TY - JOUR
T1 - General statistical framework for quantitative proteomics by stable isotope labeling
AU - Navarro, Pedro
AU - Trevisan-Herraz, Marco
AU - Bonzon-Kulichenko, Elena
AU - Núñez, Estefanía
AU - Martínez-Acedo, Pablo
AU - Pérez-Hernández, Daniel
AU - Jorge, Inmaculada
AU - Mesa, Raquel
AU - Calvo, Enrique
AU - Carrascal, Montserrat
AU - Hernáez, María Luisa
AU - García, Fernando
AU - Bárcena, José Antonio
AU - Ashman, Keith
AU - Abian, Joaquín
AU - Gil, Concha
AU - Redondo, Juan Miguel
AU - Vázquez, Jesús
PY - 2014/3/7
Y1 - 2014/3/7
N2 - 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.
AB - 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.
KW - Quantitative proteomics
KW - stable isotope labeling
KW - statistical analysis
KW - yeast
UR - http://www.scopus.com/inward/record.url?scp=84896767479&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/24512137
U2 - 10.1021/pr4006958
DO - 10.1021/pr4006958
M3 - Article
C2 - 24512137
AN - SCOPUS:84896767479
SN - 1535-3893
VL - 13
SP - 1234
EP - 1247
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 3
ER -