论文标题
最佳运输使用gans进行谱系跟踪
Optimal Transport using GANs for Lineage Tracing
论文作者
论文摘要
在本文中，我们提出了Super-Ot，这是一种用于计算谱系跟踪的新型方法，将监督学习框架与基于生成对抗网络（GAN）的最佳传输结合在一起。与以前的谱系跟踪方法不同，Super-OT具有集成配对数据的灵活性。我们基于对Waddington-OT的单细胞RNA-seq数据进行基准测试，这是一种流行的谱系跟踪方法，它也采用了最佳运输。我们表明，Super-OT在预测分化过程中细胞的类别的阶级结果方面取得了进步，因为它允许在训练过程中整合其他信息。
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.
