biostudies-literatureQu RU.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)U.S. Department of Health & Human Services | NIH | National Institute of General Medical SciencesNIDA NIH HHSNIA NIH HHSU.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)U.S. Department of Health & Human Services | NIH | National Institute on AgingNIAID NIH HHSU.S. Department of Health & Human Services | NIH | National Institute on Drug AbuseNSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical SciencesU.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA)U.S. Department of Health & Human Services | NIH | National Cancer InstituteNSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS)U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin DiseasesNCI NIH HHSU.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)NIAMS NIH HHSNIGMS NIH HHS258-268https://www.ebi.ac.uk/biostudies/studies/S-EPMC11452571biostudies-literatureUnknown43(2)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 GeneTrajectoryNature biotechnologyGene trajectory inference for single-cell data by optimal transport metrics.PMC11452571U54AG076043R01 GM131642U54 AG079759R01GM131642R01 AI141009U54 AG076043P50 CA121974UM1DA051410R01AR076420P50CA121974U54AG079759R01 AR076420UM1 DA051410U01 DA053628U01DA053628DMS-223784Kluger YOdell IDLanda BStrino FFlavell RAGarritano JMyung PCheng XQu RPlatt SSefik ECoifman RStanley Iii JSfalseGene trajectory inference for single-cell data by optimal transport metrics.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 GeneTrajectory2025-01-01T00:00:00Z2025 Feb2025-04-03T23:36:35.264Z2025-04-03T23:36:35.264ZS-EPMC114525713858086110.1038/s41587-024-02186-3