Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland.
Department of Ecology and Genetics, University of Oulu, Oulu, Finland.
PLoS Comput Biol. 2021 May 3;17(5):e1008960. doi: 10.1371/journal.pcbi.1008960. eCollection 2021 May.
A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method's high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework's versatility and broad practical relevance.
已经开发出了各种各样的参数回归模型和共表达网络，用于从表达数据中寻找复杂性状的基因间相互作用。虽然这两种方法都有其众所周知的优点，但它们的协同潜力却知之甚少。我们的研究介绍了它们的方法融合，通过内置的信息共享机制，交叉利用了各个方法的优势。这种融合在理论上是基于两个基因之间在特定性状条件下的依赖模式，这取决于它们在基础参数模型中的作用。由此产生的特定性状的共表达网络估计方法 1) 有助于从参数角度增强对生物网络的解释，并且 2) 在估计过程中利用了基础参数模型本身。为了同时考虑到表达数据中通常存在的大量内在噪声和共线性，我们沿着这个框架引入了一种量身定制的共表达度量，以缓解相关的计算问题。在模拟场景中，与参考方法相比，该方法的显著进步证明了它的高效性。作为概念验证，这种协同方法成功地应用于急性髓细胞白血病数据的生存分析中，进一步突出了该框架的多功能性和广泛的实际相关性。
