Trajectory Inference for Single Cell Omics
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911330401779712 |
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| author | Hutton, Alexandre Meyer, Jesse G. |
| author_facet | Hutton, Alexandre Meyer, Jesse G. |
| contents | Trajectory inference is used to order single-cell omics data along a path that reflects a continuous transition between cells. This approach is useful for studying processes like cell differentiation, where a stem cell matures into a specialized cell type, or investigating state changes in pathological conditions. In the current article, we provide a general introduction to trajectory inference, explaining the concepts and assumptions underlying the different methods. We then briefly discuss the strengths and weaknesses of different trajectory inference methods. We also describe best practices for using trajectory inference, such as how to validate the results and how to interpret them in the context of biological knowledge. Finally, the article highlights some applications of trajectory inference in single-cell omics research. These applications include studying cell differentiation, development, and disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trajectory Inference for Single Cell Omics Hutton, Alexandre Meyer, Jesse G. Quantitative Methods Genomics Molecular Networks Trajectory inference is used to order single-cell omics data along a path that reflects a continuous transition between cells. This approach is useful for studying processes like cell differentiation, where a stem cell matures into a specialized cell type, or investigating state changes in pathological conditions. In the current article, we provide a general introduction to trajectory inference, explaining the concepts and assumptions underlying the different methods. We then briefly discuss the strengths and weaknesses of different trajectory inference methods. We also describe best practices for using trajectory inference, such as how to validate the results and how to interpret them in the context of biological knowledge. Finally, the article highlights some applications of trajectory inference in single-cell omics research. These applications include studying cell differentiation, development, and disease. |
| title | Trajectory Inference for Single Cell Omics |
| topic | Quantitative Methods Genomics Molecular Networks |
| url | https://arxiv.org/abs/2502.09354 |