Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics

Single-cell sequencing technologies have enabled researchers to study dynamical biological processes, such as cell type differentiation, by constructing single-cell trajectories. Trajectories connecting the transcriptomes of groups of cells are typically inferred from cell manifolds embedded in high-dimensional space. To study the mechanisms underlying a biological process, genes whose expression patterns change smoothly along cell trajectories are extracted. However, when a mixture of biological processes coexist in the system, they often cannot be effectively de-mixed using standard cell trajectory approaches. This poses a major challenge to identifying genes that mediate each process and delineating their temporal order. Here, we present a new approach, GeneTrajectory, to unravel gene trajectories associated with distinct biological processes. To infer gene trajectories, we first compute a cell-cell graph that preserves the manifold structure of the cells. Then, we construct a gene-gene graph where the affinities between genes are based on the Wasserstein distances between their distributions on the cell graph. Notably, this method does not require any prior knowledge of the cell lineages or gene players involved. We use simulated and two real-world scRNA-seq datasets to demonstrate that GeneTrajectory reveals the gene dynamics underlying complex biological processes that cannot be resolved by standard cell trajectory inference.