NetReg: Network-regularized linear models for biological association studies

Dirmeier S, Fuchs C, Mueller NS, Theis FJ (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Bioinformatics Oxford University Press (OUP): Policy B - Oxford Open Option B

Book Volume: 34

Pages Range: 896-898

Journal Issue: 5

DOI: 10.1093/bioinformatics/btx677

Abstract

Modelling biological associations or dependencies using linear regression is often complicated when the analyzed data-sets are high-dimensional and less observations than variables are available (n ≪ p). For genomic data-sets penalized regression methods have been applied settling this issue. Recently proposed regression models utilize prior knowledge on dependencies, e.g. in the form of graphs, arguing that this information will lead to more reliable estimates for regression coefficients. However, none of the proposed models for multivariate genomic response variables have been implemented as a computationally efficient, freely available library. In this paper we propose netReg, a package for graph-penalized regression models that use large networks and thousands of variables. netReg incorporates a priori generated biological graph information into linear models yielding sparse or smooth solutions for regression coefficients.

Involved external institutions

Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich

Switzerland (CH) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich

Germany (DE)

How to cite

APA:

Dirmeier, S., Fuchs, C., Mueller, N.S., & Theis, F.J. (2018). NetReg: Network-regularized linear models for biological association studies. Bioinformatics, 34(5), 896-898. https://doi.org/10.1093/bioinformatics/btx677

MLA:

Dirmeier, Simon, et al. "NetReg: Network-regularized linear models for biological association studies." Bioinformatics 34.5 (2018): 896-898.

BibTeX: Download