General statistical framework for quantitative proteomics by stable isotope labeling

Navarro, Pedro, Trevisan-Herraz, Marco, Bonzon-Kulichenko, Elena, Nunez, Estefnía, Martinez-Acedo, Pablo, Perez-Hernandez, Daniel, Jorge, Immaculada, Mesa, Raquel, Calvo, Enrique, Carrascal, Montsarrat, Hernaez, Maria Luisa, Garcia, Fernando, Barcena, Jose Antonio, Ashman, Keith, Abian, Joaquin, Gil, Concha, Redondo, Juan Miguel and Vazquez, Jesus (2014) General statistical framework for quantitative proteomics by stable isotope labeling. Journal of Proteome Research, 13 3: 1234-1247. doi:10.1021/pr4006958

Author Navarro, Pedro
Trevisan-Herraz, Marco
Bonzon-Kulichenko, Elena
Nunez, Estefnía
Martinez-Acedo, Pablo
Perez-Hernandez, Daniel
Jorge, Immaculada
Mesa, Raquel
Calvo, Enrique
Carrascal, Montsarrat
Hernaez, Maria Luisa
Garcia, Fernando
Barcena, Jose Antonio
Ashman, Keith
Abian, Joaquin
Gil, Concha
Redondo, Juan Miguel
Vazquez, Jesus
Title General statistical framework for quantitative proteomics by stable isotope labeling
Journal name Journal of Proteome Research   Check publisher's open access policy
ISSN 1535-3893
Publication date 2014-03-07
Year available 2014
Sub-type Article (original research)
DOI 10.1021/pr4006958
Open Access Status
Volume 13
Issue 3
Start page 1234
End page 1247
Total pages 14
Place of publication Washington, DC, United States
Publisher American Chemical Society
Collection year 2015
Language eng
Subject 1303 Specialist Studies in Education
1600 Chemistry
Abstract The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.
Keyword Quantitative proteomics
Stable isotope labeling
statistical analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2015 Collection
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
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