论文标题
贝叶斯辅助方向依赖性因果推断基因表达数据
Bayesian Copula Directional Dependence for causal inference on gene expression data
论文作者
论文摘要
建模和理解定向基因网络是生物学的主要挑战，因为它们在遗传系统的结构和功能中起着重要作用。 Copula定向依赖性（CDD）可以测量变量之间的定向连接性，而无需任何分布和线性假设的严格要求。此外，Copulas可以通过隔离关节分布的依赖性结构来实现这一目标。在这项工作中，引入了贝叶斯环境中常见主义者CDD的新型扩展。将新方法与频繁的CDD进行了比较，并在六个基因相互作用上进行了验证，三种来自鼠标scrna-seq数据集，三个来自散装的表观基因组数据集。结果表明，与频繁的方法相比，新颖的贝叶斯CDD能够鉴定出六个真实相互作用中的四个与鲁棒性增加的相互作用。因此，可以将贝叶斯CDD视为建模基因网络中信息流的替代方法。
Modelling and understanding directional gene networks is a major challenge in biology as they play an important role in the architecture and function of genetic systems. Copula Directional Dependence (CDD) can measure the directed connectivity among variables without any strict requirements of distributional and linearity assumptions. Furthermore, copulas can achieve that by isolating the dependence structure of a joint distribution. In this work, a novel extension of the frequentist CDD in the Bayesian setting is introduced. The new method is compared against the frequentist CDD and validated on six gene interactions, three coming from a mouse scRNA-seq dataset and three coming from a bulk epigenome dataset. The results illustrate that the novel proposed Bayesian CDD was able to identify four out of six true interactions with increased robustness compared to the frequentist method. Therefore, the Bayesian CDD can be considered as an alternative way for modeling the information flow in gene networks.
