Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies

Goodpaster, Aaron M.; Kennedy, Michael A.

doi:10.1016/j.chemolab.2011.08.009

Cited by 79 publications

(73 citation statements)

References 11 publications

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“…Simply, a visual inspection of the clustering pattern or class separation in a scores plot is not typically sufficient to infer statistical relevance. Methods using cluster overlap metrics [99], statistical distances [98], and hierarchical clustering [100,101] have been successfully used to quantify separations in scores plots. Also, class membership may be inferred from 95% confidence ellipses calculated from scores [101].…”

Section: Scoresmentioning

confidence: 99%

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Multivariate Analysis in Metabolomics

Worley¹,

Powers²

2012

CMB

322

388

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Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

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Section: Scoresmentioning

confidence: 99%

“…For PCA and validated PLS scores, quantitative measures must be applied to reliably infer significant separations between classes within a scores plot [98]. Simply, a visual inspection of the clustering pattern or class separation in a scores plot is not typically sufficient to infer statistical relevance.…”

Section: Scoresmentioning

confidence: 99%

Multivariate Analysis in Metabolomics

Worley¹,

Powers²

2012

CMB

322

388

View full text Add to dashboard Cite

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“…PCA was performed on the bucket tables generated from AMIX using both AMIX and MetaboAnalyst 2.0 (MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis) [25]. For each scores plot generated during the analysis, Mahalanobis distance ( D M ), two-sample Hotelling's T 2 statistic ( T 2 ), F values (Ft) and critical F values (Fc) were calculated using MatLabR2010b [26]. …”

Section: Methodsmentioning

confidence: 99%

NMR-based metabolomics study of the biochemical relationship between sugarcane callus tissues and their respective nutrient culture media

et al. 2014

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The culture of sugarcane leaf explant onto culture induction medium triggers the stimulation of cell metabolism into both embryogenic and non-embryogenic callus tissues. Previous analyses demonstrated that embryogenic and nonembryogenic callus tissues have distinct metabolic profiles. This study is the follow-up to understand the biochemical relationship between the nutrient media and callus tissues using one-dimensional (1D 1H) and two-dimensional (2D 1H–13C) NMR spectroscopy followed by principal component analysis (PCA). 1D 1H spectral comparisons of fresh unspent media (FM), embryogenic callus media (ECM), non-embryogenic callus media (NECM), embryogenic callus (EC), and non-embryogenic callus (NEC), showed different metabolic relationships between callus tissues and media. Based on metabolite fold change analysis, significantly changing sugar compounds such as glucose, fructose, sucrose, and maltose were maintained in large quantities by EC only. Significantly different amino acid compounds such as valine, leucine, alanine, threonine, asparagine, and glutamine and different organic acid derivatives such as lactate, 2-hydroxyisobutyrate, 4-aminobutyrate, malonate, and choline were present in EC, NEC, and NECM, which indicates that EC maintained these nutrients, while NEC either maintained or secreted the metabolites. These media and callus-specific results suggest that EC and NEC utilize and/or secrete media nutrients differently.

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“…4a) shows clustering of groups and the distribution of these clusters around the descriptors. A statistical analysis of cluster separation was made using the Mahalanobis distance between centroids as described in (Goodpaster and Kennedy 2011) and significant cluster separation was found between the four groups (p \ 0.001).…”

Section: Example Of a Clinical Application Of Predicted Lipidsmentioning

confidence: 99%

Design and evaluation of standard lipid prediction models based on 1H-NMR spectroscopy of human serum/plasma samples

et al. 2015

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New approaches are increasingly being used for studying and evaluating coronary heart disease (CHD), especially since the irruption of metabolomics. The classical approach is to use enzymatically-measured standard lipids and these are still the main markers for assessing risk of CHD. Since metabolomics relies on advanced analytical technologies, such as MS and NMR, using them to estimate standard lipids would be of great interest because there is no need for additional biochemical measures. The present study evaluates partial least squares and N-way partial least squares regression models to predict standard lipid concentrations by using serum and plasma sample sets from various clinical centres. Information provided by editing NMR techniques and 2D diffusion NMR was incorporated in these models using four different data structures. Firstly, the models were calibrated and validated with three of the four sample sets (n = 591) involved. Then the best estimation models were selected and applied to the left-out sample set. This evaluation of a new sample set gave correlation coefficients of predicted versus biochemical variables above 0.86 and %rRMSE lower than 18 %. These values are similar to those found by other studies although, in our case, the results are more general because we used a higher number of samples (n = 785) from different sample sets, different clinical centres and different blood matrices (serum and plasma). Finally, we compared the performance of NMR predicted lipids and enzymatically measured lipids in a clinical case study.

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Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies

Cited by 79 publications

References 11 publications

Multivariate Analysis in Metabolomics

Multivariate Analysis in Metabolomics

NMR-based metabolomics study of the biochemical relationship between sugarcane callus tissues and their respective nutrient culture media

Design and evaluation of standard lipid prediction models based on 1H-NMR spectroscopy of human serum/plasma samples

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