Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets

Mass Spectrometry Imaging allows for the study of the distribution of chemical compounds in tissue sections, opening-up possibilities of much more detailed understanding of biological processes. The size and complexity of the datasets, however, makes the data analysis phase challenging, in particular in the case of untargeted datasets. In this contribution we present a novel approach based on self-organizing maps for the analysis of untargeted imaging datasets. The key idea is to generate a shortlist of prototype images which represent the distribution of related ionic signals. The possibilities and advantages of the new approach are illustrated on an in-house data set of apple slices