Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland.
Biocenter Oulu, University of Oulu, Oulu FI-90014, Finland.
Bioinformatics. 2021 May 5;37(5):726-727. doi: 10.1093/bioinformatics/btaa734.
Graphical lasso (Glasso) is a widely used tool for identifying gene regulatory networks in systems biology. However, its computational efficiency depends on the choice of regularization parameter (tuning parameter), and selecting this parameter can be highly time consuming. Although fully Bayesian implementations of Glasso alleviate this problem somewhat by specifying a priori distribution for the parameter, these approaches lack the scalability of their frequentist counterparts.
Here, we present a new Monte Carlo Penalty Selection method (MCPeSe), a computationally efficient approach to regularization parameter selection for Glasso. MCPeSe combines the scalability and low computational cost of the frequentist Glasso with the ability to automatically choose the regularization by Bayesian Glasso modeling. MCPeSe provides a state-of-the-art 'tuning-free' model selection criterion for Glasso and allows exploration of the posterior probability distribution of the tuning parameter.
R source code of MCPeSe, a step by step example showing how to apply MCPeSe and a collection of scripts used to prepare the material in this article are publicly available at GitHub under GPL (https://github.com/markkukuismin/MCPeSe/).
Supplementary data are available at Bioinformatics online.
图lasso（Glasso）是系统生物学中用于识别基因调控网络的一种广泛使用的工具。然而，其计算效率取决于正则化参数（调谐参数）的选择，而选择该参数可能非常耗时。尽管 Glasso 的完全贝叶斯实现通过为参数指定先验分布在某种程度上缓解了这个问题，但这些方法缺乏其频率论对应物的可扩展性。
在这里，我们提出了一种新的蒙特卡罗惩罚选择方法（MCPeSe），这是一种用于 Glasso 正则化参数选择的计算效率方法。MCPeSe 将频率论 Glasso 的可扩展性和低计算成本与贝叶斯 Glasso 建模自动选择正则化的能力相结合。MCPeSe 为 Glasso 提供了一种最先进的“无调优”模型选择标准，并允许探索调优参数的后验概率分布。
MCPeSe 的 R 源代码，逐步展示如何应用 MCPeSe 的示例以及用于准备本文材料的脚本集在 GPL 下可在 GitHub 上公开获得（https://github.com/markkukuismin/MCPeSe/）。
补充数据可在生物信息学在线获得。
