AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.

Authors

Shuzhen Ding, Zhou Yu, Jingsi Ming

Published in

PLoS computational biology. Volume 22. Issue 8. Pages e1014609. Aug 05, 2026. Epub Aug 05, 2026.

Abstract

The rapid advancement of spatial transcriptomics has substantially improved our understanding of the spatial architecture and gene expression heterogeneity within tissues. However, many spatial transcriptomics techniques can not reach single-cell resolution, instead measuring gene expression profiles from mixtures of potentially heterogeneous cell types. Here we propose AddaGCN, a robust deconvolution method to infer cell type composition from spatial transcriptomic data. AddaGCN leverages graph convolutional networks to incorporate spatial information and adopts an adversarial discriminative domain adaptation approach to mitigate batch effects between spatial and single-cell reference data. Comprehensive analyses of real data generated by diverse technology platforms demonstrate AddaGCN's superior performance and robustness in cell-type deconvolution compared to other methods. These analyses further reveal AddaGCN's potential to uncover spatiotemporal changes during tissue development and to characterize the tumor microenvironment.

PMID:
42555670
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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