Variability Assessment of Label-Free LC-MS Experiments for Difference Detection

Abstract

In the analysis of data acquired from label-free experiments by liquid chromatography coupled with mass spectrometry (LC-MS), accounting for potential sources of variability can improve the detection of true differences in ion abundance. Mixed effects models are commonly used to estimate variabilities due to heterogeneity of the biological specimen, differences in sample preparation, and instrument variation. In this chapter, we investigate the mixed effects models and evaluate their performance in difference detection, in comparison to other methods such as marginal t-test, which uses the average over analytical and technical replicates within each biological sample for statistical analysis. Experimental design including replication assignment and sample size calculation is discussed. These are highly dependent on the variation contributed by the different sources, which can be estimated from LC-MS pilot studies prior to running large-scale label-free experiments.