2026-07-31T10:59:22Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/349182025-12-18T16:10:04Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Navarro, Pedro author Trevisan Herraz, Marco author Bonzon Kulichenko, Elena author Núñez, Estefanía author Martínez Acedo, Pablo author Pérez Hernández, Daniel author Jorge, Inmaculada author Mesa, Raquel author Calvo, Enrique author Carrascal, Montserrat author Hernáez, María Luisa author García, Fernando author Bárcena, José Antonio author Ashman, Keith author Abian, Joaquín author Gil, Concha author Redondo, Juan Miguel author Vázquez, Jesús 2023-06-19T14:56:58Z 2023-06-19T14:56:58Z 2014-01-31 1535-3907 10.1021/pr4006958 https://hdl.handle.net/20.500.14352/34918 http://dx.doi.org/10.1021/pr4006958 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. eng General statistical framework for quantitative proteomics by stable isotope labeling. journal article URL https://docta.ucm.es/bitstreams/8d909494-5f82-4bd1-ac39-9138301d4d23/download File MD5 9ee116f0ab00c5ceb3378396ce7bd1b5 975002 application/pdf Navarro et al. - 2014 - General Statistical Framework for Quantitative Pro.pdf URL https://docta.ucm.es/bitstreams/a54c4759-597f-453d-8003-647523c3bf5a/download File MD5 e1774ac7ebb17677ac433a6ea4add332 73715 text/plain Navarro et al. - 2014 - General Statistical Framework for Quantitative Pro.pdf.txt