Campus Units

Animal Science, Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

12-2015

Journal or Book Title

Journal of Agricultural, Biological, and Environmental Statistics

Volume

20

Issue

4

First Page

577

Last Page

597

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.

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.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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