ArticleBriefings in bioinformatics2021
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 2 of them syntheses that pooled it.
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Who cites it
144 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Enhancing RNA Capture Efficiency in Spatial Transcriptomics: A Review of Innovative Technologies and Strategies.International journal of molecular sciences · 2025Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Toward simultaneous pseudo-space reconstruction and cell-type deconvolution of single-cell spatial transcriptome using SpaDicer.Cell reports methods · 2026Article
- A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics.Functional & integrative genomics · 2026Article
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Review
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Navigating cell maps by deep learning integration of single-cell and spatially resolved transcriptomics.Briefings in bioinformatics · 2026Article
- GR2ST: spatial transcriptomics prediction based on graph-enhanced multimodal contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- DiSCO: deconvoluting spatial transcriptomics via combinatorial optimization with a foundational diffusion model.Briefings in bioinformatics · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- SA2E: spatial-aware auto-encoder for cell type deconvolution of spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- SpatialDG: a novel spatial domain identification method for spatially resolved transcriptomics data based on dual-graph neural network.Briefings in bioinformatics · 2026Article
- SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.Nature communications · 2026Article
84 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG), to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieving high-level segmentation and revealing spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization. DSTG is available as a ready-to-use open source software (https://github.com/Su-informatics-lab/DSTG) for precise interrogation of spatial organizations and functions in tissues.
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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.