Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data.

Patrick X Zhao,Wenchao Zhang

doi:10.1186/1471-2105-15-s11-s5

Abstract

BackgroundExtracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. Most commonly, the LC/MS technique employs electrospray ionization as the ionization method. The EICs from LC/MS data are often noisy and contain high background signals. Furthermore, the chromatographic peak quality varies with respect to its location in the chromatogram and most peaks have zigzag shapes. Therefore, there is a critical need to develop effective metrics for quality evaluation of EICs and chromatographic peaks in LC/MS based metabolomics data analysis.ResultsWe investigated a comprehensive set of potential quality evaluation metrics for extracted EICs and detected chromatographic peaks. Specifically, for EIC quality evaluation, we analyzed the mass chromatographic quality index (MCQ index) and propose a novel quality evaluation metric, the EIC-related global zigzag index, which is based on an EIC's first order derivatives. For chromatographic peak quality evaluation, we analyzed and compared six metrics: sharpness, Gaussian similarity, signal-to-noise ratio, peak significance level, triangle peak area similarity ratio and the local peak-related local zigzag index.ConclusionsAlthough the MCQ index is suited for selecting and aligning analyte components, it cannot fairly evaluate EICs with high background signals or those containing only a single peak. Our proposed EIC related global zigzag index is robust enough to evaluate EIC qualities in both scenarios. Of the six peak quality evaluation metrics, the sharpness, peak significance level, and zigzag index outperform the others due to the zigzag nature of LC/MS chromatographic peaks. Furthermore, using several peak quality metrics in combination is more efficient than individual metrics in peak quality evaluation.

Highlights

Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis
In order to evaluate the efficiency of the metrics for the extracted EIC and the detected chromatographic peak, all of the EIC’s and peak’s chromatogram data points should be provided
We developed our own data processing program in Matlab, which consists of four sequential modules: 1) acute EIC extraction, 2) EIC quality evaluation and filtering, 3) chromatographic peak detection, and 4) peak quality evaluation and filtering (Figure 1)

Summary

Introduction

Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. The tracing method can resolve the splitting issue; it may produce low-quality extracted EICs displaying high noise and background levels that weaken or bury meaningful analyte peaks. This is due to contaminants along with other factors such as the LC mobile phase, atmospheric environment, or solvent types [8,9,10]. The processing modules in the currently available tools include EIC quality filtering and chromatographic peak filtering This is usually achieved by comparing them based on some threshold or criteria; the evaluation methods and cutoff thresholds greatly affect the final peak detection performance

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