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- W4323038957 abstract "Abstract Owing to recent advances in resolution and field-of-view, spatially resolved sequencing has emerged as a cutting-edge technology that provides a technical foundation for the interpretation of large tissues at the single-cell level. To generate accurate single-cell spatial gene expression profiles from high-resolution spatial omics data and associated images, a powerful one-stop toolbox is required. Here, we present StereoCell, an image-facilitated cell segmentation framework for high-resolution and large field-of-view spatial transcriptomics. StereoCell provides a comprehensive and systematic platform for the generation of high-confidence single-cell spatial data, which includes image stitching, registration, nuclei segmentation, and molecule labeling. During image stitching and molecule labeling, StereoCell delivers better-performing algorithms to reduce stitching error and time, in addition to improving the signal-to-noise ratio of single-cell gene expression data, in comparison with existing methods. Additionally, StereoCell has been shown to obtain highly accurate single-cell spatial data using mouse brain tissue, which facilitated clustering and annotation." @default.
- W4323038957 created "2023-03-04" @default.
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- W4323038957 date "2023-03-01" @default.
- W4323038957 modified "2023-10-16" @default.
- W4323038957 title "StereoCell enables highly accurate single-cell segmentation for spatial transcriptomics" @default.
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- W4323038957 doi "https://doi.org/10.1101/2023.02.28.530414" @default.
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