CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization.
In quantitative proteomics applications, the use of isobaric labels is a very popular concept as they allow for multiplexing, such that peptides from multiple biological samples are quantified simultaneously in one mass spectrometry experiment. Although this multiplexing allows that peptide intensities are affected by the same amount of instrument variability, systematic effects during sample preparation can also introduce a bias in the quantitation measurements. Therefore, normalization methods are required to remove this systematic error. At present, a few dedicated normalization methods for isobaric labeled data are at hand. Most of these normalization methods include a framework for statistical data analysis and rely on ANOVA or linear mixed models. However, for swift quality control of the samples or data visualization a simple normalization technique is sufficient. To this aim, we present a new and easy-to-use data-driven normalization method, named CONSTANd. The CONSTANd method employs constrained optimization and prior information about the labeling strategy to normalize the peptide intensities. Further, it allows maintaining the connection to any biological effect while reducing the systematic and technical errors. As a result, peptides can not only be compared directly within a multiplexed experiment, but are also comparable between other isobaric labeled datasets from multiple experimental designs that are normalized by the CONSTANd method, without the need to include a reference sample in every experimental setup. The latter property is especially useful when more than six, eight or ten (TMT/iTRAQ) biological samples are required to detect differential peptides with sufficient statistical power and to optimally make use of the multiplexing capacity of isobaric labels.
From the: ‡Applied Bio & molecular Systems, VITO, Boeretang 200, 2400 Mol, Belgium; §Center for Proteomics, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium;
Evelyne
E
Maes
From the: ‡Applied Bio & molecular Systems, VITO, Boeretang 200, 2400 Mol, Belgium;
Wahyu Wijaya
WW
Hadiwikarta
From the: ‡Applied Bio & molecular Systems, VITO, Boeretang 200, 2400 Mol, Belgium; §Center for Proteomics, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium;
Inge
I
Mertens
From the: ‡Applied Bio & molecular Systems, VITO, Boeretang 200, 2400 Mol, Belgium; §Center for Proteomics, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium;
Geert
G
Baggerman
From the: ‡Applied Bio & molecular Systems, VITO, Boeretang 200, 2400 Mol, Belgium; ¶Theoretical Physics, Hasselt University, Agoralaan 1, 3590 Diepenbeek, Belgium;
Jef
J
Hooyberghs
From the: ‡Applied Bio & molecular Systems, VITO, Boeretang 200, 2400 Mol, Belgium; §Center for Proteomics, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium; ‖Center for Statistics, Hasselt University, Agoralaan 1, 3590 Diepenbeek, Belgium dirk.valkenborg@vito.be.
Dirk
D
Valkenborg
D000465:Algorithms; D003627:Data Interpretation, Statistical; D010446:Peptide Fragments / Q000737:chemistry*; D040901:Proteomics / Q000379:methods / Q000592:standards*; D013194:Staining and Labeling / Q000379:methods*; D053719:Tandem Mass Spectrometry / Q000379:methods
D010446:Peptide Fragments