ArticlePLoS computational biology2025
SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
31 citing papers in PubMed.
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- st2traj: deconvolution-informed trajectory inference for multi-timepoint spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- ProST: an image prompt-guided multimodal representation learning framework for spatial domain identification.Bioinformatics (Oxford, England) · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping.Bioinformatics (Oxford, England) · 2026Article
- NicheDeSig: niche-aware deconvolution and adaptive signature analysis for spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics.Small methods · 2026Article
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Representation learning for multi-modal spatially resolved transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- SINTER3D: continuous 3D reconstruction of spatial transcriptomics via implicit neural representations.Genome biology · 2026Article
- HisCMCL: cross-modal contrastive learning with hierarchical multi-scale fusion for spatial expression prediction.Bioinformatics (Oxford, England) · 2026Article
- spAttClu: a spatial domain clustering model leveraging spatially weighted graph attention and contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder.Bioinformatics (Oxford, England) · 2026Article
- GR2ST: spatial transcriptomics prediction based on graph-enhanced multimodal contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- PAIR: Reconstructing Single-Cell Open-Chromatin Landscapes for Transcription Factor Regulome Mapping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- So3D: a comprehensive three-dimensional spatial omics resource for decoding tissue architecture in physiology and disease.Nucleic acids research · 2026Article
- STAN, a computational framework for inferring spatially informed transcription factor activity.Nucleic acids research · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.