Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots
Abstract
MOTIVATION: Trajectory inference (TI) methods for single-cell snapshots of developmental systems have yielded numerous insights into the gene regulatory networks (GRNs) that control cell differentiation. Many TI algorithms have been proposed for recovering cell trajectories from single samples containing cells spanning a spectrum of differentiation states; however, these methods cannot leverage temporal information when a time course of such diverse samples is available. As interest grows in understanding how the regulation of GRNs changes as an organism ages, current TI theory and methods must be adapted to take advantage of all information in aging time courses of single-cell data. RESULTS: In this paper, we present our novel age-conscious method, global StationaryOT, which exploits the temporal information in aging time courses to simultaneously reconstruct debiased cell trajectories at all ages. We demonstrate that this first-of-its-kind method achieves more accurate, biologically consistent trajectories in synthetic and real biological contexts where data sparsity produces significant noise in the outputs of current TI methods when they are applied to time course samples independently. AVAILABILITY: An open-source Python implementation of global StationaryOT, including documentation and examples, is available at https://github.com/ColeBoyle/global-stationaryOT. The source code, data processing scripts, and processed data for reproducing the results in this paper are archived at https://doi.org/10.5281/zenodo.20723235. The raw hematopoiesis data from Li et al. (The dynamics of hematopoiesis over the human lifespan. Nat Methods 2025;22 422-34.) can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189161.
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BibTeX
@article{boyle2026global,
title = {{Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots}},
author = {Cole Boyle and Elias Ventre and Geoffrey Schiebinger},
journal = {Bioinformatics},
year = {2026},
month = aug,
doi = {10.1093/bioinformatics/btag532},
url = {https://hal.science/hal-05762618v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots
AU - Cole Boyle
AU - Elias Ventre
AU - Geoffrey Schiebinger
PY - 2026
DA - 2026/08/03
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag532
UR - https://hal.science/hal-05762618v1
AB - MOTIVATION: Trajectory inference (TI) methods for single-cell snapshots of developmental systems have yielded numerous insights into the gene regulatory networks (GRNs) that control cell differentiation. Many TI algorithms have been proposed for recovering cell trajectories from single samples containing cells spanning a spectrum of differentiation states; however, these methods cannot leverage temporal information when a time course of such diverse samples is available. As interest grows in understanding how the regulation of GRNs changes as an organism ages, current TI theory and methods must be adapted to take advantage of all information in aging time courses of single-cell data. RESULTS: In this paper, we present our novel age-conscious method, global StationaryOT, which exploits the temporal information in aging time courses to simultaneously reconstruct debiased cell trajectories at all ages. We demonstrate that this first-of-its-kind method achieves more accurate, biologically consistent trajectories in synthetic and real biological contexts where data sparsity produces significant noise in the outputs of current TI methods when they are applied to time course samples independently. AVAILABILITY: An open-source Python implementation of global StationaryOT, including documentation and examples, is available at https://github.com/ColeBoyle/global-stationaryOT. The source code, data processing scripts, and processed data for reproducing the results in this paper are archived at https://doi.org/10.5281/zenodo.20723235. The raw hematopoiesis data from Li et al. (The dynamics of hematopoiesis over the human lifespan. Nat Methods 2025;22 422-34.) can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189161.
SN - 1367-4803
SN - 1367-4811
ER -