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Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis

Author

Listed:
  • ?uksza Marta

    (Max Planck Institute for Molecular Genetics)

  • Kluge Bogus?aw

    (University of Warsaw)

  • Ostrowski Jerzy

    (Maria Sklodowska-Curie Memorial Institute of Oncology)

  • Karczmarski Jakub

    (Maria Sklodowska-Curie Memorial Institute of Oncology)

  • Gambin Anna

    (University of Warsaw)

Abstract

Proteomic mass spectrometry is gaining an increasing role in diagnostics and in studies on protein complexes and biological systems. This experimental technology is producing high-throughput data which is inherently noisy and may contain various errors. Mathematical processing can help in removing them.In this paper we focus on the peak alignment problem in LC-MS spectra. As an alternative to heuristic approaches to the problem, we propose a mathematically sound method which exploits a model-based clustering. In this framework experiment errors are modeled as deviations from real values and mass spectra are regarded as finite Gaussian mixtures. The advantage of such an approach is that it provides convenient techniques for adjusting parameters and selecting solutions of best quality. The method can be parameterized by assuming various constraints. In this paper we investigate and compare different classes of models. We analyze the results in terms of statistically significant biomarkers that can be identified after the alignment of spectra. The study was conducted on a dataset of plasma samples of colorectal cancer patients and healthy donors.

Suggested Citation

  • ?uksza Marta & Kluge Bogus?aw & Ostrowski Jerzy & Karczmarski Jakub & Gambin Anna, 2009. "Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, February.
    • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:15
    DOI: 10.2202/1544-6115.1308
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    References listed on IDEAS

    as
    1. Ruedi Aebersold & Matthias Mann, 2003. "Mass spectrometry-based proteomics," Nature, Nature, vol. 422(6928), pages 198-207, March.
    2. Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
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    Cited by:

    1. Melnykov, Volodymyr, 2013. "On the distribution of posterior probabilities in finite mixture models with application in clustering," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 175-189.

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