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MALDI mass spectrometry imaging (MALDI MSI) is a spatially resolvedanalytical tool for biological tissue analysis by measuring mass-to-chargeratios of ionized molecules. With increasing spatial and mass resolution ofMALDI MSI data, appropriate data analysis and interpretation is getting moreand more challenging. A reliable separation of important peaks from noise (akapeak detection) is a prerequisite for many subsequent processing steps andshould be as accurate as possible. We propose a novel peak detection algorithmbased on sparse frame multipliers, which can be applied to raw MALDI MSI datawithout prior preprocessing. The accuracy is evaluated on a simulated data setin comparison with a state-of-the-art algorithm. These results also show theproposed method's robustness to baseline and noise effects. In addition, themethod is evaluated on two real MALDI-TOF data sets, whereby spatialinformation can be included in the peak picking process.

Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers