TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
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发表时间：
2020-02
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通讯作者：
Alexander Tong;Jessie Huang;Guy Wolf;D. V. Dijk;Smita Krishnaswamy
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作者：
Alexander Tong;Jessie Huang;Guy Wolf;D. V. Dijk;Smita Krishnaswamy
随着时间的推移，通过静态横截面测量捕获的动态过程数据越来越常见，特别是在生物医学环境中。最近尝试从这些数据中建立个体轨迹模型，使用最优传输来创建时间点之间的成对匹配。然而，这些方法不能模拟实体在这些系统中可能采取的连续动力学和非线性路径。为了解决这个问题，我们在连续规范化流和动态最优传输之间建立了联系，这使我们能够对点随时间的预期路径进行建模。连续的规范化流通常受到约束，因为它们被允许从源到目标分布采取任意路径。我们提出了轨迹网，它控制分布之间的连续路径，以产生动态最优运输。我们展示了这如何特别适用于研究单细胞RNA测序（scRNA-seq）技术数据中的细胞动力学，以及轨迹网改进了最近提出的可用于插值细胞分布的基于静态最优转运的模型。
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.