What is it about?
Metabolomics is an emerging field of "omics" research concerned with the comprehensive characterisation of the small molecules in biological systems. The method is increasing being used in population-based or epidemiological studies. However, there are a number of significant challenges to achieve the optimal biological outcome of a study. This articles concerns with data processing and normalisation.
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Why is it important?
This article considered the state-of-the-art of data normalisation methods and compared the performance of these method using an epidemiological study of gestational diabetes. Published works in this area was rare, and sometime biased (for example, when an author reported a new method s/he developed and compared against other methods) or have omissions (for example, when an author only consider one type of data normalisation approach). We do not feel that the conclusions or recommendations made in those works were justified and like to address these issues in this manuscript.
Perspectives
The work was initially a side project of a GDM study. The first author designed the GC-MS experiment, and the second author prepared the sample and acquired the data. The rest was handled and performed by me. The idea of the article came from the problems encountered with the in-hourse developed software MassOmics in processing the raw data, and so a better method had to be developed.
Kai Law
Read the Original
This page is a summary of: Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy, F1000Research, June 2017, Faculty of 1000, Ltd.,
DOI: 10.12688/f1000research.11823.1.
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