Optimal Transport using GANs for Lineage Tracing
Abstract
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.
Keywords
Cite
@article{arxiv.2007.12098,
title = {Optimal Transport using GANs for Lineage Tracing},
author = {Neha Prasad and Karren Yang and Caroline Uhler},
journal= {arXiv preprint arXiv:2007.12098},
year = {2022}
}
Comments
4 pages excluding references, 2 figures, 3 tables. Accepted at ICML 2020 Workshop on Computational Biology for Spotlight Presentation. Code can be found here: https://github.com/uhlerlab/superot