Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell–cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

单细胞 RNA 测序已广泛用于研究细胞状态转变和生物过程的基因动态。当前推断过程中基因顺序动态的策略通常依赖于通过细胞轨迹推断构建细胞伪时间。然而，同一组细胞中同时存在的基因过程和技术噪音可能会掩盖所研究过程的真实进展。为了应对这一挑战，我们提出了 GeneTrajectory，这是一种识别基因轨迹而不是细胞轨迹的方法。具体来说，计算细胞-细胞图中基因分布之间的最佳传输距离，以提取基因程序并定义其基因伪时间顺序。在这里，我们证明 GeneTrajectory 可以准确提取骨髓谱系成熟过程中的基因动态。此外，我们表明 GeneTrajectory 可以解构小鼠皮肤毛囊真皮凝析分化的关键基因程序，而细胞轨迹方法无法解决这一问题。 GeneTrajectory 有助于发现控制生物过程变化和活动的基因程序。