RNA-seq differential expression studies: more sequence or more replication?

• Published in: Bioinformatics Vol. 30; no. 3; pp. 301 - 304
• Main Authors: Liu, Yuwen, Zhou, Jie, White, Kevin P
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-02-2014
• Subjects: Discovery Notes; Gene Expression Profiling - methods; High-Throughput Nucleotide Sequencing - methods; Humans; MCF-7 Cells; Reproducibility of Results; Sample Size; Sequence Analysis, RNA - methods
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btt688

RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources.
We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF7, adding more sequencing depth after 10 M reads gives diminishing returns on power to detect DE genes, whereas adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large-scale RNA-seq DE studies. Our analysis showed that sequencing less reads and performing more biological replication is an effective strategy to increase power and accuracy in large-scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies.
The code used in this paper is provided on: http://home.uchicago.edu/∼jiezhou/replication/. The expression data is deposited in the Gene Expression Omnibus under the accession ID GSE51403.