A Multi-feature Reproducibility Assessment of Mass Spectral Data in Clinical Proteomic Studies

Abstract

Background: The use of mass spectrometry to investigate disease-associated proteins among thousands of candidates simultaneously creates challenges with the evaluation of operational and biological variation. Traditional statistical methods, which evaluate reproducibility of a single feature, are likely to provide an inadequate assessment of reproducibility. This paper proposes a systematic approach for the evaluation of the global reproducibility of multidimensional mass spectral data at the post-identification stage. Methods: The proposed systematic approach combines dimensional reduction and permutation to test and summarize the reproducibility. First, principal component analysis is applied to the mean quantities from identified features of paired replicated samples. An eigenvalue test is used to identify the number of significant principal components which reflect the underlying correlation pattern of the multiple features. Second, a simulation-based permutation test is applied to the derived paired principal components. Third, a modified form of Bland Altman or MA plot is produced to visualize agreement between the replicates. Last, a discordance index is used to summarize the agreement. Results: Application of this method to data from both a cardiac liquid chromatography tandem mass spectrometry experiment with iTRAQ labeling and simulation experiments derived from an ovarian cancer SELDI-MS experiment demonstrate that the proposed global reproducibility test is sensitive to the simulated systematic bias when the sample size is above 15. The two proposed test statistics (max t statistics and a sign score statistic) for the permutation tests are shown to be reliable. Conclusion: The methodology presented in this paper provides a systematic approach for the global measurement of reproducibility in clinical proteomic studies. © 2009 Springer Science+Business Media, LLC.