期刊
METABOLITES
卷 10, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/metabo10100416
关键词
data cleaning; preprocessing; Python; untargeted metabolomics; reference materials; data curation; system suitability; signal drift; quality control
资金
- CONICET (Consejo Nacional de Investigaciones Cientificas y Tecnicas, Argentina) [PUE 055]
- National Agency of Scientific and Technological Promotion [PRH-PICT-2015-0022, PICT-2018-02137]
Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography-mass spectrometry (LC-MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC-MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC-MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.
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