Ghent University Academic Bibliography

Advanced

unifiedWMWqPCR: the unified Wilcoxon–Mann–Whitney test for analyzing RT-qPCR data in R

Jan De Neve UGent, Joris Meys UGent, Jean-Pierre Ottoy UGent, Lieven Clement UGent and Olivier Thas UGent (2014) BIOINFORMATICS. 30(17). p.2494-2495
abstract
Motivation: 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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
journal title
BIOINFORMATICS
Bioinformatics
volume
30
issue
17
pages
2494 - 2495
Web of Science type
Article
Web of Science id
000342912400055
JCR category
MATHEMATICAL & COMPUTATIONAL BIOLOGY
JCR impact factor
4.981 (2014)
JCR rank
3/57 (2014)
JCR quartile
1 (2014)
ISSN
1367-4803
DOI
10.1093/bioinformatics/btu313
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
4376165
handle
http://hdl.handle.net/1854/LU-4376165
date created
2014-05-03 13:33:25
date last changed
2016-12-19 15:44:56
@article{4376165,
  abstract     = {Motivation: 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.},
  author       = {De Neve, Jan and Meys, Joris and Ottoy, Jean-Pierre and Clement, Lieven and Thas, Olivier},
  issn         = {1367-4803},
  journal      = {BIOINFORMATICS},
  language     = {eng},
  number       = {17},
  pages        = {2494--2495},
  title        = {unifiedWMWqPCR: the unified Wilcoxon--Mann--Whitney test for analyzing RT-qPCR data in R},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btu313},
  volume       = {30},
  year         = {2014},
}

Chicago
De Neve, Jan, Joris Meys, Jean-Pierre Ottoy, Lieven Clement, and Olivier Thas. 2014. “unifiedWMWqPCR: The Unified Wilcoxon–Mann–Whitney Test for Analyzing RT-qPCR Data in R.” Bioinformatics 30 (17): 2494–2495.
APA
De Neve, Jan, Meys, J., Ottoy, J.-P., Clement, L., & Thas, O. (2014). unifiedWMWqPCR: the unified Wilcoxon–Mann–Whitney test for analyzing RT-qPCR data in R. BIOINFORMATICS, 30(17), 2494–2495.
Vancouver
1.
De Neve J, Meys J, Ottoy J-P, Clement L, Thas O. unifiedWMWqPCR: the unified Wilcoxon–Mann–Whitney test for analyzing RT-qPCR data in R. BIOINFORMATICS. 2014;30(17):2494–5.
MLA
De Neve, Jan, Joris Meys, Jean-Pierre Ottoy, et al. “unifiedWMWqPCR: The Unified Wilcoxon–Mann–Whitney Test for Analyzing RT-qPCR Data in R.” BIOINFORMATICS 30.17 (2014): 2494–2495. Print.