Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

By Warwick B. Dunn, David Broadhurst, P. Begley, E. Zelená, S. Francis-McIntyre, N. Anderson, Marie Brown, Joshua D. Knowles, A. Halsall, J. Haselden and 4 more. Nature Protocols, 2011

2,882 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/a81beb0ad724e97a0212b9f5299b90a181de9c61

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Citations in the corpus

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling (2016, methodology): “Prior to univariate and multivariate data analysis this data matrix typically undergoes a number of processes including quality control (Dunn et al. 2011), missing value imputation, normalisation, scaling and transformation; here we will define this as ‘data processing’ and the processes applied…”
NOREVA: normalization and evaluation of MS-based metabolomics data (2017, methodology): “Secondly, data pre-processing corrected signals by QC-RLSC (5) and missing value imputation (56).”
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights (2021, methodology): “The utility function for batch effect and signal drift correction has been updated with eight algorithms-EigenMS (52), QC-RLSC (53), ANCOVA (54), RUV-random (55), RUV2 (56), RUVseq (57), NOMIS (58) and CCMS (59) for correction based on either the data itself, QC samples or internal standards.”
MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis (2012, background): “Likewise, with increasing numbers of large-scale metabolomic studies being performed, the need for data quality control (QC) and quality assessment support has become much more apparent (6,7) and much more frequently requested by MetaboAnalyst users.”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “Here we will focus on the ‘processing’ step in the analysis of metabolomics data as the other steps are reviewed in more detail elsewhere [7,22,68–76].”
MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics (2020, background): “QC-RLSC could be specified to adjust the signal drift.”
metaX: a flexible and comprehensive software for processing metabolomics data (2017, background): “These effects can detrimentally impact the significant signal discovery and MS features that are required for rigorous quality assurance [14, 18].”