Document Type
Article
Publication Date
6-2014
Abstract
Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with strong gene differentiation effects and large sample size. In this paper, we conduct a thorough study to evaluate the effects of normalization procedures combined with several commonly used statistical tests and MTPs under different configurations of effect size and sample size. We conduct theoretical evaluation based on a random effect model, as well as simulation and biological data analyses to verify the results. Based on our findings, we provide some practical guidance for selecting a suitable normalization procedure under different scenarios.
Recommended Citation
Qiu X, Hu R, Wu Z (2014) Evaluation of Bias-Variance Trade-Off for Commonly Used Post-Summarizing Normalization Procedures in Large-Scale Gene Expression Studies. PLoS ONE 9(6): e99380. doi:10.1371/journal.pone.0099380
Comments
Copyright: 2014 Qiu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research is supported by the University of Rochester CTSI pilot award(UL1RR024160) from the National Center For Research Resources, Award Number UL1TR000042 from the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH), and HHSN272201000055C/N01-AI50020 and 5 R01 AI087135-02 from NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.