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

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.