ArticleBMC bioinformatics2021
CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 30 citations in OpenAlex.
- Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell.Interdisciplinary sciences, computational life sciences · 2026Article
- SHIELD: A weakly supervised graph attention neural network for decoding disease-relevant cell-cell interactions.Patterns (New York, N.Y.) · 2026Article
- Histology-informed spatial domain identification through multi-view graph convolutional networks.PLoS computational biology · 2026Article
- stRGAT: identifying spatial domains in spatial transcriptomics via a relational graph attention network.Journal of translational medicine · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine.World journal of diabetes · 2025Review
- [Identifying spatial domains from spatial transcriptome by graph attention network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewBriefings in bioinformatics · 2024Article
- Spatial transcriptomics in development and disease.Molecular biomedicine · 2023Review
- Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.Genome biology · 2023Article
- SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.Biomolecules · 2023Article
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.Frontiers in oncology · 2023Review
- Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight.Cell reports methods · 2022Review
- Spatiotemporal Omics-Refining the landscape of precision medicine.Life medicine · 2022Review
- Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering.GigaScience · 2022Article
- Spatial components of molecular tissue biology.Nature biotechnology · 2022Review
- Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics.Frontiers in neuroscience · 2022Article
- Emerging artificial intelligence applications in Spatial Transcriptomics analysis.Computational and structural biotechnology journal · 2022Review
- Computer vision for plant pathology: A review with examples from cocoa agriculture.Applications in plant sciencesArticle
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships across cell types in a tissue. However, many current analysis methods do not take full advantage of the spatial organization of the data, instead treating pixels as independent features. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering.
resultsBy analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes broader spatial patterns rather than pixel-level correlation. CoSTA provides a quantitative measure of expression pattern similarity between each pair of genes rather than only classifying genes into categories. We find that CoSTA identifies narrower, but biologically relevant, sets of significantly related genes as compared to other approaches.
conclusionsThe deep learning CoSTA approach provides a different angle to spatial transcriptomics analysis by focusing on the shape of expression patterns, using more information about the positions of neighboring pixels than would an overlap or pixel correlation approach. CoSTA can be applied to any spatial transcriptomics data represented in matrix form and may have future applications to datasets such as histology in which images of different genes are from similar but not identical biological sections.
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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.