TY - JOUR A1 - Lieb, Florian A1 - Boskamp, Tobias A1 - Stark, Hans-Georg T1 - Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers JF - Journal of Proteomics N2 - MALDI mass spectrometry imaging (MALDI MSI) is a spatially resolved analytical tool for biological tissue analysis by measuring mass-to-charge ratios of ionized molecules. With increasing spatial and mass resolution of MALDI MSI data, appropriate data analysis and interpretation is getting more and more challenging. A reliable separation of important peaks from noise (aka peak detection) is a prerequisite for many subsequent processing steps and should be as accurate as possible. We propose a novel peak detection algorithm based on sparse frame multipliers, which can be applied to raw MALDI MSI data without prior preprocessing. The accuracy is evaluated on a simulated data set in comparison with state-of-the-art algorithms. These results also show the proposed method's robustness to baseline and noise effects. In addition, the method is evaluated on real MALDI-TOF data sets, whereby spatial information can be included in the peak picking process. Significance: The field of proteomics, in particular MALDI Imaging, encompasses huge amounts of data. The processing and preprocessing of this data in order to segment or classify spatial structures of certain peptides or isotope patterns can hence be cumbersome and includes several independent processing steps. In this work, we propose a simple peak-picking algorithm to quickly analyze large raw MALDI Imaging data sets, which has a better sensitivity than current state-of-the-art algorithms. Further, it is possible to get an overall overview of the entire data set showing the most significant and spatially localized peptide structures and, hence, contributes all data driven evaluation of MALDI Imaging data. KW - MALDI imaging, Peak picking, Frame multiplier KW - MALDI-MS KW - Massenspektrometrie Y1 - 2020 UR - https://doi.org/10.1016/j.jprot.2020.103852 U6 - https://doi.org/https://doi.org/10.1016/j.jprot.2020.103852 VL - 2020 IS - 225 SP - 103852 EP - 103860 ER -