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

Author

Listed:
  • Łuksza Marta

    (Max Planck Institute for Molecular Genetics)

  • Kluge Bogusław

    (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.

Suggested Citation

  • Łuksza Marta & Kluge Bogusław & 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 1-34, 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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