Simon Rogers, Rónán Daly and Rainer Breitling.
In Proceedings of the Ninth International Workshop on Computational Systems Biology, pages 71–74, 2012
In recent years, the use of liquid chromatography coupled to mass spectrometry has enabled the high-throughput profiling of the metabolic composition of biological samples. However, the large amount of data obtained is often difficult to analyse. This paper focuses on a particular problem, that of detecting and potentially removing derivative peaks of a substance of interest. A mixture model for clustering peaks based on chromatographic peak shape correlation is presented, and comparison of this model to the behaviour of a leading mass spectrometry analysis tool is presented. Based on the results, the mixture model is shown to have better overall performance characteristics.