Using a spike-in experiment to evaluate analysis of LC-MS data

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to newer approaches for measuring changes in peptide/protein abundances. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization, and transformation. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. In this paper, we use a spike-in experiment to evaluate the performance of three software tools in accurately detecting changes in peptide abundances from LC-MS data obtained by a label-free LC-MS method. We observe that tools that incorporate peptide isotope cluster and multiple charge information lead to more accurate difference detection with fewer false positives.