CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomics Article Swipe
Mei Li
,
Huanlin Liu
,
Qiang Kang
,
Shuangsang Fang
,
Min Li
,
Jiajun Zhang
,
Fei Teng
,
Dan Wang
,
Weixuan Cen
,
Zepeng Li
,
Ning Feng
,
Jing Guo
,
Qiqi He
,
Leying Wang
,
Tiantong Zheng
,
Shengkang Li
,
Yinqi Bai
,
Min Xie
,
Yong Bai
,
Sha Liao
,
Ao Chen
,
Yong Zhang
,
Susanne Brix
,
Xun Xu
,
Yuxiang Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.02.28.530414
YOU?
·
· 2023
· Open Access
·
· 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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Metadata
- 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
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4323038957Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.02.28.530414Digital Object Identifier
- Title
-
CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomicsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-01Full publication date if available
- Authors
-
Mei Li, Huanlin Liu, Qiang Kang, Shuangsang Fang, Min Li, Jiajun Zhang, Fei Teng, Dan Wang, Weixuan Cen, Zepeng Li, Ning Feng, Jing Guo, Qiqi He, Leying Wang, Tiantong Zheng, Shengkang Li, Yinqi Bai, Min Xie, Yong Bai, Sha Liao, Ao Chen, Yong Zhang, Susanne Brix, Xun Xu, Yuxiang LiList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.02.28.530414Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/03/31/2023.02.28.530414.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/03/31/2023.02.28.530414.full.pdfDirect OA link when available
- Concepts
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Pipeline (software), Transcriptome, Computational biology, Gene expression, Expression (computer science), Computer science, High resolution, Gene, Biology, Genetics, Geography, Remote sensing, Operating system, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 8, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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