title: A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data.
abstract: 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.
authors.affiliation: College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: College of Economics, Jinan University, Guangzhou, China.
authors.forename: Guochang
authors.initials: G
authors.lastname: Wang
authors.affiliation: College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Economics, Shenzhen University, Shenzhen, China.
authors.forename: Han
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D053595:Embryonic Stem Cells; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D020043:Genes, Essential; D059014:High-Throughput Nucleotide Sequencing; D007668:Kidney / Q000378:metabolism; D008099:Liver / Q000378:metabolism