PNNL

Abstract

In the analysis of differential peptide peak intensities (i.e., abundance measures), LC-MS analyses with poor quality pep-tide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high quality data set. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses (e.g., ANOVA) rely upon accurate estimates of sample variance, and their results are influenced by extreme values. Results: We describe a multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates the significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest this strategy significantly outperforms correlation alone in the identification of statistically extreme LC-MS runs.