ArticleBriefings in bioinformatics2024
Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics.Nature communications · 2026Article
- DSSMST: A Deterministic State Space Model for Self-Supervised Spatial Domain Identification in Spatial Transcriptomics.Biochemical genetics · 2026Article
- A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics.Functional & integrative genomics · 2026Article
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MMSpa is a deep learning-based tool that enhances the identification of spatial domains in spatial transcriptomics studies.PLoS biology · 2026Article
- SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation.PLoS computational biology · 2025Article
- Spatially informed graph transformers for spatially resolved transcriptomics.Communications biology · 2025Article
- Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics.Computational and structural biotechnology journal · 2024Review
- Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.Briefings in bioinformatics · 2024Article
- Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.Briefings in bioinformatics · 2024Article
- scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.Briefings in bioinformatics · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.
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