In:
Phytochemical Analysis, Wiley, Vol. 21, No. 1 ( 2010-01), p. 48-60
Abstract:
Analysis of the data is one of the most fulfilling steps in the metabolomics pipeline, because the generated models show the actual result of the experiment. However, the multivariate statistical methods used in this step may be remote from the expertise of many scientists involved in these interdisciplinary studies. This overview provides a step‐by‐step description of a multivariate data analysis, starting from the experiment and ending with the figures appearing in scientific journals. Instead of plant metabolomics data with many measured metabolites, we developed a thought experiment in a greenhouse that explores the relationship of three plant developmental descriptors to a difference in nutrient levels. The rationale behind several steps in data preprocessing, model fit and model validation will be described using the plant arrangement within the greenhouse, the photographs taken of the greenhouse and the critical evaluation of the information in the photos in the thought experiment. This paper will familiarize non‐specialized researchers with the main concepts in multivariate data analysis and allow them to develop and evaluate metabolomic data analyses more critically.
Type of Medium:
Online Resource
ISSN:
0958-0344
,
1099-1565
Language:
English
Publisher:
Wiley
Publication Date:
2010
detail.hit.zdb_id:
1498615-2
SSG:
12
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