Abstract
MOTIVATION: Accumulation models, where a system progressively acquires binary features over time, are common in the study of cancer progression, evolutionary biology, and other fields. Many approaches have been developed to infer the accumulation pathways by which features (e.g. mutations) are acquired over time. However, most of these approaches do not support reversibility: the loss of a feature once it has been acquired (e.g. the clearing of a mutation from a tumor or population). RESULTS: Here, we demonstrate how the well-established Mk model from evolutionary biology, embedded on a hypercubic transition graph, can be used to infer the dynamics of accumulation processes, including the possibility of reversible transitions, from data which may be uncertain and cross-sectional, longitudinal, or phylogenetically/phylogenomically embedded. Positive and negative interactions between arbitrary sets of features (not limited to pairwise interactions) are supported. We demonstrate this approach with synthetic datasets and real data on bacterial drug resistance and cancer progression. While this implementation is limited in the number of features that can be considered, we discuss how this limitation may be relaxed to deal with larger systems. AVAILABILITY AND IMPLEMENTATION: The code implementing this setup in R is freely available at https://github.com/StochasticBiology/hypermk.