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

Date
2015-12-01
Authors
Nguyen, Yet
Nettleton, Dan
Nettleton, Dan
Liu, Haibo
Tuggle, Christopher
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Animal Science
Organizational Unit
Statistics
Organizational Unit
Journal Issue
Series
Department
Animal ScienceStatistics
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.

Comments

This article is published as Nguyen, Yet, Dan Nettleton, Haibo Liu, and Christopher K. Tuggle. "Detecting differentially expressed genes with rna-seq data using backward selection to account for the effects of relevant covariates." Journal of agricultural, biological, and environmental statistics 20, no. 4 (2015): 577-597. doi: 10.1007/s13253-015-0226-1.

Description
Keywords
Citation
DOI
Collections