SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis

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ID: 321825
2026
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Abstract
MOTIVATION: Spatial transcriptomics (ST) enables molecular profiling within native tissue architecture, yet accurate delineation of spatial domains in ST data is challenging, as it demands the coordinated integration of transcriptomic, spatial, and tissue histological information. RESULTS: We present SRLST, an unsupervised representation learning framework that holistically harmonize these three complementary data modalities to precisely uncover tissue organization. SRLST employs a dual-graph variational autoencoding strategy to jointly model spatial proximity and morphological relations, fusing these with gene-expression embeddings into a unified latent space. Across distinct experimental datasets, SRLST consistently outperforms existing methods in delineating cortical organization, identifying small discontinuous tissue compartments, and capturing complex intratumor heterogeneity. AVAILABILITY AND IMPLEMENTATION: The code implementation of the SRLST algorithm is available at https://github.com/lanbiolab/SRLST.
Reference Key
openalex_W7169843589 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wei Lan, Xiao Deng, TongSheng Ling, Guohang He, Xuhua Yan, Ruiqing Zheng, M M Li, Shirui Pan, Yi Pan
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag524
URL
https://doi.org/10.1093/bioinformatics/btag524
Keywords Keywords not found

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