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Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity.

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第一作者： Dee,Velazquez

第一单位： Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.;Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.

作者： Dee,Velazquez ^[1] ; Caleb,Hallinan ^[1] ; Roujin,An ^[1] ; Kalen,Clifton ^[1] ; Jean,Fan ^[1]

作者单位： Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.;Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA. ^[1]

关键词 deep learning feature extraction image analysis rasterization spatial transcriptomics

DOI 10.64898/2025.12.18.695193

PMID 41497642

发布时间 2026-01-12

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Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity.

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Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity.

bioRxiv

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