title: unifiedWMWqPCR: the unified Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data in R.
abstract: BACKGROUND
  Recently, De Neve et al. proposed a modification of the Wilcoxon-Mann-Whitney (WMW) test for assessing differential expression based on RT-qPCR data. Their test, referred to as the unified WMW (uWMW) test, incorporates a robust and intuitive normalization and quantifies the probability that the expression from one treatment group exceeds the expression from another treatment group. However, no software package for this test was available yet.
  
  
  RESULTS
  We have developed a Bioconductor package for analyzing RT-qPCR data with the uWMW test. The package also provides graphical tools for visualizing the effect sizes.
  
  
  METHODS
  The unifiedWMWqPCR package and its user documentation can be obtained through Bioconductor.
authors.affiliation: Department of Mathematical Modelling, Statistics and Bioinformatics, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium and National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia.
authors.forename: Jan
authors.initials: J
authors.lastname: De Neve
authors.affiliation: Department of Mathematical Modelling, Statistics and Bioinformatics, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium and National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia.
authors.forename: Joris
authors.initials: J
authors.lastname: Meys
authors.affiliation: Department of Mathematical Modelling, Statistics and Bioinformatics, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium and National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia.
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Ottoy
authors.affiliation: Department of Mathematical Modelling, Statistics and Bioinformatics, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium and National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia.
authors.forename: Lieven
authors.initials: L
authors.lastname: Clement
authors.affiliation: Department of Mathematical Modelling, Statistics and Bioinformatics, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium and National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia Department of Mathematical Modelling, Statistics and Bioinformatics, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Gent, Belgium and National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia.
authors.forename: Olivier
authors.initials: O
authors.lastname: Thas
mesh_terms: D006801:Humans; D035683:MicroRNAs / Q000032:analysis; D009447:Neuroblastoma / Q000235:genetics; D060888:Real-Time Polymerase Chain Reaction / Q000379:methods*; D012984:Software; D018709:Statistics, Nonparametric
chemical_list: D035683:MicroRNAs