基因共同表达网络中的细胞类型异质性:对毒理学研究的影响
Imke B Bruns1, Yingxue Li1, James L Stevens1
1Division of Cell Systems and Drug Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Briefings in bioinformatics
|August 26, 2025
概括
了解基因共同表达网络对于生物见解和安全性评估至关重要. 本综述强调了捕获细胞类型特定模式的方法,克服了转录组数据分析的挑战,以进行更好的毒性和致病性研究.
科学领域:
- 基因组学
- 系统生物学
- 生物信息学
背景情况:
- 生物研究旨在了解推动生物反应的基因和蛋白质相互作用.
- 转录组数据分析对于了解病变和药物诱导的不利影响至关重要.
- 转录组数据的高维度和复杂性给解释带来了挑战.
研究的目的:
- 审查捕获细胞类型特异性基因共同表达模式的方法.
- 讨论这些方法对于理解毒性和致病机制的有用性.
- 解决细胞类型异质性在大量组织分析中的混影响.
主要方法:
- 基因协同表达分析以构建跨疗法协同表达的基因网络.
- 网络推断以确定生物相关性和基因病理相关性.
- 为特定细胞类型的共同表达模式捕获突出方法.
主要成果:
- 基因共同表达分析减少了复杂性,并有助于推断生物相关性.
- 大量组织中的细胞类型异质性可能会掩盖细胞内类型特异的共同表达网络.
- 有方法可以捕获细胞类型特定的共表达模式.
结论:
- 细胞类型的共同表达分析提供了更好的生物洞察力.
- 这些方法增强了基于机制的毒性和致病性安全性评估.
- 克服大量组织的局限性是准确的转录学解释的关键.
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