StabJGL: a stability approach to sparsity and similarity selection in multiple network reconstruction

06/05/2023
by   Camilla Lingjærde, et al.
0

In recent years, network models have gained prominence for their ability to capture complex associations. In statistical omics, networks can be used to model and study the functional relationships between genes, proteins, and other types of omics data. If a Gaussian graphical model is assumed, a gene association network can be determined from the non-zero entries of the inverse covariance matrix of the data. Due to the high-dimensional nature of such problems, integrative methods that leverage similarities between multiple graphical structures have become increasingly popular. The joint graphical lasso is a powerful tool for this purpose, however, the current AIC-based selection criterion used to tune the network sparsities and similarities leads to poor performance in high-dimensional settings. We propose stabJGL, which equips the joint graphical lasso with a stable and accurate penalty parameter selection approach that combines the notion of model stability with likelihood-based similarity selection. The resulting method makes the powerful joint graphical lasso available for use in omics settings, and outperforms the standard joint graphical lasso, as well as state-of-the-art joint methods, in terms of all performance measures we consider. Applying stabJGL to proteomic data from a pan-cancer study, we demonstrate the potential for novel discoveries the method brings. A user-friendly R package for stabJGL with tutorials is available on Github at https://github.com/Camiling/stabJGL.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/20/2021

GGLasso – a Python package for General Graphical Lasso computation

We introduce GGLasso, a Python package for solving General Graphical Las...
research
09/04/2009

Tuning parameter selection for penalized likelihood estimation of inverse covariance matrix

In a Gaussian graphical model, the conditional independence between two ...
research
06/23/2022

Scalable Multiple Network Inference with the Joint Graphical Horseshoe

Network models are useful tools for modelling complex associations. If a...
research
01/04/2023

l_1-2 GLasso: L_1-2 Regularized Multi-task Graphical Lasso for Joint Estimation of eQTL Mapping and Gene Network

A critical problem in genetics is to discover how gene expression is reg...
research
04/20/2019

Estimating Sparse Networks with Hubs

Graphical modelling techniques based on sparse selection have been appli...
research
03/07/2015

Exact Hybrid Covariance Thresholding for Joint Graphical Lasso

This paper considers the problem of estimating multiple related Gaussian...

Please sign up or login with your details

Forgot password? Click here to reset