ArticleBriefings in bioinformatics2025
spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- RelAgent: a multi-agent solution for molecular relationship grounding.Bioinformatics (Oxford, England) · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Ambiguity-Aware Multi-Stage Cell-Type Annotation for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Exosome-enabled bone defect repair: mechanistic foundations, bioengineered delivery, and artificial intelligence-driven translation.Journal of nanobiotechnology · 2026Review
- GeDi: simplifying gene set distances for enhanced omics interpretation in R/Bioconductor.BMC bioinformatics · 2025Article
- Spatial omics: applications and utility in profiling the tumor microenvironment.Cancer metastasis reviews · 2025Review
- Spatial transcriptomics: a bibliometric analysis with large language model on English literatures.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Spatial multi-omics technologies provide valuable data on gene expression from various omics in the same tissue section while preserving spatial information. However, deciphering spatial domains within spatial omics data remains challenging due to the sparse gene expression. We propose spaLLM, the first multi-omics spatial domain analysis method that integrates large language models to enhance data representation. Our method combines a pre-trained single-cell language model (scGPT) with graph neural networks and multi-view attention mechanisms to compensate for limited gene expression information in spatial omics while improving sensitivity and resolution within modalities. SpaLLM processes multiple spatial modalities, including RNA, chromatin, and protein data, potentially adapting to emerging technologies and accommodating additional modalities. Benchmarking against eight state-of-the-art methods across four different datasets and platforms demonstrates that our model consistently outperforms other advanced methods across multiple supervised evaluation metrics. The source code for spaLLM is freely available at https://github.com/liiilongyi/spaLLM.
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Registered trials
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.