Discrete wavelet transform-based multivariate exploration of tissue via imaging mass spectrometry

Mass spectral imaging (MSI) or imaging mass spectrometry is a developing technology that combines spatial information with traditional mass spectrometry. It enables researchers to study the spatial distribution of biomolecules such as proteins, peptides, and metabolites throughout organic tissue sections. MSI has particular merit in exploratory settings where there is no prior hypothesis of relevant target molecules. It is rapidly becoming a potent exploratory instrument for tissue biomarker studies.

MSI is a high-throughput technique that mines massive amounts of measurements from a single tissue section. As various parameters such as the covered tissue surface area, the spatial resolution, and the extent of the mass range grow, MSI data sets rapidly become very large, making analysis from a computational and memory standpoint increasingly difficult. In this paper we introduce the discrete wavelet transform (DWT) as a means of reducing the dimensionality of the data, while retaining a maximum amount of biochemical information. The DWT delivers a more compact description of each mass spectrum, expressed as wavelet coefficients. The efficacy of performing analyses directly in the DWT-reduced space is illustrated using unsupervised trend detection via principal component analysis (PCA) on the MSI measurement of a sagittal section of mouse brain.