title: General statistical framework for quantitative proteomics by stable isotope labeling.
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.
authors: Centro de Biología Molecular Severo Ochoa, CSIC-UAM , 28049 Madrid, Spain.
authors: Pedro
authors: P
authors: Navarro
authors: Marco
authors: M
authors: Trevisan-Herraz
authors: Elena
authors: E
authors: Bonzon-Kulichenko
authors: Estefanía
authors: E
authors: Núñez
authors: Pablo
authors: P
authors: Martínez-Acedo
authors: Daniel
authors: D
authors: Pérez-Hernández
authors: Inmaculada
authors: I
authors: Jorge
authors: Raquel
authors: R
authors: Mesa
authors: Enrique
authors: E
authors: Calvo
authors: Montserrat
authors: M
authors: Carrascal
authors: María Luisa
authors: ML
authors: Hernáez
authors: Fernando
authors: F
authors: García
authors: José Antonio
authors: JA
authors: Bárcena
authors: Keith
authors: K
authors: Ashman
authors: Joaquín
authors: J
authors: Abian
authors: Concha
authors: C
authors: Gil
authors: Juan Miguel
authors: JM
authors: Redondo
authors: Jesús
authors: J
authors: Vázquez
mesh_terms: D057225:Data Mining; D015870:Gene Expression / Q000187:drug effects; D006861:Hydrogen Peroxide / Q000494:pharmacology; D007553:Isotope Labeling; D015233:Models, Statistical; D058977:Molecular Sequence Annotation; D010103:Oxygen Isotopes; D020543:Proteome / Q000032:analysis* / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000187:drug effects / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis* / Q000235:genetics / Q000378:metabolism
chemical_list: D010103:Oxygen Isotopes; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins; D006861:Hydrogen Peroxide