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, researchers from the University of Pennsylvania 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.
Unwanted variation dominates the signal in RNA-seq studies in experimental neuroscience. PCA plots of gene counts normalized using either upper-quantile (UQ) or FPKM from publicly available datasets from the mouse hippocampus.
The researchers show that RUVSeq is able to increase power and biological insight of the results and 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.
A step-by-step guide to implement normalization of RNA-seq using RUVSeq
Peixoto L, Risso D, Poplawski SG, Wimmer ME, Speed TP, Wood MA, Abel T. (2015) How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets. Nucleic Acids Res [Epub ahead of print]. [article]