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

A common challenge in analysis of transcriptomic data is to identify differentially expressed genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can bemeasured simul-taneously for thousands of genes in multiple biological samples using RNA sequenc-ing (RNA-seq) technology. Part of the variation in RNA-seq measures of transcript abundance may be associated with variation in continuous and/or categorical covariates measured for each experimental unit or RNA sample. Ignoring relevant covariates or modeling the effects of irrelevant covariates can be detrimental to identifying differen-tially expressed genes. We propose a backward selection strategy for selecting a set of covariates whose effects are accounted for when searching for differentially expressed 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 selec-tion procedure over alternative strategies that either ignore or adjust for all measured covariates