Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO.

ABSTRACT:

Background

Conventional differential gene expression analysis by methods such as student's t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker c

SUBMITTER: Zuo Y 

PROVIDER: S-EPMC5303311 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature