publication . Article . Other literature type . 2016

Improved batch correction in untargeted MS-based metabolomics

Jos Hageman; Joost Keurentjes; ROBERT HALL; Padraic Flood; Ron Wehrens;
Open Access
  • Published: 18 Mar 2016 Journal: Metabolomics, volume 12 (issn: 1573-3882, eissn: 1573-3890, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
  • Country: Netherlands
Abstract
<p>Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. Methods: Batch correction method...
Subjects
free text keywords: Original Article, Batch correction, Untargeted metabolomics, Non-detects, Mass spectrometry, Arabidopsis thaliana, Clinical Biochemistry, Biochemistry, Endocrinology, Diabetes and Metabolism
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Article . 2016
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Article . 2016
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41 references, page 1 of 3

Cordovez, V., Carrion, V. J., Etalo, D. W., Mumm, R., Zhu, H., & van Wezel, G. P., et al. (2015). Diversity and functions of volatile organic compounds produced by streptomyces from a diseasesuppressive soil. Frontiers in Microbiology (accepted for publication).

de Vos, R. C. H., Moco, S., Lommen, A., Keurentjes, J. J. B., Bino, R. J., & Hall, R. D. (2007). Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nature Protocols, 2, 778-791. [OpenAIRE]

De Livera, A. M., Sysi-Aho, M., Jacob, L., Gagnon-Bartsch, J. A., Castillo, S., Simpson, J. A., et al. (2015). Statistical methods for handling unwanted variation in metabolomics data. Analytical Chemistry, 87, 3606-3615. [OpenAIRE]

Draisma, H. H. M., Reijmers, T. H., van der Kloet, F., BobeldijkPastorova, I., Spies-Faber, E., Vogels, J. T. W. E., et al. (2010). Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets. Analytical Chemistry, 82, 1039-1046.

Dunn W. B., Broadhurst D., Begley P., Zelena E., Francis-McIntyre S., Anderson N., Brown M., Knowles J. D., Halsall A., Haselden J. N., Nicholls A. W., Wilson I. D., Kell D. B., Goodacre R., & The Human Serum Metabolome (HUSERMET) Consortium (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7):1060-1083.

Dunn WB, Erban A, Weber RJM, Creek DJ, Brown M, Breitling R, et al. (2013). Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics, 9, 44-66.

Ferna´ndez-Albert, F., Llorach, R., Garcia-Aloy, M., Ziyatdinov, A., Andres-Lacueva, C., & Perera, A. (2014). Intensity drift removal in LC/MS metabolomics by common variance compensation. Bioinformatics, 30, 2899-2905.

Flood P (2015) Natural genetic variation in Arabidopsis thaliana photosynthesis. PhD thesis, Wageningen UR,.

Franceschi, P., Mylonas, R., Shahaf, N., Scholz, M., Arapitsas, P., Masuero, D., et al. (2014). MetaDB: a data processing workflow in untargeted MS-based metabolomics experiments. Frontiers in Bioengineering and Biotechnology, 2, 72.

Gagnon-Bartsch, J. A., & Speed, T. P. (2012). Using control genes to correct for unwanted variation in microarray data. Biostatistics, 13, 539-552.

Gomez Roldan, M. V., Engel, B., de Vos, R. C. H., Vereijken, P., Astola, L., Groenenboom, M., et al. (2014). Metabolomics reveals organ-specific metabolic rearrangement during early tomato seedling development. Metabolomics, 10, 958-974. [OpenAIRE]

Greene, W. H. (2003). Econometric analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Hendriks, M. M. W. B., van Eeuwijk, F. A., Jellema, R. H., Westerhuis, J. A., Reijmers, T. H., Hoefsloot, H. C. J., et al. (2011). Data-processing strategies for metabolomics studies. Trends in Analytical Chemistry, 30, 1685-1698. [OpenAIRE]

Hennig C (2014). fpc: Flexible procedures for clustering. URL http:// CRAN.R-project.org/package=fpc. R package version 2.1-9

Horton, M. W., Hancock, A. M., Huang, Y. S., Toomajian, C., Atwell, S., Auton, A., et al. (2012). Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nature Genetics, 44, 212-216. [OpenAIRE]

41 references, page 1 of 3
Abstract
<p>Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. Methods: Batch correction method...
Subjects
free text keywords: Original Article, Batch correction, Untargeted metabolomics, Non-detects, Mass spectrometry, Arabidopsis thaliana, Clinical Biochemistry, Biochemistry, Endocrinology, Diabetes and Metabolism
Related Organizations
Download fromView all 7 versions
MPG.PuRe
Article . 2016
Provider: MPG.PuRe
Wageningen Yield
Article . 2016
Provider: NARCIS
41 references, page 1 of 3

Cordovez, V., Carrion, V. J., Etalo, D. W., Mumm, R., Zhu, H., & van Wezel, G. P., et al. (2015). Diversity and functions of volatile organic compounds produced by streptomyces from a diseasesuppressive soil. Frontiers in Microbiology (accepted for publication).

de Vos, R. C. H., Moco, S., Lommen, A., Keurentjes, J. J. B., Bino, R. J., & Hall, R. D. (2007). Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nature Protocols, 2, 778-791. [OpenAIRE]

De Livera, A. M., Sysi-Aho, M., Jacob, L., Gagnon-Bartsch, J. A., Castillo, S., Simpson, J. A., et al. (2015). Statistical methods for handling unwanted variation in metabolomics data. Analytical Chemistry, 87, 3606-3615. [OpenAIRE]

Draisma, H. H. M., Reijmers, T. H., van der Kloet, F., BobeldijkPastorova, I., Spies-Faber, E., Vogels, J. T. W. E., et al. (2010). Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets. Analytical Chemistry, 82, 1039-1046.

Dunn W. B., Broadhurst D., Begley P., Zelena E., Francis-McIntyre S., Anderson N., Brown M., Knowles J. D., Halsall A., Haselden J. N., Nicholls A. W., Wilson I. D., Kell D. B., Goodacre R., & The Human Serum Metabolome (HUSERMET) Consortium (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7):1060-1083.

Dunn WB, Erban A, Weber RJM, Creek DJ, Brown M, Breitling R, et al. (2013). Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics, 9, 44-66.

Ferna´ndez-Albert, F., Llorach, R., Garcia-Aloy, M., Ziyatdinov, A., Andres-Lacueva, C., & Perera, A. (2014). Intensity drift removal in LC/MS metabolomics by common variance compensation. Bioinformatics, 30, 2899-2905.

Flood P (2015) Natural genetic variation in Arabidopsis thaliana photosynthesis. PhD thesis, Wageningen UR,.

Franceschi, P., Mylonas, R., Shahaf, N., Scholz, M., Arapitsas, P., Masuero, D., et al. (2014). MetaDB: a data processing workflow in untargeted MS-based metabolomics experiments. Frontiers in Bioengineering and Biotechnology, 2, 72.

Gagnon-Bartsch, J. A., & Speed, T. P. (2012). Using control genes to correct for unwanted variation in microarray data. Biostatistics, 13, 539-552.

Gomez Roldan, M. V., Engel, B., de Vos, R. C. H., Vereijken, P., Astola, L., Groenenboom, M., et al. (2014). Metabolomics reveals organ-specific metabolic rearrangement during early tomato seedling development. Metabolomics, 10, 958-974. [OpenAIRE]

Greene, W. H. (2003). Econometric analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Hendriks, M. M. W. B., van Eeuwijk, F. A., Jellema, R. H., Westerhuis, J. A., Reijmers, T. H., Hoefsloot, H. C. J., et al. (2011). Data-processing strategies for metabolomics studies. Trends in Analytical Chemistry, 30, 1685-1698. [OpenAIRE]

Hennig C (2014). fpc: Flexible procedures for clustering. URL http:// CRAN.R-project.org/package=fpc. R package version 2.1-9

Horton, M. W., Hancock, A. M., Huang, Y. S., Toomajian, C., Atwell, S., Auton, A., et al. (2012). Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nature Genetics, 44, 212-216. [OpenAIRE]

41 references, page 1 of 3
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