Abstract
In this paper we propose to use Gaussian mixture decompositions of protein mass spectral signals to construct least squares estimators of peptide species concentrations in proteomic samples and further to use these estimators as spectral features in cancer versus normal spectral classifiers. For a real dataset we compare variances of least squares estimators to variances of analogous estimators based on spectral peaks. We also evaluate performance of spectral classifiers with features defined by either least squares estimators or by spectral peaks by their power to differentiate between patterns specific for case and control samples of head and neck cancer patients. Cancer/normal classifiers based on spectral features defined by Gaussian components achieved lower average error rates than classifiers based on spectral peaks.
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Acknowledgements
This work was financially supported by the Polish National Science Centre UMO-2011/01/B/ST6/06868 grant (A.P.), GeCONiI project number POIG.02.03.01-24-099/13 (M.M.) and internal grant from Silesian University of Technology BK/265/RAU-1/2014 t.10 (J.P.). All the calculations were carried out using GeCONiI infrastructure funded by project number POIG.02.03.01-24-099/13.
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Polanski, A., Marczyk, M., Pietrowska, M., Widlak, P., Polanska, J. (2015). Least Squares Estimators of Peptide Species Concentrations Based on Gaussian Mixture Decompositions of Protein Mass Spectra. In: Steland, A., Rafajłowicz, E., Szajowski, K. (eds) Stochastic Models, Statistics and Their Applications. Springer Proceedings in Mathematics & Statistics, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-13881-7_47
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DOI: https://doi.org/10.1007/978-3-319-13881-7_47
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