DSSMST is introduced, a Deterministic State Space Model for Spatial Transcriptomics, as a self-supervised learning (SSL) framework that integrates a Deterministic State Space Model (DSSM), graph neural networks (GNNs), and contrastive learning.
Abstract Spatial transcriptomics enables high-resolution profiling of gene expression within intact tissue architecture, providing new opportunities to study the spatial organization of tissue structures and cell types. However, identifying spatial domains that are reproducible across samples and individuals remains ch...
Shi-Wei Fu, Han Li, Wei Vivian Li· Briefings in Bioinformatics· 0 citations
Accurately identifying spatial domains based on spatial transcriptomics (ST) data can greatly promote our understanding of cellular composition and tissue organization. While graph neural networks (GNNs) have shown significant advancements in spatial clustering, they tend to be insensitive to noisy edges, leading to in...
Li-Hong Peng, Long Yang, Min Chen et al.· Genomics, Proteomics & Bioin...· 0 citations
StKAN is introduced, a novel framework integrating Kolmogorov-Arnold Network with variational autoencoder to effectively model spatially resolved gene expression with graph attention network and shows strong potential for downstream analyses, offering deeper insights into disease pathology and tumor invasion.
Jing Lin, Ai-Jing Feng, Yan-Kun Cao et al.· Computational biology and ch...· 0 citations
A systematic ablation study providing evidence-based design guidance for spatial transcriptomics GNNs is provided, with findings support using GAT attention and contrastive learning and investing in node feature quality over edge feature complexity.
M. Al-Taie, Firas Hazzaa, Akram Qashou et al.· BMC Bioinformatics· 0 citations
Spatial multi-omics technologies facilitate simultaneous measurement of multiple molecular modalities within their native spatial context, offering opportunities to characterize tissue organization and cellular heterogeneity, and SpaHDSRL, a hierarchical dual-graph self-supervised representation learning framework for...
Xiang Li, Kang-Kang Zhang, Yi-Fei Li et al.· Briefings in Bioinformatics· 0 citations
A novel Shared-Specific Multi-view Graph Convolutional Network (SSMGCN) is proposed, which consistently outperforms existing methods in spatial domain identification and its unified embedding supports downstream tasks such as cell-type annotation, spatial localization, and functional analysis, providing a robust founda...
Wei Zhang, Dan-Yang Dong, Bang-Yi Zhang et al.· IEEE transactions on computa...· 0 citations
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