metaX: a flexible and comprehensive software for processing metabolomics data

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 183
• Main Authors: Wen, Bo, Mei, Zhanlong, Zeng, Chunwei, Liu, Siqi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.03.2017; Springer Nature B.V
• Subjects: Acids; Algorithms; Biocompatibility; Bioinformatics; Biomass; Biomedical and Life Sciences; Cardiovascular disease; Chambers; Chromatography; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer programs; Coronary artery disease; Data acquisition; Data analysis; Data processing; Dopamine D1 receptors; Dopamine D4 receptors; Dopamine D5 receptors; Environment models; Enzymes; Functions (mathematics); Gas chromatography; Gene expression; Genomes; Heart; Heart diseases; Information systems; Life Sciences; Liquid chromatography; Mass spectra; Mass spectrometry; Mass Spectrometry - methods; Mathematical analysis; Metabolism; Metabolites; Metabolomics - methods; Microarrays; Microorganisms; Modules; Multivariate statistical analysis; Network analysis; Open source software; Programming languages; Proteomics; Quality Control; Quality standards; Regression analysis; Software; Statistical analysis; Statistics; Surgical implants; Urine; Variance analysis; Metabolomics; Workflow; Normalization; Quality control; Pipeline
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1579-y

Background 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. Results 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/ . Conclusions The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.