ArticleBioinformatics (Oxford, England)2026
st2traj: deconvolution-informed trajectory inference for multi-timepoint spatial transcriptomics.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
motivationMulti-timepoint spatial transcriptomics enables study of developmental processes in native tissue context, but cell-state mixtures within spots and lack of direct spatial correspondence across sections complicate trajectory inference and biological interpretation.
resultsst2traj is a deconvolution-informed trajectory framework using spot-state composition for multi-timepoint spatial trajectory inference. In a human heart pseudo-spot benchmark, DECODE showed competitive and balanced performance among five deconvolution methods. In multi-timepoint human heart data, unscaled DECODE-derived proportions produced smoother trajectory fields and stronger agreement with expression-derived marker programs than normalized spot-level expression. st2traj also showed greater spatial coherence than spaTrack, while exploratory comparisons with moscot and CASCAT revealed complementary method-specific strengths. Application to an independent chicken heart dataset recovered stage-associated trajectory changes across D7, D10, and D14. AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/xiaoxiaoxier/st2traj. Software v0.1.0 and processed data are archived at Zenodo: https://doi.org/10.5281/zenodo.21487030 and https://doi.org/10.5281/zenodo.21502094.
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