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6 April 2023 Single-cell gene expression prediction using H&E images based on spatial transcriptomics
Chao-Hui Huang, Yoson Park, Jincheng Pang, Jadwiga R. Bienkowska
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Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247105 (2023) https://doi.org/10.1117/12.2654294
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
Abstract
In situ RNA capturing represents an excellent opportunity for bridging transcriptomic data to a spatial domain, making it possible to map the gene expression to the corresponding anatomical structure. As a result, scientists can better understand the transcriptional heterogeneity with spatially resolved, anatomical, and pathophysiological contexts. However, high throughput sequencing technologies paired with histological images suffer from lower resolution mapping between transcriptome and imaging data. Here, we present Spatial Transcriptome Auto-encoder and Deconvolution (ST-AnD), a scalable deep generative model for predicting gene expression at cellular or nuclei level based on H&E imaging and in situ RNA capturing, thus allowing a better understanding of the tissue microenvironment.
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Chao-Hui Huang, Yoson Park, Jincheng Pang, and Jadwiga R. Bienkowska "Single-cell gene expression prediction using H&E images based on spatial transcriptomics", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247105 (6 April 2023); https://doi.org/10.1117/12.2654294
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KEYWORDS
Data modeling
Genomics
Machine learning
Spatial resolution
Tumors
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