ArticleBioinformatics (Oxford, England)2024
SST-editing: in silico spatial transcriptomic editing at single-cell resolution.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Spatial omics technology potentially promotes the progress of tumor immunotherapy.British journal of cancer · 2025Review
- Towards generative digital twins in biomedical research.Computational and structural biotechnology journal · 2024Review
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2 authors.
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Abstract
motivationGenerative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data.
resultsWe propose In Silico Spatial Transcriptomic editing that enables gene expression-guided editing of immunofluorescence images. Using cell-level spatial transcriptomics data extracted from normal and tumor tissue slides, we train the approach under the framework of GAN (Inversion). To simulate cellular state transitions, we then feed edited gene expression levels to trained models. Compared to normal cellular images (ground truth), we successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features. AVAILABILITY AND IMPLEMENTATION: https://github.com/CTPLab/SST-editing.
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