Relative quantification: Characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides

Douglas W. Mahoney, Terry M. Therneau, Carrie J. Heppelmann, Leeann Higgins, Linda M. Benson, Roman M. Zenka, Pratik Jagtap, Gary L. Nelsestuen, H. Robert Bergen, Ann L. Oberg

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of proteinmass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.

Original languageEnglish (US)
Pages (from-to)4325-4333
Number of pages9
JournalJournal of Proteome Research
Volume10
Issue number9
DOIs
StatePublished - Sep 2 2011

Keywords

  • bias
  • biomarker discovery
  • iTRAQ
  • mass spectrometry
  • relative quantification
  • shotgun proteomics
  • spike-in
  • variance
  • weighted least-squares

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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