metaX: a flexible and comprehensive software for processing metabolomics data

By Bo Wen, Zhanlong Mei, Chunwei Zeng, Siqi Liu. BMC Bioinformatics, 2017

822 citations recorded by the source corpus.

Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface (http://metax.genomics.cn) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/. The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.

Subfields: Biophysics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/96bfffb06ac6ef32b6731e7ee667e4881ce3f9b7

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Citations in the corpus

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies (2018, methodology): “A more efficient approach is to use, for example, k-fold or leave-one-out cross validation (Kirwan et al. 2013; Wen et al. 2017).”