ArticleGenome biology2025
LSGI: interpretable spatial gradient analysis for spatial transcriptomics data.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Benchmarking segmentation methods for spatial transcriptomics.Nature methods · 2026Article
- Spatial multiomics to inform immunocytokine engineering: knowledge base, gaps, and QC solutions.Frontiers in immunology · 2026Review
- Host microenvironment in potato-Phytophthora infestans interaction revealed by single-cell spatiotemporal transcriptome.Nature plants · 2026Article
- Targeting CXCL8 signaling sensitizes HNSCC to anlotinib by reducing tumor-associated macrophage-derived CLU.Journal of experimental & clinical cancer research : CR · 2025Article
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Abstract
Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
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