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

TY - JOUR

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

M3 - Journal article

SN - 0974-7419

VL - 84

SP - 6869

EP - 6876

JO - Analytical Chemistry

JF - Analytical Chemistry

IS - 15

ER -