SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics

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ID: 317346
2026
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Abstract
Abstract Motivation Spatial transcriptomics (ST) profiles gene expression in tissue context, enabling spatial domain detection. However, relatively few methods jointly recover discrete spatial domains and continuous within-section pseudotemporal trends in a single framework. Current spatiotemporal approaches often emphasise trajectory continuity to recover smooth progression-associated gradients, but this may blur neighbouring domain boundaries and reduce clustering accuracy. Conversely, specialised spatial clustering algorithms typically rely on external single-cell trajectory tools rather than providing an integrated, spatially aware pseudotime model. Results We introduce SECTOR (Structural Entropy-based Clustering and pseudoTime ORdering), a lightweight deep graph learning framework that unifies spatial domain detection and pseudotime inference. SECTOR optimises a differentiable structural entropy (SE) objective on a fused spatial–expression graph, with spatial total variation regularisation to promote tissue continuity. Across seven benchmark datasets spanning standard and modern high-resolution ST platforms, SECTOR consistently outperformed existing spatiotemporal methods in clustering accuracy and matched or exceeded leading spatial clustering algorithms, while maintaining modest computational demands. In human breast cancer and mouse olfactory bulb case studies, SECTOR recovered spatially organised pseudotime patterns supported by semivariance, transition-gene, enrichment and marker-gene analyses. Together, these results show that SE-based learning provides an effective and scalable strategy for modelling within-section spatiotemporal organisation in ST. Availability SECTOR is available on GitHub at https://github.com/lhbcb/SECTOR and archived on Figshare at https://doi.org/10.6084/m9.figshare.32029830. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164825375 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li Huang, Jingyun Zhang, Weikang Gong, Guangjie Zeng, Hao Peng, Dongsheng Chen
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag367
URL
https://doi.org/10.1093/bioinformatics/btag367
Keywords Keywords not found

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