The use of random projections for the analysis of mass spectrometry imaging data

Andrew D. Palmer, Josephine Bunch, Iain B. Styles

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
403 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)315-322
JournalJournal of the American Society for Mass Spectrometry
Volume26
Issue number2
Early online date19 Dec 2014
DOIs
Publication statusPublished - 1 Feb 2015

Keywords

  • Random projection
  • Mass spectrometry imaging
  • Informatics
  • Segmentation
  • Digital histology
  • Dimensionality reduction
  • Data processing

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