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Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

5-25-2020

Journal or Book Title

Bioinformatics

DOI

10.1093/bioinformatics/btaa525

Abstract

Motivation: With the reduction in price of next generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs.

Results: We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units.

Availability:We provide anRpackage rmRNAseq implementing our proposed method (function TC_CAR1) at https://cran.r-project.org/web/packages/rmRNAseq/index.html. Reproducible R codes for data analysis and simulation are available at https://github.com/ntyet/rmRNAseq/ tree/master/simulation.

Comments

This is a manuscript of an article published as Nguyen, Yet, and Dan Nettleton. "rmRNAseq: Differential Expression Analysis for Repeated-measures RNA-seq Data." Bioinformatics (2020). doi: 10.1093/bioinformatics/btaa525. Posted with permission.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Available for download on Tuesday, May 25, 2021

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