How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets
Details
Publication Year 2015-07-21, Volume 43, Issue #16, Page 7664-7674
Journal Title
Nucleic Acids Res
Publication Type
Journal Article
Abstract
The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or 'batch effects' can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic when it is influenced simultaneously by a variety of biological factors in addition to the one of interest. Using examples from experimental neuroscience, we show that batch effects can dominate the signal of interest; and that the choice of normalization method affects the power and reproducibility of the results. While commonly used global normalization methods are not able to adequately normalize the data, more recently developed RNA-seq normalization can. We focus on one particular method, RUVSeq and show that it is able to increase power and biological insight of the results. Finally, we provide a tutorial outlining the implementation of RUVSeq normalization that is applicable to a broad range of studies as well as meta-analysis of publicly available data.
Publisher
OUP
WEHI Research Division(s)
Bioinformatics
PubMed ID
26202970
Rights Notice
Refer to copyright notice on published article.


Creation Date: 2015-07-28 12:00:08
Last Modified: 2015-09-22 04:12:41
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