Evidence map›Paper›PMID 42478747›Full record

ArticleBioinformatics (Oxford, England)2026

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

Wei Lan, Xiao Deng, Tongsheng Ling, Guohang He, Xuhua Yan, Ruiqing Zheng, Min Li, Shirui Pan, Yi Pan

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In one paragraph

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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wei LanGuangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, Nanning, Guangxi, China.ORCID 0000-0001-5839-7504
Xiao DengGuangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, Nanning, Guangxi, China.
Tongsheng LingGuangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, Nanning, Guangxi, China.
Guohang HeGuangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, Nanning, Guangxi, China.
Xuhua YanGuangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, Nanning, Guangxi, China.
Ruiqing ZhengSchool of Computer and Engineering, Central South University, Changsha, Hunan, China.
Min LiSchool of Computer and Engineering, Central South University, Changsha, Hunan, China.ORCID 0000-0002-0188-1394
Shirui PanSchool of Information and Communication Technology, Griffith University, Southport, Queensland, Australia.
Yi PanSchool of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China.ORCID 0000-0002-2766-3096

Funding

Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Centre 2025AQ104the Guangxi Bagui Youth Talent Programthe Key Laboratory of Intelligent Analysis of Biomedical Big Data of the Department of Education of Guangdong Province 2024KSYS010the National Natural Science Foundation of China U24A20256the Natural Science Foundation of Guangxi 2024GXNSFFA010006the Science and Technology Project for Disease Prevention and Control of Guangxi GXJKKJ2025YB019
6 · The paper itself

Abstract

motivationSpatial 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.

resultsWe 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.

Indexed as

Gene Expression ProfilingSoftwareSpatial TranscriptomicsTranscriptomeAlgorithmsAnimalsHumansRepresentation Machine Learning

Identifiers

PMID42478747
PMCPMC13452167

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.