ArticleNature methods2024
Dependency-aware deep generative models for multitasking analysis of spatial omics data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Pan-tumor spatial transcriptomics reveals conserved properties of tumor organization.Cell reports. Medicine · 2026Article
- Decoding Spatial Heterogeneity and Multi-Omics Regulation with Hierarchical Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatial multi-omics imputation and embedding with SpaMIE.Cell reports methods · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- AGCLD: an adaptive graph contrastive learning method with denoising for spatial domain identification.Briefings in bioinformatics · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV.Nature communications · 2026Article
- A dependency-aware deep generative model for inferring RNA velocity from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.Bioinformatics (Oxford, England) · 2026Article
- GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- SMART: spatial multi-omic aggregation using graph neural networks and metric learning.Nature communications · 2026Article
- Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications.Precision clinical medicine · 2026Review
- DuaST: an integrated deep learning framework for spatial transcriptomics with cross-branch interaction.Briefings in bioinformatics · 2026Article
- GATCL: graph attention network meets contrastive learning for spatial domain identification.Briefings in bioinformatics · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.
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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.