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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Huang, Zhaoyang Guo, Xinyang Qin, Jie Gao, Lin Ju, Fen Zhao, Chenguang Yu, Liang |
| Abstract | RNA velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. However, 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 an incorrect velocity stream. 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 is demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb tissue, as well as on several public datasets. The results show that VeloVGI outperformed other methods in terms of metric performance. |
| Related Links | https://bmcbiol.biomedcentral.com/counter/pdf/10.1186/s12915-024-02085-8.pdf |
| Ending Page | 16 |
| Page Count | 16 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17417007 |
| DOI | 10.1186/s12915-024-02085-8 |
| Journal | BMC Biology |
| Issue Number | 1 |
| Volume Number | 22 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-12-18 |
| Access Restriction | Open |
| Subject Keyword | Life Sciences scRNA-seq data RNA velocity Batch effect Complex lineage Optimal transport |
| Content Type | Text |
| Resource Type | Article |
| Subject | Ecology, Evolution, Behavior and Systematics Structural Biology Biochemistry, Genetics and Molecular Biology Plant Science Biotechnology Physiology Agricultural and Biological Sciences Cell Biology Developmental Biology |
| Journal Impact Factor | 4.4/2023 |
| 5-Year Journal Impact Factor | 5.4/2023 |
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