Path-MGCN: a pathway activity based multi-view graph convolutional network for determining spatial domains with attention mechanism

Abstract Gene functional relationships are always ignored in spatial-domain recognition based on spatial transcriptomics (ST). We develop Path-MGCN, a multi-view graph convolutional network (MGCN) with attention mechanism that embeds pathway information. We generate a pathway activity profile with spot-specific pathway enrichment. Unique and shared embeddings from pathway and spatial graphs are extracted by a MGCN encoder, dynamically optimized by attention mechanism, followed by a decoder to retain the original pathway information. Path-MGCN outperforms state-of-the-art spatial clustering methods. Moreover, Path-MGCN could identify spatial domain-specific pathways for further mechanism study in the context of microenvironment, enabling the precision medicine of complex diseases.