Tutorial: Differential Network Analysis with Application to High-Dimensional Biological Data

Music City Center 

Identifying differences between biological networks, particularly in disease settings, can point to targets for diagnosis and treatment. Differential network analysis has been used to characterize gene expression network differences in cancer vs normal tissue, changes in brain networks over time in Alzheimer's, and gut microbial networks under various conditions. We identified 40+ methods for estimating and testing network differences, which are implemented in over 25 R and Python packages. There is not currently a practical review and comparative study of this wide variety of software. We will present a comprehensive application-focused review and comparative evaluation, with the aim of providing a resource for applied researchers to select and put these methods into practice. We will give accessible explanations of the overarching methods and provide reproducible code examples for select methods using publicly available biological datasets. We focus on comparing popular frequentist estimation (Joint Graphical Lasso, Danaher 2014 and iDingo, Class 2017) with a Bayesian counterpart (Spike and Slab Joint Graphical Lasso, Li 2019).

graphical models

biological pathway estimation

differential network analysis

Bayesian network estimation

software application

method overview 

Section on Statistical Learning and Data Science