Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22543
Title: CONSTANd : a normalization method for isobaric labeled spectra by constrained optimization
Authors: Maes, Evelyne
Hadiwikarta, Wahyu Wijaya
Mertens, Inge
Baggerman, Geert
HOOYBERGHS, Jef 
VALKENBORG, Dirk 
Issue Date: 2016
Publisher: AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
Source: MOLECULAR & CELLULAR PROTEOMICS, 15(8), p. 2779-2790
Abstract: 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.
Notes: [Maes, Evelyne; Hadiwikarta, Wahyu Wijaya; Mertens, Inge; Baggerman, Geert; Hooyberghs, Jef; Valkenborg, Dirk] VITO, Appl Bio & Mol Syst, Boeretang 200, B-2400 Mol, Belgium. [Maes, Evelyne; Mertens, Inge; Baggerman, Geert; Valkenborg, Dirk] Univ Antwerp, Ctr Prote, Groenenborgerlaan 171, B-2020 Antwerp, Belgium. [Hooyberghs, Jef] Hasselt Univ, Theoret Phys, Agoralaan 1, B-3590 Diepenbeek, Belgium. [Valkenborg, Dirk] Hasselt Univ, Ctr Stat, Agoralaan 1, B-3590 Diepenbeek, Belgium.
Document URI: http://hdl.handle.net/1942/22543
ISSN: 1535-9476
e-ISSN: 1535-9484
DOI: 10.1074/mcp.M115.056911
ISI #: 000380809100018
Rights: Copyright 2016 by The American Society for Biochemistry and Molecular Biology, Inc.
Category: A1
Type: Journal Contribution
Validations: ecoom 2017
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
A Normalization Method for Isobaric.pdf
  Restricted Access
Version in press2.07 MBAdobe PDFView/Open    Request a copy
Show full item record

SCOPUSTM   
Citations

10
checked on Sep 7, 2020

WEB OF SCIENCETM
Citations

22
checked on May 21, 2022

Page view(s)

54
checked on May 26, 2022

Download(s)

40
checked on May 26, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.