netReg: network-regularized linear models for biological association studies

Bioinformatics
Simon DirmeierFabian J Theis

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. netReg is implemented as both R-package and C ++ commandline tool. The main computations are done in C ++, where we use Armadillo for fast matrix calculations and Dlib for optimization. The R package is freely available on Bioconductorhttps://bioconductor.org/packages/netReg. The command line tool can be ...Continue Reading

References

Jul 23, 2005·PLoS Biology·John D StoreyLeonid Kruglyak
Dec 31, 2008·BMC Bioinformatics·Peter Langfelder, Steve Horvath
Sep 13, 2012·BMC Bioinformatics·Andrey AlexeyenkoYudi Pawitan
Jul 5, 2013·Biometrics·Sunkyung KimXiaotong Shen
Jun 17, 2014·Bioinformatics·Wei ChengWei Wang
Jan 21, 2017·BMC Bioinformatics·André VeríssimoSusana Vinga

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Citations

Jul 1, 2020·Briefings in Bioinformatics·Susana Vinga

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