A mixture model for peak filtering and adduct identification for mass spectrometry metabolomics data

Abstract: Metabolite identification from high throughput mass spectrometry (MS) data is highly challenging.  Current approaches use a labour intensive pipeline requiring several arbitrary thresholding steps and expert intervention.  I will present a mixture model that brings together three forms of data available from the MS output and is able to automatically group related adducts and their isotope spectra.

Bio: Simon Rogers is a Lecturer in the School of Computing Science at University of Glasgow.