斯利弗:通过因果结构学习和模块聚合揭示单细胞转录组数据的大规模基因调控网络
Hongyang Jiang1, Yuezhu Wang1, Chaoyi Yin1
1School of Artificial Intelligence, Jilin University, Changchun, 130012, China.
Computers in biology and medicine
|June 16, 2024
概括
我们介绍了SLIVER,这是一种用于使用因果推理构建基因调节网络 (GRNs) 的新算法. 与基于关联的方法相比,SLIVER显著提高了准确性和效率,使复杂生物系统的分析成为可能.
科学领域:
- 计算生物学和生物信息学
- 系统生物学 系统生物学
- 基因组学和转录基因组学
背景情况:
- 目前的基因调节网络 (GRN) 推断方法主要使用相关性分析,该分析无法捕捉生物系统中固有的因果关系.
- 现有的因果发现算法,通常基于定向环形图 (DAG),面临着大型网络的计算挑战,限制了它们的可扩展性.
研究的目的:
- 开发一个可扩展和准确的因果推理算法,用于基因调节网络的构建.
- 解决处理复杂生物数据和大规模网络的现有方法的局限性.
主要方法:
- 提出了SLIVER (因果发现通过维度减少) 算法,集成因果结构方程模型和图解分解.
- SLIVER使用因子节点来表示功能模块,将GRN缩小到低维矩阵,以实现高效的因果学习.
- 采用结构因果模型 (SCM) 并在缩小维空间中强制执行 DAG 约束,通过等号相似性指导功能聚合.
主要成果:
- 与12种广泛使用的方法相比,SLIVER在12个单细胞转录组数据集中在GRN推断准确度和计算效率方面表现出卓越的表现.
- 对因子节点的分析揭示了功能模块内基因聚合的生物学解释.
- 对2型糖尿病的成功应用scRNA-seq数据确定了β细胞的关键转录调节变化.
结论:
- 斯利弗为因果GRN推理提供了强大且可扩展的方法,克服了传统方法的局限性.
- 该算法提供了对基因调节和功能模块的生物学可解释的见解.
- 在分子层面上,Sliver有望促进我们对复杂疾病的理解,如2型糖尿病.
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