AJGM:使用自适应图形模型对异质基因网络进行联合学习
Shunqi Yang1, Lingyi Hu1, Pengzhou Chen1
1Department of Statistics, Hunan University, Changsha, Hunan, 410006, China.
Bioinformatics (Oxford, England)
|March 12, 2025
概括
我们开发了一个自适应性联合图形模型 (AJGM) 来从异质数据中推断基因网络. AJGM准确地识别了跨亚型的共享基因关系,在网络估计和样本分类中表现优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 基因网络推断揭示了生物途径和基因功能关系.
- 异质基因表达数据可能表明未知的亚型,需要同时分类和网络推断的方法.
- 现有的高斯图形模型 (GGM) 无法捕捉跨子类型的网络异质性,并与零膨胀单细胞RNA-seq (scRNA-seq) 数据进行斗争.
研究的目的:
- 提出一个自适应性联合图形模型 (AJGM) 来从异质数据中进行可靠的基因网络估计.
- 通过强调跨亚型的共享基因关系来提高网络推断的准确性.
- 为了解决现有方法的局限性,特别是对于零膨胀的scRNA-seq数据.
主要方法:
- 开发了适应性联合图形模型 (AJGM),包含了共享关系的整体网络.
- 利用自适应权重将子类型网络与整体网络连接起来,加强对共同点的关注.
- 将AJGM应用于合成数据集和现实世界的基因表达数据.
主要成果:
- 与合成数据的现有方法相比,AJGM在样本分类和网络推理方面表现优异.
- 该模型有效地确定了共享的基因关系,这与以前的方法相比是一个关键的优势.
- 对三阴性乳腺癌数据的应用验证了已知的途径,并确定了新的生物学见解.
结论:
- AJGM提供了一个强大的框架,可以从异构的生物数据中推断基因网络.
- 该方法通过有效建模共享和亚型特定的基因关系来提高准确性.
- AJGM为分析复杂的生物系统,包括scRNA-seq数据提供了显著的进步.
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