Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics

Adam Dowle, Julie Carol Wilson, Jerry R. Thomas

Research output: Contribution to journalArticlepeer-review


Diagnostic classification accuracy is critical in expression proteomics to ensure that as many true differences as possible are identified with acceptable false-positive rates. We present a comparison of the diagnostic accuracy of iTRAQ with three label-free methods, peak area, spectral counting, and emPAI, for relative quantification using a spiked proteome standard. We provide the first validation of emPAI for intersample relative quantification and find clear differences among the four quantification approaches that could be considered when designing an experiment. Spectral counting was observed to perform surprisingly well in all regards. Peak area performed best for smaller fold differences and was shown to be capable of discerning a 1.1-fold difference with acceptable specificity and sensitivity. The performance of iTRAQ was dramatically worse than the label-free methods with low abundance proteins. Using the iTRAQ data set for validation, we also demonstrate a novel iTRAQ analysis regime that avoids the use of ratios in significance testing and outperforms a common commercial alternative.
Original languageEnglish
Pages (from-to)3550-3562
Number of pages13
JournalJournal of Proteome Research
Issue number10
Early online date22 Aug 2016
Publication statusPublished - 7 Oct 2016

Bibliographical note

© 2016 American Chemical Society. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.
Embargo period: 12 months

Cite this