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

arXiv: https://arxiv.org/abs/1911.00491 · HTML: https://mlsanity.com/paper/1911.00491/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging](https://mlsanity.com/paper/2603.10487/) (2026)
- [Supervised topological data analysis for MALDI mass spectrometry imaging applications](https://mlsanity.com/paper/2302.13948/) (2023)
- [IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging](https://mlsanity.com/paper/2604.19369/) (2026)

## Abstract (arXiv)

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 a state-of-the-art algorithm. These results also show the proposed method's robustness to baseline and noise effects. In addition, the method is evaluated on two real MALDI-TOF data sets, whereby spatial information can be included in the peak picking process.