Repeated Decision Stumping Distils Simple Rules from Single Cell Data
Ivan Croydon Veleslavov, Michael PH Stumpf
Cold Spring Harbor Laboratory | Published : 2020
Abstract 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.