Department of Mathematics, Imperial College London, London, UK; EPSRC Centre for the Mathematics of Precision Healthcare, Imperial College, London, UK; NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College London, London, UK.
Department of Mathematics, Imperial College London, London, UK; EPSRC Centre for the Mathematics of Precision Healthcare, Imperial College, London, UK.
Cell Syst. 2020 Jan 22;10(1):39-51.e10. doi: 10.1016/j.cels.2019.10.009. Epub 2019 Nov 27.
The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalizable statistical platform to infer the dynamic pathways by which many, potentially interacting, traits are acquired or lost over time. We use HyperTraPS (hypercubic transition path sampling) to efficiently learn progression pathways from cross-sectional, longitudinal, or phylogenetically linked data, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. This Bayesian approach allows inclusion of prior knowledge, quantifies uncertainty in pathway structure, and allows predictions, such as which symptom a patient will acquire next. We provide visualization tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.
生物医学科学领域的数据爆炸为了解进化和疾病进展的动态提供了前所未有的机会，但利用这些大型和多样化的数据集仍然具有挑战性。在这里，我们描述了一个高度可推广的统计平台，用于推断随着时间的推移，许多潜在相互作用的特征是如何获得或丧失的动态途径。我们使用 HyperTraPS（超立方转换路径采样）从横截面、纵向或系统发育相关数据中高效地学习进展途径，轻松区分多种竞争途径，并确定给定观察结果的最简约机制。这种贝叶斯方法允许包含先验知识，量化途径结构的不确定性，并允许进行预测，例如患者将获得下一个症状。我们提供了可视化工具，用于直观评估多种变量途径。我们将该方法应用于卵巢癌进展和结核病中多药耐药性的演变，证明了它揭示以前未检测到的动态途径的强大功能。
