ArticleGenome research2023
Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics.
Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Unified representation learning for spatial multi-omics.Bioinformatics (Oxford, England) · 2026Article
- STWave: Fine-Scale Spatial Structure Discovery in Microscopic-Resolution Spatial Transcriptomics via Patchwise Wavelet Graphs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.Bioinformatics (Oxford, England) · 2026Article
- ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled Experts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.Nature methods · 2026Article
- STAHD: a scalable and accurate method to detect spatial domains in high-resolution spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- A graph neural network-based spatial multi-omics data integration method for deciphering spatial domains.PLoS computational biology · 2025Article
- Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Spatially informed graph transformers for spatially resolved transcriptomics.Communications biology · 2025Article
- INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.Nature communications · 2025Article
- Article
- Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.Briefings in bioinformatics · 2024Article
- Enhancing spatial domain detection in spatial transcriptomics with EnSDD.Communications biology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.
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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.