General statistical modeling of data from protein relative expression isobaric tags.

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.