A comparative study of techniques for differential expression analysis on RNA-seq data

Zhang, Zong Hong, Jhaveri, Dhanisha J., Marshall, Vikki M., Bauer, Denis C., Edson, Janette, Narayanan, Ramesh K., Robinson, Gregory J., Lundberg, Andreas E., Bartlett, Perry F., Wray, Naomi R. and Zhao, Qiong-Yi (2014) A comparative study of techniques for differential expression analysis on RNA-seq data. PLoS One, 9 8: 1-11. doi:10.1371/journal.pone.0103207

Author Zhang, Zong Hong
Jhaveri, Dhanisha J.
Marshall, Vikki M.
Bauer, Denis C.
Edson, Janette
Narayanan, Ramesh K.
Robinson, Gregory J.
Lundberg, Andreas E.
Bartlett, Perry F.
Wray, Naomi R.
Zhao, Qiong-Yi
Title A comparative study of techniques for differential expression analysis on RNA-seq data
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2014-08-13
Year available 2014
Sub-type Article (original research)
DOI 10.1371/journal.pone.0103207
Open Access Status DOI
Volume 9
Issue 8
Start page 1
End page 11
Total pages 11
Place of publication San Francisco, United States
Publisher Public Library of Science
Collection year 2015
Language eng
Abstract Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article # 03207

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
UQ Diamantina Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
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