Visualisation in imaging mass spectrometry using the minimum noise fraction transform

Abstract

Background: Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample. IMS datasets are typically huge and visualisation and subsequent analysis can be challenging. Principal component analysis (PCA) is one popular data reduction technique that has been used and we propose another; the minimum noise fraction (MNF) transform which is popular in remote sensing. Findings: The MNF transform is able to extract spatially coherent information from IMS data. The MNF transform is implemented through an R-package which is available together with example data from http://staff.scm.uws.edu.au/∼glenn/#Software. Conclusions: In our example, the MNF transform was able to find additional images of interest. The extracted information forms a useful basis for subsequent analyses.