Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data Article Swipe
Zhaoyang Huang
,
Xinyang Guo
,
Jie Qin
,
Lin Gao
,
Fen Ju
,
Chenguang Zhao
,
Liang Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.19.567699
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.19.567699
RNA Velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. Nevertheless, existing RNA velocity methods are limited by the batch effect because they cannot directly correct for batch effects in the input data, which comprises spliced and unspliced matrices in a proportional relationship. This limitation can lead to incorrect velocity graphs. This paper introduces VeloVGI, which addresses this issue innovatively in two key ways. Firstly, it employs an optimal transport (OT) and mutual nearest neighbor (MNN) approach to construct neighbors in batch data. This strategy overcomes the limitations of existing methods that are affected by the batch effect. Secondly, VeloVGI improves upon VeloVI’s velocity estimation by incorporating the graph structure into the encoder for more effective feature extraction. The effectiveness of VeloVGI was demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb, as well as on several public datasets. The results showed that VeloVGI outperformed other methods in terms of metric performance. Significance Statement RNA Velocity is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. This paper introduces VeloVGI, which addresses this batch effect issue for existing RNA velocity methods. The effectiveness of VeloVGI was demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb, as well as on several public datasets. The results showed that VeloVGI outperformed other methods in terms of metric performance.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.11.19.567699
- https://www.biorxiv.org/content/biorxiv/early/2023/11/19/2023.11.19.567699.full.pdf
- OA Status
- green
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388806787
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388806787Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.11.19.567699Digital Object Identifier
- Title
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Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq dataWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
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2023-11-19Full publication date if available
- Authors
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Zhaoyang Huang, Xinyang Guo, Jie Qin, Lin Gao, Fen Ju, Chenguang Zhao, Liang YuList of authors in order
- Landing page
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https://doi.org/10.1101/2023.11.19.567699Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/11/19/2023.11.19.567699.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/11/19/2023.11.19.567699.full.pdfDirect OA link when available
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Computer science, Data mining, Metric (unit), RNA, Inference, Encoder, Algorithm, Artificial intelligence, Biology, Engineering, Gene, Operations management, Biochemistry, Operating systemTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 49, 84, 145, 215 |
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| cited_by_percentile_year | |
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