Increasing the Accuracy of Gene Expression Classifiers by Incorporating Pathway Information: A Latent Group Selection Approach

• Activity Number: 429
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yaohui Zeng* and Patrick Breheny
• Companies: The University of Iowa and The University of Iowa
• Keywords: Overlapping group lasso ; Penalized logistic regression ; Pathway selection ; Gene set enrichment analysis ; Genomewide expression analysis ; R

Abstract

Discovering important genes that account for the phenotype of interest has long been challenging in genomewide 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 extend grpreg to allow for overlapping group structure using the latent variable approach proposed by Jacob et al. (2009). We compare this approach to both ordinary (non-grouped) lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information substantially improves the accuracy of gene expression classifiers and that hypothesis-testing approaches such as GSEA have several inherent limitations compared with the pathway-based regression models.