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Flexible inference of evolutionary accumulation dynamics using uncertain observational data

Understanding and predicting evolutionary accumulation pathways is a key objective in many fields of research, ranging from classical evolutionary biology to diverse applications in medicine. In this context, we are often confronted with the problem that data is sparse and uncertain. To use the available data as best as possible, inference approaches that can handle this uncertainty are required. One way that allows us to use not only cross-sectional data, but also phylogenetic related and longitudinal data, is using `hypercubic inference' models. In this article we introduce HyperLAU, a new algorithm for hypercubic inference that makes it possible to use datasets including uncertainties for learning evolutionary pathways. Expanding the flexibility of accumulation modelling, HyperLAU allows us to infer dynamic pathways and interactions between features, even when large sets of particular features are unobserved across the source dataset. We show that HyperLAU is able to highlight the main pathways found by other tools, even when up to 50% of the features in the input data are uncertain. Additionally, we demonstrate how it can help to overcome possible biases that can occur then reducing the used data by excluding uncertain parts. We illustrate the approach with a case study on multidrug resistance in tuberculosis, showing that HyperLAU allows more flexible data and provides new information about evolutionary pathways compared to existing approaches.

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