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

TL;DR

This paper introduces a novel peak detection algorithm for comprehensive two-dimensional gas chromatography mass spectrometry data, improving accuracy and reducing false discoveries through a combined statistical modeling approach.

Contribution

The paper presents a new peak detection method using NEB and mixture models, offering a simpler yet effective alternative to existing complex models for GC×GC-TOF MS data analysis.

Findings

Lower false discovery rates compared to existing algorithms

Effective peak detection with a simpler mixture model

Optimal model selection via trial-and-error approach

Abstract

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