Metabonomics data

With respect to preclinical studies, one interesting observation determined in metabonomic studies, is that vehicles used for compound administration are not biologically inert, and may influence the analysis of metabonomic data. In fact, some commonly used vehicles for compound administration are not compatible with metabonomics applications, most notably Labrofil and polyethylene glycol (21). Finally, because NMR is... 

C. E. Thomas and G Ganji, Integration of genomic and metabonomic data in systems biology Are we there yet Curr. Opin. Drug Discovery Dev. 9 (2006), 92-100. 

Vehtari A, Makinen VP, Soininen P, Ingman P, Makela SM, Savolainen MJ, Hannuksela ML, Kaski K, and Ala-Korpela M. A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in IH NMR metabonomic data. BMC Bioinformat. 2007 8(suppl 2) S8. 

An in-depth review of statistical methods for metabonomic data analysis is beyond the scope of this chapter. Briefly, there are a few main approaches to data analysis. Examples of multivariate data analyses include the so-called unsupervised analyses such as PCA, independent component analysis (ICA), and hierarchical clustering analysis (HCA), while partial least square differential analysis (PLS-DA) is... 

Commercial and Free Software Tools for MS Metabonomics Data Processing... 

Moreover, there are a number of publicly available free and commercial spectral (e.g., MS- and NMR-based), chemical, and biochemical/metabolic pathway databases that can be useful in identification and characterization of metabolites, as well as interpretation of metabonomics data at a biochemical pathway level. Examples of such databases are listed in Table 10.4. In a very recent publication, Loftus et al. [150] provided an example in which unknown analytes in complex biological matrices can be detected and characterized using high mass accuracy ESI MS" and formula prediction software, as well as by comparison to mass spectral databases, rather than by following the standard identification route via comparison to an authentic standard. This seems to be an attractive and a promising alternative for future profiling studies. 

Ebbels T, Keun H, Beckonert O, et al. (2003). Toxicity classification from metabonomic data using a density superposition approach CLOUDS . Analyt. Chim. Acta. 490 109-122. 

Statistical Analysis and Reporting Methods for statistical analysis of metabonomics data sets include a variety of supervised and unsupervised multivariate techniques (Holmes et al., 2000) as well as univariate analysis strategies. These chemometric approaches have been recently reviewed (Holmes and Antti, 2002 Robertson et al., 2007), and a thorough discussion of these is outside the scope of this chapter. Perhaps the best known of the unsupervised multivariate techniques is principle component analysis (PCA) and is widely... 

A. Craig, O. Cloarec, E. Holmes, J. K. Nicholson, J. C. Lindon, Scaling and Normalization Effects in NMR Spectroscopic Metabonomic Data Sets, Analytical Chemistry (2006), 78 (7), 2262 - 2267. 

Craig A, Cloarec O, Hohnes E, Nicholson JK, Lindon JC. Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem 2006 78 2262-7. 

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