对单细胞测序分析的一种信息理论方法
Michael J Casey1,2, Jörg Fliege1, Rubén J Sánchez-García3,4,5
1Mathematical Sciences, University of Southampton, Southampton, UK.
BMC bioinformatics
|August 12, 2023
概括
我们开发了一种新的方法来测量单细胞测序数据中的基因表达异质性. 这种方法有效地识别了不同的细胞类型,并改善了对生物见解的数据分析.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞测序 (sc-Seq) 实验产生了大量的数据集.
- 大量的sc-Seq数据量并不总是与高信息含量相关.
- 在sc-Seq数据中量化信息对于生物发现至关重要.
研究的目的:
- 为了正式量化从sc-Seq实验中获得的信息.
- 建立基因表达异质性和细胞类型之间的联系.
- 为sc-Seq数据开发一个无监督的集群方法.
主要方法:
- 在sc-Seq实验中信息的正式量化.
- 将基因表达异质性分解为集群内部和集群内部的组成部分.
- 利用异质性作为集群算法的客观函数.
主要成果:
- 证明了量化信息与基因表达异质性之间的直接对应.
- 表明异质性可以分解为细胞类型特定 (集群间) 和细胞类型内部 (集群内) 的组件.
- 验证的异质性作为细胞类型相关的基因表达模式的描述符.
结论:
- 基因异质性为细胞类型提供了生物学上有意义的定义.
- 开发的异质性衡量标准是非参数的,内在的和不偏见的.
- 为sc-Seq数据集群开发了一种高效,无监督的方法,并在R包中开发并实施.
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