Markku Kuismin, Mikko J Sillanpää, MCPeSe: Monte Carlo penalty selection for graphical lasso, Bioinformatics, Volume 37, Issue 5, 1 March 2021, Pages 726–727, https://doi.org/10.1093/bioinformatics/btaa734
MCPeSe : Monte Carlo penalty selection for graphical lasso
|Author:||Kuismin, Markku1,2; Sillanpää, Mikko J.1,2,3|
1Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland
2Biocenter Oulu, University of Oulu, Oulu FI-90014, Finland
3Infotech Oulu, University of Oulu, Oulu FI-90014, Finland
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021062840257
Oxford University Press,
|Publish Date:|| 2021-06-29
Motivation: 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.
Results: 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.
Availability and implementation: 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 information: Supplementary data are available at Bioinformatics online.
|Pages:||726 - 727|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
112 Statistics and probability
This work was supported by the Biocenter Oulu funding; the Technology Industries of Finland Centennial Foundation & the Jane and Aatos Erkko Foundation and the Academy of Finland Profi 5 funding for mathematics and AI: data insight for high-dimensional dynamics [Project 326291].
|Academy of Finland Grant Number:||
326291 (Academy of Finland Funding decision)
© The Author(s) 2020. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.