The Use of Random Projections for the Analysis of Mass Spectrometry Imaging Data

• Published in: Journal of the American Society for Mass Spectrometry 2015-02, Vol.26 (2), p.315-322
• Main Authors: Palmer, Andrew D., Bunch, Josephine, Styles, Iain B.
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Bioinformatics; Biotechnology; Chemistry; Chemistry and Materials Science; Cluster Analysis; Clustering; Datasets; Diagnostic Imaging - methods; Hepatocytes - pathology; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Ions; Liver; Liver - chemistry; Liver - pathology; Liver Diseases - pathology; Mass spectrometry; Non-alcoholic Fatty Liver Disease - pathology; Organic Chemistry; Proteomics; Random Allocation; Reduction; Research Article; Scientific imaging; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Spectroscopy; Vector quantization
• ISSN: 1044-0305; 1879-1123
• DOI: 10.1007/s13361-014-1024-7

The ‘curse of dimensionality’ imposes fundamental limits on the analysis of the large, information rich datasets that are produced by mass spectrometry imaging. Additionally, such datasets are often too large to be analyzed as a whole and so dimensionality reduction is required before further analysis can be performed. We investigate the use of simple random projections for the dimensionality reduction of mass spectrometry imaging data and examine how they enable efficient and fast segmentation using k -means clustering. The method is computationally efficient and can be implemented such that only one spectrum is needed in memory at any time. We use this technique to reveal histologically significant regions within MALDI images of diseased human liver. Segmentation results achieved following a reduction in the dimensionality of the data by more than 99% (without peak picking) showed that histologic changes due to disease can be automatically visualized from molecular images. Graphical Abstract ᅟ