Title: Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers Show Chinese title

Title: 基于稀疏框架乘子的MALDI质谱成像数据峰值检测

Abstract: 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. Show Chinese abstract

Abstract: 基质辅助激光解吸电离质谱成像（MALDI MSI）是一种用于生物组织分析的空间分辨分析工具，通过测量离子化分子的质荷比来实现。随着MALDI MSI数据的空间分辨率和质量分辨率的提高，适当的数据分析和解释变得越来越具有挑战性。从噪声中可靠地分离出重要的峰（即峰检测）是许多后续处理步骤的前提，并且应尽可能准确。我们提出了一种基于稀疏框架乘子的新峰检测算法，该算法可以应用于原始MALDI MSI数据而无需预先处理。该算法的准确性通过与现有算法相比的模拟数据集进行了评估。这些结果还显示了所提出方法对基线和噪声效应的鲁棒性。此外，该方法在两个真实的MALDI-TOF数据集上进行了评估，在这个过程中可以包括空间信息。