Exploiting Clinical Staging Data to Constrain Pseudo-Time Modelling of Disease Progression

Pseudo Time methods enable the construction of time-based models from non-temporal cross-sectional data. This means that temporal characteristics of disease can be inferred. However, the success of these approaches is dependent on appropriate distance metrics and labelling to guide the trajectory modelling. Clinical staging information, such as “early stage” and “advanced stage” of disease can be exploited to constrain the construction of pseudo time models to ensure more realistic trajectories are captured. In this paper we explore how clinical staging information can be used in this way on simulated data and on breast cancer data. Using the simulated data, we show how more precise estimates can be made of the underlying transition parameters in a model derived from constrained pseudo time methods, by preventing unrealistic transitions. The breast cancer pseudo time models are constrained based on uniformity of cell size, a proxy to disease staging, and this is shown to result in models that better represent the monotonically increasing symptoms over time.