Mass spectrometry data are often generated from various biological or chemical experiments. However, due to technical reasons, outlying observations are often obtained, some of which may be extreme. Identifying the causes of outlying observations is important in the analysis of replicated MS data because elaborate pre-processing is essential in order to obtain successful analyses with reliable results, and because manual outlier detection is a time-consuming pre-processing step. It is natural to measure the variability of observations using standard deviation or interquartile range calculations, and in this work, these criteria for identifying outliers are presented. However, the low replicability and the heterogeneity of variability are often obstacles to outlier detection. Therefore, quantile regression methods for identifying outliers with low replication are also presented. The procedures are illustrated with artificial and real examples, while a software program is introduced to demonstrate how to apply these procedures in the R environment system.
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