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Mixed effect modelling of proteomic mass spectrometry data by using Gaussian mixtures

Author

Listed:
  • William J. Browne
  • Ian L. Dryden
  • Kelly Handley
  • Shahid Mian
  • Dirk Schadendorf

Abstract

Summary. Statistical methodology for the analysis of proteomic mass spectrometry data is proposed using mixed effects models. Each high dimensional spectrum is represented by using a near orthogonal low dimensional representation with a basis of Gaussian mixture functions. Linear mixed effect models are proposed in the lower dimensional space. In particular, differences between groups are investigated by using fixed effect parameters, and individual variability of spectra is modelled by using random effects. A deterministic peak fitting algorithm provides estimates of the near orthogonal Gaussian basis. The mixed effects model is fitted by using restricted maximum likelihood, and a parallel fitting procedure is used for computational convenience. The methodology is applied to proteomic mass spectrometry data from serum samples from melanoma patients who were categorized as stage I or stage IV, and significant locations of peaks are identified.

Suggested Citation

  • William J. Browne & Ian L. Dryden & Kelly Handley & Shahid Mian & Dirk Schadendorf, 2010. "Mixed effect modelling of proteomic mass spectrometry data by using Gaussian mixtures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 617-633, August.
    • Handle: RePEc:bla:jorssc:v:59:y:2010:i:4:p:617-633
    DOI: 10.1111/j.1467-9876.2009.00706.x
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    References listed on IDEAS

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    1. Jeffrey S. Morris & Philip J. Brown & Richard C. Herrick & Keith A. Baggerly & Kevin R. Coombes, 2008. "Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models," Biometrics, The International Biometric Society, vol. 64(2), pages 479-489, June.
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    Cited by:

    1. Huang, Shih-Ting & Xie, Fang & Lederer, Johannes, 2021. "Tuning-free ridge estimators for high-dimensional generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).

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