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Histology-informed spatial domain identification through multi-view graph convolutional networks
Journal article   Open access   Peer reviewed

Histology-informed spatial domain identification through multi-view graph convolutional networks

Huihui Zhang, Jiaxing Chang, Zirong Li, Yue Sun, Pinli Hu, Haoxiu Wang, Hang Yang, Yonglin Ren, Xingtan Zhang, Zehua Chen, …
PLoS computational biology, Vol.22(6), e1014281
2026
DOI: https://doi.org/10.1371/journal.pcbi.1014281
PMID: 42224211
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Open Access CC BY V4.0

Abstract

Algorithms Animals Cluster Analysis Clustering Algorithms Computational Biology - methods Convolutional Neural Networks Graph Neural Networks Humans Spatial Transcriptomics
Identifying spatial domains is crucial in spatial transcriptomics, yet effectively integrating gene expression, spatial location, and histology remains challenging. We present STESH, a Spatial Transcriptomics clustering method that combines Expression, Spatial information and Histology. STESH extracts histological features using a convolutional neural network and generates expression, histology, spatial, and collaborative convolution modules for a multi-view graph convolutional network with a decoder and attention mechanism. We evaluated STESH on multiple tissue types and technology platforms. STESH consistently outperformed ten state-of-the-art methods, achieving superior clustering accuracy with the highest scores in adjusted Rand index, normalized mutual information, and Fowlkes-Mallows index.

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