How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets.

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 o

SUBMITTER: Peixoto L 

PROVIDER: S-EPMC4652761 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature