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STimage dataset for SkinVisium raw sequencing data

The University of Queensland
Dr Quan Nguyen (Aggregated by) Dr Quan Nguyen (Aggregated by) Mr Xiao Tan (Aggregated by) Mr Xiao Tan (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/c179fee&rft.title=STimage dataset for SkinVisium raw sequencing data&rft.identifier=RDM ID: 0407b2e0-4d2c-4855-811a-99692de0d95a&rft.publisher=The University of Queensland&rft.description=Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology. This dataset contains the raw sequencing data for SkinVsium. It is supplementary raw data ffor the previous dataset - see the link above STimage dataset with BreastCancer_Xenium and SkinVisium files.&rft.creator=Dr Quan Nguyen&rft.creator=Dr Quan Nguyen&rft.creator=Mr Xiao Tan&rft.creator=Mr Xiao Tan&rft.date=2025&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Bioinformatics and computational biology&rft_subject=BIOLOGICAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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[email protected]
Institute for Molecular Bioscience

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Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology. This dataset contains the raw sequencing data for SkinVsium. It is supplementary raw data ffor the previous dataset - see the link above STimage dataset with BreastCancer_Xenium and SkinVisium files.

Issued: 2025

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Biological Sciences | Bioinformatics and Computational Biology | eng |

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