Optimal Transport using GANs for Lineage Tracing
This is an incremental improvement for researchers in computational biology, specifically for lineage tracing using single-cell RNA-seq data.
The paper tackles computational lineage tracing by proposing Super-OT, a method combining supervised learning with optimal transport using GANs, which achieves gains over Waddington-OT in predicting cell class outcomes during differentiation.
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.