Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.288662v1?rss=1

Authors: Croydon Veleslavov, I. A., Stumpf, M. P. H.

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.

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