Compression strategies for the chemometric analysis of mass spectrometry imaging data

Application of chemometric methods to mass spectrometry imaging (MSI) data faces a bottleneck concerning the vast size of the experimental data sets. This drawback is critical when considering high-resolution mass spectrometry data, which provide several thousand points for each considered pixel. In this work, different approaches have been tested to reduce the size of the analyzed data with the aim to allow the subsequent application of typical chemometric methods for image analysis. The standard approach for MSI data compression consists in binning mass spectra for each pixel to reduce the number of m/z values. In this work, a method is proposed to handle the huge size of MSI data based on the adaptation of a liquid chromatography-mass spectrometry data compression method by the detection of regions of interest. Results showed that both approaches achieved high compression rates, although the proposed regions of interest-based method attains this reduction requiring lower computational requirements and keeping utter spectral information. For instance, typical compression rate reached values higher than 90% without loss of information in images and spectra. Application of chemometric methods to high-resolution mass spectrometry images requires a preliminary compression of experimental data. An algorithm for the detection of relevant variables for the analysis of images obtained by mass spectrometry is presented. Evaluation of different approaches is performed considering achieved compression rate, mass spectrometry information loss, and computational resources needed.