Tian Xiang, Zou Zhu, Yang Zhibo
Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA.
Dynamic Omics, Center of Genomics Research (CGR), R&D, AstraZeneca, Gaithersburg, MD, USA.
Methods Mol Biol. 2022;2437:253-272. doi: 10.1007/978-1-0716-2030-4_18.
Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.
质谱成像(MSI)数据由于其固有的复杂化学和空间信息,通常包含大尺寸和高维结构。已经开发了多种数据分析方法来全面分析MSI实验结果并提取关键信息。在这里,我们描述了数据预处理协议以及新兴的数据分析方法,包括多变量分析、机器学习和图像融合,这些方法已应用于单探针MSI技术生成的数据。这些策略和方法可能适用于处理其他MSI技术产生的数据。