Repeated Decision Stumping Distils Simple Rules from Single Cell Data

Here we introduce repeated decision stumping, to distill simple models from single cell data. We develop decision trees of depth one - hence stumps - to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key-players involved in these processes in an unbiased manner without prior knowledge. The approach is computationally efficient, has remarkable predictive power, and yields robust and statistically stable predictors: the same set of candidates is generated by applying the algorithm to different subsamples of the data.