ArticleNature communications2024
PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Spatially organized regulated cell death-immune coupling in solid tumors: integrating spatial omics with actionable regulated cell death biology.Molecular cancer · 2026Review
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.Briefings in bioinformatics · 2026Article
- DuaST: an integrated deep learning framework for spatial transcriptomics with cross-branch interaction.Briefings in bioinformatics · 2026Article
- SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- stRGAT: identifying spatial domains in spatial transcriptomics via a relational graph attention network.Journal of translational medicine · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.PLoS computational biology · 2026Article
- Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper.Genome biology · 2025Article
- DiSTect: a Bayesian model for disease-associated gene discovery and prediction in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Denoising spatially resolved transcriptomics with consistency of heterogeneous spatial coordinates, transcription, and morphology.Briefings in bioinformatics · 2025Article
- Thor: a platform for cell-level investigation of spatial transcriptomics and histology.Nature communications · 2025Article
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.Nucleic acids research · 2025Article
- Spatially informed graph transformers for spatially resolved transcriptomics.Communications biology · 2025Article
- Systematic inference of super-resolution cell spatial profiles from histology images.Nature communications · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
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Authors and funding
11 authors.
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Abstract
Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.
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