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生物:一个贝叶斯框架,用于整合单细胞多组学来推断转录因子活动,并改善细胞的身份特征
Lan Cao1, Wenhao Zhang1, Fan Yang1,2,3
1Department of Automation, Xiamen University, Xiang'an South Road, Xiang'an, 361102, Xiamen, Fujian, China.
Briefings in bioinformatics
|January 20, 2025
概括
贝叶斯模型BIOTIC从单细胞多组数据推断转录因子 (TF) 活性,以了解细胞身份和基因调节. 这种方法准确地描述了细胞类型和发育轨迹,优于现有的方法.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 细胞命运的决定依赖于基因调控,转录因子 (TFs) 起着关键作用.
- TF活性取决于TF表达水平和染色质可访问性.
- 现有的模型往往难以将这些因素整合到准确的监管分析中.
研究的目的:
- 开发一个先进的贝叶斯模型,BIOTIC,使用单细胞多组数据推断转录因子 (TF) 活动.
- 为了利用基因调节结构和变异推理来进行可靠的TF活动估计.
- 为了证明BIOTIC在表征细胞身份和改进下游分析方面的实用性.
主要方法:
- 生物采用贝叶斯框架,将基因表达和色素可访问性从单细胞多组数据整合起来.
- 变异推理被用来估计模型的基因调节结构中的TF活性.
- 该模型的性能与细胞类型,发育跟踪和批次校正的基线方法进行验证.
主要成果:
- BIOTIC准确地推断出TF活性,这可以作为细胞身份的强有力的特征.
- 与现有方法相比,该模型在细胞类型,细胞发育跟踪和批量效应校正方面表现出卓越的性能.
- BIOTIC可以仅应用于单细胞转录组学数据,使TF活动推断和细胞地图中细胞类型注释成为可能.
结论:
- BIOTIC提供了一个开创性的,生物知情的框架,用于推断TF活性,并在基因调控水平上理解细胞身份.
- 该模型的可适应性结构允许结合额外的生物因素进行更全面的分析.
- 生物提升我们对TFs和基因表达之间的复杂相互作用的理解,推进细胞生物学研究.
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