Mao-Mao Zeng

Recipe for revealing informative metabolites based on model population analysis

An important application of metabolic profiles is to discover informative metabolites/biomarkers which are predictive of a clinical outcome under investigation. Therefore, there is a need to develop statistically efficient method for screening such kind of metabolites from the candidates. The most commonly used criteria to assess variable (metabolite) importance may be the P value obtained by performing t test on each metabolite alone, without considering the influence of other variables. In this work, a new strategy, called subwindow permutation analysis (SPA) coupled with partial least squares linear discriminant analysis (PLSLDA), is developed for statistical assessment of variable importance. The main contribution of SPA is that, unlike t test, it can output a conditional P value by implicitly taking into account the synergetic effect of all the other variables. In this sense, the conditional P value could to some extent help locate a good combination of informative variables. When applied to two metabolic datasets (type 2 diabetes mellitus data and childhood overweight data), it is shown that the performance of both the unsupervised principal component analysis (PCA) and the supervised PLSLDA are greatly improved when using the informative metabolites revealed by SPA. The source codes for implementing SPA in both MATLAB and R (R package for both Linux and Windows) are freely available at: http://code.google.com/p/spa2010/downloads/list.

Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis

Metabolic fingerprinting is a powerful tool for exploring systemic metabolic perturbations and potential biomarkers, thus may shed light on the pathophysiological mechanism of diseases. In this work, a new strategy of metabolic fingerprinting was proposed to exploit the disturbances of metabolic patterns and biomarker candidates of childhood obesity. Plasma samples from children with normal weight, overweight and obesity were first profiled by GC/MS. ULDA (uncorrelated linear discriminant analysis) then revealed that the metabolic patterns of the three groups were different. Furthermore, several metabolites, say isoleucine, glyceric acid, serine, 2,3,4-trihydroxybutyric acid and phenylalanine were screened as potential biomarkers of childhood obesity by both ULDA and CCA (canonical correlation analysis). CCA also shows satisfactory correlation between the metabolic patterns and clinical parameters, and the results further suggest that WHR (waist-hip ratio) together with TG (total triglycerides), TC (total cholesterol), HDL (high density lipoprotein) and LDL (low density lipoprotein) were the most important parameters which are associated closely with the metabolic perturbations of childhood obesity, so as to be paid more attention for dealing with metabolic disturbances of childhood obesity in clinical practice rather than regularly monitored BMI (body-mass index). The results have demonstrated that the proposed metabolic fingerprinting approach may be a useful tool for discovering metabolic abnormalities and possible biomarkers for childhood obesity.

A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis.

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.

Comparison of the volatile constituents of different parts of Cortex magnolia officinalis by GC-MS combined with chemometric resolution method.

Volatile compositions of different parts (stem, branch and root barks) of cortex Magnolia officinalis, cultivated in China, were investigated for the first time by GC-MS with the help of heuristic evolving latent projection (HELP). Identification of components was conducted by similarity matching to NIST mass library but also assisted by comparison of temperature-programmed retention indices (PTRIs) with the data web available. A total of 90, 82 and 76 volatile compounds in the essential oils of the three samples taken from the same batch aforementioned were qualitatively and quantitatively determined, representing 84.03, 83.68 and 83.10% of the total content, respectively. Among the constituents determined, there were 50 components coexisting. Eudesmol and its isomers were shown to be the principal compounds in the studied samples, accounting for 47.66, 36.74 and 36.31%, respectively. The three kinds of isomers (alpha-, beta- and gamma-eudesmol) in houpo volatile oils have been tentatively qualified and quantified simultaneously for the first time. By comparative analysis, significant qualitative and semi-quantitative differences and similarities were observed among the three samples. The results achieved provide a scientific evidence for further exploitation of Magnolia bark and clinical medication.

Metabolic alterations of impaired fasting glucose by GC/MS based plasma metabolic profiling combined with chemometrics

In this paper, a new strategy for processing GC/MS based metabolic profiling data via multivariate methods was proposed, which is applied to a small pilot study of impaired fasting glucose. The data obtained from plasma samples of impaired fasting glucose patients and healthy controls were first treated by principal component analysis and partial least squares-discriminant analysis to explore the differences and discriminators of the two groups. Subsequently, correlation analyses were employed to examine the relationships between blood glucose and the discriminators or their linear combination, thus may be considered as potential biomarkers of impaired fasting glucose. The results showed that the metabolic patterns of the two groups were different. Furthermore, eleven metabolites were screened as discriminators. Levels of nine of the eleven discriminators, say lactate, 2-ketoisocaproic acid, alanine, α-hydroxyisobutyric acid, urea, phosphoric acid, α-glycerophosphoric acid, palmitic acid and stearic acid, were found to be significantly higher in impaired fasting glucose patients, while 1-monopalmitin and 1-monostearin showed the opposite trend. Correlation analysis indicated that 2-ketoisocaproic acid, stearic acid were positively, while 1-monopalmitin and 1-monostearin were negatively correlated with blood glucose. Moreover, blood glucose correlated well with the linear combination of the eleven discriminators by canonical correlation analysis. The results have demonstrated that 2-ketoisocaproic acid, stearic acid and the linear combination of the eleven discriminators may be considered as the potential biomarkers of impaired fasting glucose and the proposed method may be useful in a larger study for exploring the metabolic alterations and biomarker candidates of diseases.