Detecting Differentially Expressed Genes with RNA-Seq Data Using Backward Selection to Account for the Effects of Relevant Covariates

• Activity Number: 193
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yet Nguyen* and Dan Nettleton and Haibo Liu and Chris Tuggle
• Companies: Iowa State University and Iowa State University and Iowa State University and Iowa State University
• Keywords: False Discovery Rate ; Generalized Linear Model ; Grenander Estimator ; Kolmogorov-Smirnov Statistics ; Quasi-likelihood

Abstract

A common challenge in analysis of transcriptomic data is to identify differentially expressed (DE) genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can be measured simultaneously for thousands of genes in multiple biological samples using RNA sequencing (RNA-seq) technology. Part of the variation in RNA-seq measures of transcript abundance may be associated with variation in continuous and categorical covariates measured for each experimental unit and/or RNA sample. Ignoring relevant covariates or modeling the effects of irrelevant covariates can be detrimental to identifying DE genes. We propose a backward selection strategy for selecting a set of covariates whose effects are accounted for when searching for DE genes. We illustrate our approach through the analysis of an RNA-seq study intended to identify genes differentially expressed between two lines of pigs divergently selected for residual feed intake. We use simulation to show the advantages of our backward selection procedure over alternative strategies that either ignore or adjust for all measured covariates.