Evaluation of Bias-Variance Trade-Off for Commonly Used Post-Summarizing Normalization Procedures in Large-Scale Gene Expression Studies

• Published in: PloS one Vol. 9; no. 6; p. e99380
• Main Authors: Qiu, Xing, Hu, Rui, Wu, Zhixin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.06.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Arrays; Bias; Bioinformatics; Biological effects; Biology and Life Sciences; Computer simulation; Data analysis; Data Interpretation, Statistical; Data processing; Gene expression; Gene Expression Profiling - methods; Genomics; Noise; Oligonucleotide Array Sequence Analysis; Performance evaluation; Physical Sciences; Random variables; Research and Analysis Methods; Sample Size; Statistical analysis; Statistical methods; Statistical tests; Studies; Tradeoffs; Transcriptome; Variance
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0099380

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.