Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions.

ABSTRACT: RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An underemphasized feature of normalization is the assumptions on which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this article, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspe

SUBMITTER: Evans C 

PROVIDER: S-EPMC6171491 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature