Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.

By S. Bijlsma, I. Bobeldijk, E. Verheij, R. Ramaker, S. Kochhar, I. Macdonald, B. van Ommen, Age K. Smilde. Analytical Chemistry, 2006

837 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6f46c71416c56e7eeea78b79a43cb69968e8795a

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Citations in the corpus

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis (2019, methodology): “MetaboAnalyst provides two options for its test statistics: (a) the separation distance which is defined as the ratio of the between-group sum of the squares and the within-group sum of squares (B/W-ratio) as suggested by Bijlsma et al. (2006), or (b) the prediction accuracy.”
MetaboAnalyst: a web server for metabolomic data analysis and interpretation (2009, methodology): “We also implemented PLS-DA permutation tests to help users determine the importance of class separation (19).”
Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data (2018, methodology): “For both targeted and profiling/non-targeted metabolomics data, we recommend (1) checking raw data and if necessary, adjusting parameter settings or manually peak matching to fill back certain missing values in an accurate way; (2) filtering missing variables, e.”
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis (2016, methodology): “MetaboAnalyst provides two options for its test statistics: (a) The separation distance, which is defined as the ratio of the between-group sum of the squares and the within-group sum of squares (B/W ratio) as suggested by Bijlsma et al. (2006); and (b) The prediction accuracy.”
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.”
Multiclass partial least squares discriminant analysis: Taking the right way—A critical tutorial (2018, neutral): “PCA gives us the following decomposition: Ŷ 1⁄4 um þ TP (3)”
Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst (2011, methodology): “The first is the separation distance, which is defined as the ratio of the between-group sum of squares and the within-group sum of squares (B/W ratio), as suggested by Bijlsma et al”
Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling (2016, methodology): “Bijlsma et al. (2006) assessed different scaling, univariate analysis and multivariate analysis procedures in order to identify lipidomic biomarkers applying LC–MS.”