Dexter, A., Race, A. M., Steven, R. T., Barnes, J. R., Hulme, H., Goodwin, R. J.A., Styles, I. B. and Bunch, J. (2017) Two-phase and graph-based clustering methods for accurate and efficient segmentation of large mass spectrometry images. Analytical Chemistry, 89(21), pp. 11293-11300. (doi: 10.1021/acs.analchem.7b01758) (PMID:28849641)
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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.
| Item Type: | Articles |
|---|---|
| Additional Information: | A.D. gratefully acknowledges financial support from the EPSRC through a studentship from the PSIBS Doctoral Training Centre (EP/F50053X/1), NPL and AstraZeneca. J.B., A.D., R.T.S., and A.M.R. all gratefully acknowledge funding from NPL Strategic Capability programme “AIMSHIGHER”. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | HULME, Heather |
| Authors: | Dexter, A., Race, A. M., Steven, R. T., Barnes, J. R., Hulme, H., Goodwin, R. J.A., Styles, I. B., and Bunch, J. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Life Sciences |
| Journal Name: | Analytical Chemistry |
| Publisher: | American Chemical Society (ACS) |
| ISSN: | 0003-2700 |
| ISSN (Online): | 1520-6882 |
| Published Online: | 29 August 2017 |
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