Modular metaX Pipeline for Processing Untargeted Metabolomics Data
Mass spectrometry-based untargeted metabolomics enables high-throughput profiling of metabolites with very diverse physicochemical properties in biological samples. The large amount of data generated from mass spectrometry platforms requires intensive computational processing using different methods. Processing and interpretation of these complex metabolomics data have become a key challenge in metabolomics studies. In this chapter, we present an R package metaX, which provides end-to-end metabolomics data analysis for mass spectrometry-based metabolomic data. The functions in metaX include peak detection, data pre-processing, normalization, univariate statistical analysis, multivariate statistical analysis such as PCA and PLS-DA, metabolite identification, functional analysis, power analysis, feature selection and modeling, data quality assessment, and normalization method evaluation. Importantly, metaX can be used to process and analyze different types of -omics data, which are often generated in conjunction with metabolomics data. This eliminates the need to use multiple software packages and pipelines in the analysis of a single multi-omics experiment.