Imaging mass spectrometry statistical analysis

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings.Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC–MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research.This article is part of a Special Issue entitled: Imaging Mass Spectrometry: A User’s Guide to a New Technique for Biological and Biomedical Research.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (287 K)Download as PowerPoint slideHighlights► Description of nature of imaging MS data sets ► Overview of data reduction methods for efficiently handling the large data sets ► Review of imaging MS data analysis strategies ► Summary of multivariate algorithms used for imaging MS based molecular histology ► Tutorial about statistical methods for imaging MS based biomarker discovery