Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection.
Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data.
Department of Biostatistics, University of Iowa, Iowa City, IA, USA.
Yaohui
Y
Zeng
Department of Biostatistics, University of Iowa, Iowa City, IA, USA.
Patrick
P
Breheny
gene set enrichment analysis; overlapping group lasso; pathway selection; penalized logistic regression
Hum Mutat. 2002 Jun;19(6):607-14
12007217
Nat Med. 2002 Aug;8(8):816-24
12118244
Nat Genet. 2003 Jul;34(3):267-73
12808457
Nat Genet. 2004 Jul;36(7):663; author reply 663
15226741
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Biostatistics. 2007 Apr;8(2):265-84
16772399
Bioinformatics. 2007 Apr 15;23(8):980-7
17303618
BMC Bioinformatics. 2007 Jul 05;8:242
17612399
Biometrics. 2007 Dec;63(4):1079-88
18078480
Brief Bioinform. 2008 May;9(3):189-97
18202032
Bioinformatics. 2008 Nov 15;24(22):2586-91
18790795
Bioinformatics. 2010 Sep 1;26(17):2176-82
20610611
Stat Methods Med Res. 2016 Feb;25(1):472-87
23070592
Stat Comput. 2015 Mar;25(2):173-187
25750488
BMC Bioinformatics. 2015 Apr 28;16:132
25928861