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General statistical framework for quantitative proteomics by stable isotope labeling.

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Navarro, Pedro and Trevisan Herraz, Marco and Bonzon Kulichenko, Elena and Núñez, Estefanía and Martínez Acedo, Pablo and Pérez Hernández, Daniel and Jorge, Inmaculada and Mesa, Raquel and Calvo, Enrique and Carrascal, Montserrat and Hernáez, María Luisa and García, Fernando and Bárcena, José Antonio and Ashman, Keith and Abian, J. and Gil, Concha and Redondo, Juan Miguel and Vázquez, Jesús (2014) General statistical framework for quantitative proteomics by stable isotope labeling. Journal of proteome research, 13 (3). pp. 1234-47. ISSN 1535-3907

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Official URL: http://dx.doi.org/10.1021/pr4006958



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 (18)O, 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.


Item Type:Article
Uncontrolled Keywords:Quantitative proteomics, stable isotope labeling, statistical analysis, yeast
Subjects:Medical sciences > Pharmacy > Microbiology
ID Code:33630
Deposited On:26 Nov 2015 13:14
Last Modified:29 Nov 2015 15:28

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