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Issue 7, 2015
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Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy

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Abstract

Raman microspectroscopy has been investigated for some time for use in label-free cell sorting devices. These approaches require coupling of the Raman spectrometer to complex data mining algorithms for identification of cellular subtypes such as the leukocyte subpopulations of lymphocytes and monocytes. In this study, three distinct multivariate classification approaches, (PCA-LDA, SVMs and Random Forests) are developed and tested on their ability to classify the cellular subtype in extracted peripheral blood mononuclear cells (T-cell lymphocytes from myeloid cells), and are evaluated in terms of their respective classification performance. A strategy for optimisation of each of the classification algorithm is presented with emphasis on reduction of model complexity in each of the algorithms. The relative classification performance and performance characteristics are highlighted, overall suggesting the radial basis function SVM as a robust option for classification of leukocytes with Raman microspectroscopy.

Graphical abstract: Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy

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Article information


Submitted
17 Oct 2014
Accepted
22 Dec 2014
First published
24 Dec 2014

Analyst, 2015,140, 2473-2481
Article type
Paper
Author version available

Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy

A. Maguire, I. Vega-Carrascal, J. Bryant, L. White, O. Howe, F. M. Lyng and A. D. Meade, Analyst, 2015, 140, 2473
DOI: 10.1039/C4AN01887G

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