CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomics Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.1101/2023.02.28.530414
Background Owing to recent advances in resolution and field-of-view, spatially resolved transcriptomics sequencing, such as Stereo-seq, has emerged as a cutting-edge technology 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, a powerful computational tool is required. Findings We present CellBin, an image-facilitated one-stop pipeline for high-resolution and large field-of-view spatial transcriptomic data of Stereo-seq. CellBin provides a comprehensive and systematic platform for generating high-confidence single-cell spatial gene expression profiles, which specifically includes image stitching, image registration, tissue segmentation, nuclei segmentation and molecule labeling. CellBin is user-friendly and does not require a specific level of omics and image analysis expertise. Conclusions During image stitching and molecule labeling, CellBin 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, CellBin has been shown to obtain highly accurate single-cell spatial data using mouse brain tissue, which facilitated clustering and annotation.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.02.28.530414
- https://www.biorxiv.org/content/biorxiv/early/2023/03/31/2023.02.28.530414.full.pdf
- OA Status
- green
- Cited By
- 18
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323038957