A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data

• Published in: PloS one Vol. 12; no. 1; p. e0169594
• Main Authors: Zhou, Yan, Wang, Guochang, Zhang, Jun, Li, Han
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.01.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Binomial distribution; Bioinformatics; Biology; Biology and Life Sciences; Computational Biology - methods; Computer and Information Sciences; Data analysis; Data processing; Differential equations; Embryonic Stem Cells; Empirical analysis; Expected values; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation; Gene sequencing; Genes; Genes, Essential; Genomes; High-Throughput Nucleotide Sequencing; Hypotheses; Kidney - metabolism; Liver - metabolism; Medicine and Health Sciences; Methods; Noise measurement; Noise reduction; Research and analysis methods; Ribonucleic acid; RNA; RNA sequencing; Scaling; Scaling factors; Test procedures; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0169594

Next-generation sequencing technologies have made RNA sequencing (RNA-seq) a popular choice for measuring gene expression level. To reduce the noise of gene expression measures and compare them between several conditions or samples, normalization is an essential step to adjust for varying sample sequencing depths and other unwanted technical effects. In this paper, we develop a novel global scaling normalization method by employing the available knowledge of housekeeping genes. We formulate the problem from the hypothesis testing perspective and find an optimal scaling factor that minimizes the deviation between the empirical and the nominal type I error. Applying our approach to various simulation studies and real examples, we demonstrate that it is more accurate and robust than the state-of-the-art alternatives in detecting differentially expression genes.