MultiSpaceNet: Graph-based Joint Representation Learning for Paired Spatial Transcriptome–Proteome Data

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ID: 329711
2026
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Abstract
MOTIVATION: Paired spatial assays now measure the transcriptome and the proteome on the same tissue section. Existing integration methods commit to a structural choice at the outset: a separate graph per modality, early fusion that folds the measured protein into the embedding so it can no longer be predicted, or intersection rules that discard most similarity edges. Each choice limits what one trained model can do afterwards. RESULTS: We present MultiSpaceNet, a graph-based framework for joint representation learning from paired spatial transcriptomic and proteomic data. A section is represented as one cell graph over a shared node set, with spatial, transcriptomic and proteomic relations carried as typed edges and the two modality branches kept separate until a per-cell attention fusion. On five benchmark datasets, MultiSpaceNet outperforms nine published state-of-the-art methods in spatial domain identification (mean adjusted Rand index 0.472). Its leakage-free RNA-to-protein imputation matches or exceeds established methods for signal-bearing proteins, and it preserves biological structure across replicate sections better than all compared alternatives. A single trained model thus provides spatial domains, protein imputation, cross-section joint embedding and descriptive per-cell modality-dominance maps. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/yongzhuangliulab/MultiSpaceNet and archived at Zenodo (DOI 10.5281/zenodo.22667390). The scripts that regenerate the reported tables and figures, together with their machine-readable result summaries, are included in the repository (directory resubmission/) and its Zenodo archive. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7170074634 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhengqian Zhang, Binghong Chen, Jingyi Bai, Jialiang Wang, Junjun Ren, Chongyao Liu, Ziqi Liu, Yikun Cao, Yongzhuang Liu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag676
URL
https://doi.org/10.1093/bioinformatics/btag676
Keywords Keywords not found

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