Global StationaryOT: Trajectory inference for aging time courses of single-cell snapshots

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ID: 322592
2026
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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. (2025) can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc=GSE189161. SUPPLEMENTARY INFORMATION: Supplementary information is available as a PDF at Bioinformatics online.
Reference Key
openalex_W7170144532 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cole Boyle, Elias Ventre, Geoffrey Schiebinger
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag532
URL
https://doi.org/10.1093/bioinformatics/btag532
Keywords Keywords not found

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