Dataset Information

Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.

ABSTRACT: The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two-dimensional gas chromatography mass spectrometry (2DGCMS)-based metabolomics data. Due to uncontrollable experimental conditions, e.g., the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Various peak alignment algorithms have been developed to correct retention time shifts for homogeneous, heterogeneous or both type of mass spectrometry data. However, almost all existing algorithms have been focused on a local alignment and are suffering from low accuracy especially when aligning dense biological data with many peaks. We have developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time-based CPD-GPA (RT), prior CPD-GPA (P), mixture CPD-GPA (M), and prior mixture CPD-GPA (P+M). The method RT performs the peak alignment based only on the retention time distance, while the methods P, M, and P+M carry out the peak alignment using both the retention time distance and mass spectral similarity. The method P incorporates the mass spectral similarity through prior information and the methods M and P+M use the mixture distance measure. Four developed algorithms are applied to homogeneous and heterogeneous spiked-in data as well as two real biological data and compared with three existing algorithms, mSPA, SWPA, and BiPACE-2D. The results show that our CPD-GPA algorithms perform better than all existing algorithms in terms of F1 score.

SUBMITTER: Li Z

PROVIDER: S-EPMC7837599 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.

Li Zeyu Z Kim Seongho S Zhong Sikai S Zhong Zichun Z Kato Ikuko I Zhang Xiang X

Journal of chemometrics 20200328 8

The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two-dimensional gas chromatography mass spectrometry (2DGCMS)-based metabolomics data. Due to uncontrollable experimental conditions, e.g., the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Va ...[more]

PMID: 33505107

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Project description:BackgroundComprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.ResultsWe developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.ConclusionsThe proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.

Project description:BACKGROUND: Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features. RESULTS: In this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CeMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum). CONCLUSIONS: We have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.

Dataset Information

Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.

Publications

Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.

Similar Datasets

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