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Dexter A, Race AM, Steven RT, et al. Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images. Anal Chem. 2017;89(21):11293-11300doi: 10.1021/acs.analchem.7b01758.
Dexter, A., Race, A. M., Steven, R. T., Barnes, J. R., Hulme, H., Goodwin, R. J. A., Styles, I. B., & Bunch, J. (2017). Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images. Analytical chemistry, 89(21), 11293-11300. https://doi.org/10.1021/acs.analchem.7b01758
Dexter, Alex, et al. "Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images." Analytical chemistry vol. 89,21 (2017): 11293-11300. doi: https://doi.org/10.1021/acs.analchem.7b01758
Dexter A, Race AM, Steven RT, Barnes JR, Hulme H, Goodwin RJA, Styles IB, Bunch J. Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images. Anal Chem. 2017 Nov 07;89(21):11293-11300. doi: 10.1021/acs.analchem.7b01758. Epub 2017 Oct 25. PMID: 28849641.
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Anal Chem. 2017 Nov 07;89(21):11293-11300. doi: 10.1021/acs.analchem.7b01758. Epub 2017 Oct 25.

Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.

Analytical chemistry

Alex Dexter, Alan M Race, Rory T Steven, Jennifer R Barnes, Heather Hulme, Richard J A Goodwin, Iain B Styles, Josephine Bunch

Affiliations

  1. PSIBS Doctoral Training Centre, University of Birmingham Edgbaston, Birmingham B15 2TT, United Kingdom.
  2. National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom.
  3. AstraZeneca , Drug Safety and Metabolism, Cambridge CB4 0WG, United Kingdom.
  4. University of Glasgow, University Avenue , Glasgow, G12 8QQ, United Kingdom.
  5. School of Computer Science, University of Birmingham , Edgbaston, Birmingham B15 2TT, United Kingdom.
  6. School of Pharmacy, University of Nottingham , Nottingham, Nottinghamshire NG7 2RD, United Kingdom.

PMID: 28849641 DOI: 10.1021/acs.analchem.7b01758

Abstract

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.

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