A new method of peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data

摘要

We develop a novel peak detection algorithm for the analysis of comprehensivetwo-dimensional gas chromatography time-of-flight mass spectrometry(GC$\times$GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixtureprobability models. The algorithm first performs baseline correction anddenoising simultaneously using the NEB model, which also defines peak regions.Peaks are then picked using a mixture of probability distribution to deal withthe co-eluting peaks. Peak merging is further carried out based on the massspectral similarities among the peaks within the same peak group. The algorithmis evaluated using experimental data to study the effect of different cutoffsof the conditional Bayes factors and the effect of different mixture modelsincluding Poisson, truncated Gaussian, Gaussian, Gamma and exponentiallymodified Gaussian (EMG) distributions, and the optimal version is introducedusing a trial-and-error approach. We then compare the new algorithm with twoexisting algorithms in terms of compound identification. Data analysis showsthat the developed algorithm can detect the peaks with lower false discoveryrates than the existing algorithms, and a less complicated peak picking modelis a promising alternative to the more complicated and widely used EMG mixturemodels.