Reference : RepExplore: Addressing technical replicate variance in proteomics and metabolomics da...

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Biotechnology Life sciences : Multidisciplinary, general & others Human health sciences : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/20114

Title :	RepExplore: Addressing technical replicate variance in proteomics and metabolomics data analysis
Language :	English
Author, co-author :	Glaab, Enrico [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Schneider, Reinhard [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Publication date :	2015
Journal title :	Bioinformatics
Volume :	31
Issue/season :	13
Pages :	2235
Peer reviewed :	Yes
Audience :	International
Keywords :	[en] technical replicates ; proteomics ; metabolomics ; differential analysis ; PCA ; visualization ; statistical analysis ; web-application ; mass spectrometry
Abstract :	[en] High-throughput omics datasets often contain technical replicates, included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses. We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ran- king tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/20114
DOI :	10.1093/bioinformatics/btv127
Other URL :	http://bioinformatics.oxfordjournals.org/content/31/13/2235.long

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