Abstract
Meta-analysis of LC-MS data in metabolomic studies is desired to determine overlaps and differences among various experiments. For example, in large metabolic biomarker discovery studies involving hundreds of samples, it may be necessary to conduct multiple experiments, each involving a subset of the samples due to technical limitations. The ions selected from each experiment are analyzed to determine overlapping ions. One of the challenges in comparing the ion lists is the presence of a large number of derivative ions such as isotopes, adducts, and fragments. In this paper, we address this issue by grouping ions based on their annotation information. Following annotation, each ion in a cluster is represented by its monoisotopic mass. This mass is then used to compare the ions across multiple experiments. The resulting ion list provides better coverage and more accurate identification of metabolites compared to the traditional approach in which overlapping ions are selected on the basis of ion mass.
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