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Discretized skew‐t mixture model for deconvoluting liquid chromatograph mass spectrometry data

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  • Xuwen Zhu
  • Xiang Zhang

Abstract

In this paper, new statistical algorithms for accurate peak detection in the metabolomic data are proposed. Specifically, liquid chromatograph‐mass spectrometry data are analyzed. The discretized skew‐t mixture model for peak detection is proposed. It shows great flexibility and capability in fitting skewed or heavy‐tailed peaks. The methodology is further extended to cross‐sample peak alignment for identifying the true peaks. A measure of peak credibility is provided through the assessment of misclassification probabilities between two cross‐sample peaks. The proposed algorithms are applied to spike‐in data with promising results.

Suggested Citation

  • Xuwen Zhu & Xiang Zhang, 2023. "Discretized skew‐t mixture model for deconvoluting liquid chromatograph mass spectrometry data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(3), pages 284-303, August.
    • Handle: RePEc:bla:stanee:v:77:y:2023:i:3:p:284-303
    DOI: 10.1111/stan.12285
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    References listed on IDEAS

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    1. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    2. Volodymyr Melnykov, 2013. "Finite mixture modelling in mass spectrometry analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 573-592, August.
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