General statistical framework for quantitative proteomics by stable isotope labeling

Pedro Navarro, Marco Trevisan-Herraz, Elena Bonzon-Kulichenko, Estefanía Núñez, Pablo Martínez-Acedo, Daniel Pérez-Hernández, Inmaculada Jorge, Raquel Mesa, Enrique Calvo, Montserrat Carrascal, María Luisa Hernáez, Fernando García, José Antonio Bárcena, Keith Ashman, Joaquín Abian, Concha Gil, Juan Miguel Redondo, Jesús Vázquez*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

99 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1234-1247
Number of pages14
JournalJournal of Proteome Research
Volume13
Issue number3
DOIs
Publication statusPublished - 7 Mar 2014
Externally publishedYes

Keywords

  • Quantitative proteomics
  • stable isotope labeling
  • statistical analysis
  • yeast

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