MEASURING VARIABILITY SOURCES IN NMR METABOLOMIC STUDIES
E. Rozet1,*, P. De Tullio2, Ph. Hubert1, B. Govaerts3
1.
Analytical Chemistry Laboratory, CIRM, Université de Liège, Liège, Belgium.
Medicinal Chemistry, CIRM, Université de Liège, Liège, Belgium.
3.
Institut de Statistique, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.
*
Eric.Rozet@ulg.ac.be
2.
Due to the huge amount of information available in NMR spectra obtained from the analysis
of metabolomic experiments, multivariate analysis such as Principal Component Analysis
(PCA) are required to understand the influence of treatments over the metabolites [1].
However, many experiments in metabolomics studies have more complexes variability
structures than simply comparing several treatments: they may include time effects, biological
effects such as diet or hormonal status, and other blocking factors or variability sources:
samples stability, age of the individuals, pH of a buffer, days of acquisition, and so on.
Metabolomic data analysis needs to extract from the spectral data matrix the variations linked
to a change indicated in the factor of interest. However other sources of variability may
impair this objective. This stresses the importance to discover the sources of variability of the
spectral metabolomic data using appropriate methodology.
Classically, to analyze such data analysis of variance (ANOVA) or multivariate ANOVA
(MANOVA) [2] is used. However direct application of these methodologies to NMR spectra
obtained from structured metabolomics studies is inappropriate or impossible. More complex
data analyses methodologies are required to understand the importance of the various factors
implied in the experiments and to provide a measure of their variance components. Three
related methodologies have been proposed to achieve this: ASCA [3], ANOVA-PCA [4] and
AComDim [5]. The ASCA and ANOVA-PCA methodologies combine first an analysis of
variance step (ANOVA) and then a PCA step. The AComDim one adds to the output of the
ANOVA-PCA step a multi-block analysis.
In this presentation, the usefulness and applicability of these advanced techniques to data
analysis of NMR metabolomic spectra are provided to highlight the increase of knowledge
gained and the estimation of main sources of variability arising in an experimental setup.
Two NMR databases will be used [6]. The first one concerns human serum analyzed by 1HNMR where three random factors are present: day of measurement (3 days), sample (2
samples per individual) and replication of analyses as well as two fixed controlled factors,
time of measurement after thawing (2 times) and two protein suppression methods for the
spectral pre-treatment. The second database is about the 1H-NMR analyses of rats’ urine
where two different concentrations of citrate and of hippurate were deliberately added and
three other sources of variability are present: urine pool diluted or not diluted, repetitions of
analyses, days of analyses (three days), as well as two different spectral pre-treatment
procedures.
[1] Metabolomics in Practice, Successful strategies to generate and analyze metabolomic data, M. Lämmerhofer
and W. Weckwerth (Ed), Wiley-VCH Verlag, 2013, 415 p.
[2] L. Stahl, et al., Chemom. Intell. Lab. Syst. 9 (1990) 127-141.
[3] J.J. Jansen, et al., J. Chemom., 19 (2005) 469-481.
[4] P.B. Harrington, et al. Anal. Chim. Acta 544 (2005) 118-127.
[5] D.J.R. Bouveresses, et al., Chemom. Intell. Lab. Syst. 106 (2011) 173-182.
[6] R. Rousseau, Statistical Contribution to the analysis of metabonomic data in 1H-NMR spectroscopy,
Doctoral Thesis, Université Catholique de Louvain(UCL), Louvain-la-Neuve, 2011.