一个单细胞CaCa
Xiaojun Wu1, Roy Wollman2,3, Adam L MacLean1
1Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.
Journal of the Royal Society, Interface
|June 7, 2023
概括
我们开发了贝叶斯单细胞分析方法,使用转移学习从基因表达和 (Ca2+) 信号推断细胞动态. 通过转录相似性对细胞进行排序,提高了区分信号配置文件和识别标记基因的准确性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 单细胞基因组技术为细胞异质性提供了前所未有的见解.
- 在单细胞中整合基因表达和动态信号数据仍然是参数推断的挑战.
研究的目的:
- 开发使用联合单细胞基因表达和Ca2+动态数据进行参数推断的贝叶斯方法.
- 为了利用转移学习,在单细胞种群中进行高效的参数推断.
- 为了研究转录状态和细胞信号传递动态之间的关系.
主要方法:
- 贝叶斯参数推理框架.贝叶斯参数推理框架.
- 转移学习方法用于在顺序分析的单个细胞之间共享信息.
- 细胞内Ca2+信号的动态建模.
- 对成千上万个单细胞进行分析,这些单细胞具有异质的反应.
主要成果:
- 转移学习加速了对细胞序列的参数推断,无论细胞的顺序如何.
- 通过转录相似性对细胞进行排序对于区分Ca2+动态配置文件和相关标记基因至关重要.
- 推断揭示了复杂的细胞异质性,在细胞内和细胞间的背景下有不同的参数共变.
结论:
- 单细胞参数推断,由转录相似性告知,可以量化基因表达和信号动态之间的关系.
- 开发的方法提高了单细胞数据的实用性,以了解细胞动态和异质性.
- 转录相似度排序是稳定识别细胞特异信号行为和分子标记物的关键.
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