Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach

Mattias Eliasson; Stefan Rannar; Rasmus Madsen; Magdalena A. Donten; Emma Marsden-Edwards; Thomas Moritz; John P. Shockcor; Erik Johansson; Johan Trygg

doi:10.1021/ac301482k

Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach

Mattias Eliasson, Stefan Rannar, Rasmus Madsen, Magdalena A. Donten, Emma Marsden-Edwards, Thomas Moritz, John P. Shockcor, Erik Johansson, Johan Trygg

68 Citations (Scopus)

Abstract

A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

Original language	English
Journal	Analytical Chemistry
Volume	84
Issue number	15
Pages (from-to)	6869-6876
Number of pages	8
ISSN	0974-7419
DOIs	https://doi.org/10.1021/ac301482k
Publication status	Published - 7 Aug 2012
Externally published	Yes

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title = "Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach",

abstract = "A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.",

author = "Mattias Eliasson and Stefan Rannar and Rasmus Madsen and Donten, {Magdalena A.} and Emma Marsden-Edwards and Thomas Moritz and Shockcor, {John P.} and Erik Johansson and Johan Trygg",

year = "2012",

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T1 - Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach

AU - Eliasson, Mattias

AU - Rannar, Stefan

AU - Madsen, Rasmus

AU - Donten, Magdalena A.

AU - Marsden-Edwards, Emma

AU - Moritz, Thomas

AU - Shockcor, John P.

AU - Johansson, Erik

AU - Trygg, Johan

PY - 2012/8/7

Y1 - 2012/8/7

N2 - A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

AB - A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

U2 - 10.1021/ac301482k

DO - 10.1021/ac301482k

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JO - Analytical Chemistry

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IS - 15

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Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach

Abstract

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