单细胞组学的轨迹推断
Trajectory Inference for Single Cell Omics
AI总结：
本文介绍了单细胞组学中轨迹推断的基本概念、方法优缺点及应用，重点在于细胞分化、发育和疾病研究。
摘要：
轨迹推断用于按反映细胞间连续过渡的路径对单细胞组学数据进行排序。这种方法在研究如细胞分化等过程时非常有用，其中干细胞成熟为专门化的细胞类型，或调查病理条件下状态的变化。在本文中，我们提供了一个关于轨迹推断的一般介绍，解释了不同方法背后的概念和假设。我们随后简要讨论了不同轨迹推断方法的优缺点。我们还描述了使用轨迹推断的最佳实践，例如如何验证结果以及如何在生物学知识的背景下解释结果。最后，本文强调了轨迹推断在单细胞组学研究中的某些应用。这些应用包括研究细胞分化、发育和疾病。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
