Open AccessA Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data
Author(s) -
Yan Zhou,
Guochang Wang,
Jun Zhang,
Han Li
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0169594
Subject(s) - normalization (sociology) , rna seq , housekeeping gene , computational biology , database normalization , computer science , biology , gene expression , data mining , gene , genetics , artificial intelligence , pattern recognition (psychology) , transcriptome , sociology , anthropology
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.