RT Journal Article
SR Electronic
T1 DysRegNet: Patient-specific and confounder-aware dysregulated network inference
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.04.29.490015
DO 10.1101/2022.04.29.490015
A1 Olga Lazareva
A1 Zakaria Louadi
A1 Johannes Kersting
A1 Jan Baumbach
A1 David B. Blumenthal
A1 Markus List
YR 2022
UL http://biorxiv.org/content/early/2022/05/01/2022.04.29.490015.abstract
AB Gene regulation is frequently altered in diseases in unique and often patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not focus on disease-specific dysregulation or lack assessments of statistical significance. Moreover, they do not account for clinically important confounders such as age, sex or treatment history.To overcome these shortcomings, we present DysRegNet, a novel method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. We compared DysRegNet to state-of-the-art methods and demonstrated that DysRegNet produces more interpretable and biologically meaningful networks. Independent information on promoter methylation and single nucleotide variants further corroborate our results. We apply DysRegNet to eleven TCGA cancer types and illustrate how the inferred networks can be used for down-stream analysis. We show that unique as well as cancer-type-specific dysregulation patterns exist and highlight immune-related mechanisms that are not obvious when focusing on individual genes rather than their interactions.DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package) and analysis results for eleven TCGA cancer types are further available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).Competing Interest StatementThe authors have declared no competing interest.