title: Evaluation of Normalization Methods for RNA-Seq Gene Expression Estimation.
abstract: Statistical inferences on RNA-Seq data, e.g., detecting differential gene expression, are meaningful only after proper normalization. However, there is no consensus for choosing a normalization procedure from among the many existing procedures. We evaluated several RNA-Seq normalization procedures by (1) correlating estimated RNA-Seq expression values to those of microarrays, (2) examining the concordance of stable and differential gene detection between the platforms, and (3) applying the procedures to simulated RNA-Seq data. Results suggested that RNA-Seq normalization procedures have little effect on both inter-platform gene expression correlation as well as inter-platform concordance of genes detected as stably or differentially expressed. However, the results of simulated analysis suggested that some normalization procedures are more robust to changes in distribution of differentially expressed genes. These results may provide guidance for selecting RNA-Seq normalization procedures.
authors.affiliation: Department of Electrical and Computer Engineering, Georgia Institute of Technology.
authors.forename: Po-Yen
authors.initials: PY
authors.lastname: Wu
authors.affiliation: The Wallace H. Coulter Biomedical Engineering Department, Georgia Institute of Technology and Emory University.
authors.forename: John H
authors.initials: JH
authors.lastname: Phan
authors.affiliation: Research Center for Biomedical Information Technology, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
authors.forename: Fengfeng
authors.initials: F
authors.lastname: Zhou
authors.affiliation: The Wallace H. Coulter Biomedical Engineering Department, Georgia Institute of Technology and Emory University.
authors.forename: May D
authors.initials: MD
authors.lastname: Wang