title: NanoStringDiff: a novel statistical method for differential expression analysis based on NanoString nCounter data.
abstract: The advanced medium-throughput NanoString nCounter technology has been increasingly used for mRNA or miRNA differential expression (DE) studies due to its advantages including direct measurement of molecule expression levels without amplification, digital readout and superior applicability to formalin fixed paraffin embedded samples. However, the analysis of nCounter data is hampered because most methods developed are based on t-tests, which do not fit the count data generated by the NanoString nCounter system. Furthermore, data normalization procedures of current methods are either not suitable for counts or not specific for NanoString nCounter data. We develop a novel DE detection method based on NanoString nCounter data. The method, named NanoStringDiff, considers a generalized linear model of the negative binomial family to characterize count data and allows for multifactor design. Data normalization is incorporated in the model framework through data normalization parameters, which are estimated from positive controls, negative controls and housekeeping genes embedded in the nCounter system. We propose an empirical Bayes shrinkage approach to estimate the dispersion parameter in the model and a likelihood ratio test to identify differentially expressed genes. Simulations and real data analysis demonstrate that the proposed method performs better than existing methods.
authors.affiliation: Department of Statistics, University of Kentucky, Lexington, KY 40536, USA.
authors.forename: Hong
authors.initials: H
authors.lastname: Wang
authors.affiliation: Departments of Pathology and Neurosurgery, Northwestern University, Chicago, IL 60611, USA.
authors.forename: Craig
authors.initials: C
authors.lastname: Horbinski
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
authors.affiliation: Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY 40536 USA.
authors.forename: Yinxing
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Paul Laurence Dunbar High School, Lexington, KY 40513, USA.
authors.forename: Shaoyi
authors.initials: S
authors.lastname: Sheng
authors.affiliation: Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.
authors.forename: Jinpeng
authors.initials: J
authors.lastname: Liu
authors.affiliation: Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.|Department of Biostatistics, University of Kentucky, Lexington, KY 40536, USA.
authors.forename: Heidi
authors.initials: H
authors.lastname: Weiss
authors.affiliation: Department of Statistics, University of Kentucky, Lexington, KY 40536, USA.
authors.forename: Arnold J
authors.initials: AJ
authors.lastname: Stromberg
authors.affiliation: Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA chi.wang@uky.edu.|Department of Biostatistics, University of Kentucky, Lexington, KY 40536, USA.
authors.forename: Chi
authors.initials: C
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D035683:MicroRNAs / Q000235:genetics*; D015233:Models, Statistical; D012333:RNA, Messenger / Q000235:genetics*; D015203:Reproducibility of Results
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger