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

Yet Nguyen,Christopher K Tuggle,Haibo Liu,Dan Nettleton

doi:10.1007/s13253-015-0226-1

Abstract

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 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/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 differentially 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 selection procedure over alternative strategies that either ignore or adjust for all measured covariates.

Highlights

A standard challenge in transcriptomic data analysis is to identify genes whose mean transcript abundance levels differ across the levels of a categorical factor of primary scientific interest
The Oracle procedure cannot be used in practice, but its inclusion provides a useful reference measure of the performance achieved if covariate selection were perfect
We evaluated each procedure’s performance according to three criteria: the incurred false discovery rate (FDR) when FDR is nominally controlled at 5%, the number of true positive (NTP) declarations of differential expression, and the partial area under the receiver operating characteristic curve (PAUC) corresponding to false positive rates less than or equal to 0.05

Summary

Introduction

A standard challenge in transcriptomic data analysis is to identify genes whose mean transcript abundance levels differ across the levels of a categorical factor of primary scientific interest (e.g., treatment, genotype, tissue, or disease state). Such genes are typically referred to as differentially expressed (DE). For the purpose of identifying DE genes, the number of reads matching each of thousands of gene sequences is determined for each of several experimental or observational units. These read counts serve as measures of RNA abundance. A generalized linear model with a log link and a negative binomial response is fit to the count data for each gene, and DE genes are identified by testing, for each gene, whether a model parameter or linear combination of model parameters is zero

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