意义的差异。该方法被应用于合成数据和真实世界数据，这些数据涉及不同肿瘤类型的染色体畸变演化以及不同国家的耐药细菌演化。
英文摘要
In this note we describe a method for comparing inferred dynamics in evolutionary accumulation models (EvAMs). These models involve the acquisition of multiple, potentially codependent, binary features over time -- for example, mutations in cancer development, or phenotypes in evolutionary biology. As the set of methods for inferring EvAM dynamics expands, approaches for comparing inference across algorithms, datasets, and covariates are required. In particular, a comparison method supporting reversible, stochastic dynamics, interactions between feature sets, and potentially non-independent samples (as well as simpler cases) has yet to be established. It is possible for EvAM with similar relative feature orderings to produce completely different sets of observed states due to ``frameshift''-like differences; methods distinguishing state and transition similarity are therefore also desirable. Here we suggest a method focussed on ordering matrices, describing the probability that a feature is acquired under different conditions on other features, and is thus generally comparable across methods and datasets. We demonstrate how the approach captures both statistical robustness (given EvAM uncertainty) and scientifically meaningful differences in inferred dynamics. The method is applied to synthetic and real-world data on the evolution of chromosomal aberrations in different tumour types and of drug-resistant bacteria in different countries.
