Stability of the inferred trajectory
It is not only important that a method is able to infer an accurate model in a reasonable time frame, but also that it produces a similar model when given very similar input data. In order to test the stability of each method, we executed each method on 10 different subsamples of the datasets (95% of the cells, 95% of the features), and calculated the average similarity between each pair of models using the same scores used to assess the accuracy of a trajectory (Figure 1d).
Given that the trajectories of methods which fix the topology either algorithmically or through a parameter is already very constrained, it is to be expected that such methods tend to generate very stable results. Nonetheless, some fixed topology methods still produced slightly more stable results, such as SCORPIUS and MATCHER for linear methods, and MFA for multifurcating methods. Stability was much more diverse among methods with a free topology. Slingshot produced more stable models than PAGA (Tree), which in turn produced more stable results than pCreode and Monocle DDRTree.
Figure 1: Detailed results of the four main evaluation criteria: accuracy, scalability, stability and usability. (a) The names of the methods. (b) Accuracy of trajectory inference methods across metrics, dataset sources and dataset trajectory types. The performance of a method is generally more stable across dataset sources, but very variable depending on the metric and trajectory type. (c) Predicted execution times for varying numbers of cells and features (# cells × # features). Predictions were made by training a regression model after running each method on bootstrapped datasets with varying numbers of cells and features. (d) Stability results by calculating the average pairwise similarity between models inferred across multiple runs of the same method. (e) Usability scores of the tool and corresponding manuscript, grouped per category. Off-the-shelf methods were directly implemented in R and thus do not have a usability score.