STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model.

ABSTRACT: Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction. Here, we developed an STMGraph, a universal dual-view dynamic deep learning framework that combines dual-remask (MASK-REMASK) with dynamic graph attention model (DGAT) to exploit ST data outpe

SUBMITTER: Lin L 

PROVIDER: S-EPMC11704419 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature