  In this paper, we present Super-OT, a novel approach to computational lineage
tracing that combines a supervised learning framework with optimal transport
based on Generative Adversarial Networks (GANs). Unlike previous approaches to
lineage tracing, Super-OT has the flexibility to integrate paired data. We
benchmark Super-OT based on single-cell RNA-seq data against Waddington-OT, a
popular approach for lineage tracing that also employs optimal transport. We
show that Super-OT achieves gains over Waddington-OT in predicting the class
outcome of cells during differentiation, since it allows the integration of
additional information during training.
