ArticleNucleic acids research2023
STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics.Nature communications · 2026Article
- Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- DuaST: an integrated deep learning framework for spatial transcriptomics with cross-branch interaction.Briefings in bioinformatics · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets.Communications biology · 2025Article
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper.Genome biology · 2025Article
- SpaCross deciphers spatial structures and corrects batch effects in multi-slice spatially resolved transcriptomics.Communications biology · 2025Article
- FmH2ST: foundation model-based spatial transcriptomics generation from histological images.Nucleic acids research · 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
- SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.PLoS computational biology · 2025Article
- Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.Genome medicine · 2025Article
- Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments.NPJ precision oncology · 2025Review
- Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering.Computational and structural biotechnology journal · 2024Article
- Recent advances in spatially variable gene detection in spatial transcriptomics.Computational and structural biotechnology journal · 2024Review
- Enhancing spatial domain detection in spatial transcriptomics with EnSDD.Communications biology · 2024Article
- Statistical batch-aware embedded integration, dimension reduction, and alignment for spatial transcriptomics.Bioinformatics (Oxford, England) · 2024Article
- A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.Bioinformatics (Oxford, England) · 2024Article
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5 authors.
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Abstract
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graph-attention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with serveral commonly used competing methods on various spatial transcriptomic data generated by different platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.
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