Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 418440
Title Data-processing strategies for metabolomics studies
Author(s) Hendriks, M.M.W.B.; Eeuwijk, F.A. van; Jellema, R.H.; Westerhuis, J.A.; Reijmers, T.H.; Hoefsloot, H.C.J.; Smilde, A.K.
Source TrAC : Trends in Analytical Chemistry 30 (2011)10. - ISSN 0165-9936 - p. 1685 - 1698.
DOI http://dx.doi.org/10.1016/j.trac.2011.04.019
Department(s) PRI Biometris
Biometris (WU MAT)
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) principal component analysis - mass-spectrometry - variable selection - optimal-design - models - identification - metabolites - networks - tool - nmr
Abstract Metabolomics studies aim at a better understanding of biochemical processes by studying relations between metabolites and between metabolites and other types of information (e.g., sensory and phenotypic features). The objectives of these studies are diverse, but the types of data generated and the methods for extracting information from the data and analysing the data are similar. Besides instrumental analysis tools, various data-analysis tools are needed to extract this relevant information. The entire data-processing workflow is complex and has many steps. For a comprehensive overview, we cover the entire workflow of metabolomics studies, starting from experimental design and sample-size determination to tools that can aid in biological interpretation. We include illustrative examples and discuss the problems that have to be dealt with in data analysis in metabolomics. We also discuss where the challenges are for developing new methods and tailor-made quantitative strategies
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