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Main target of metabolomics research is to reveal informative metabolites or biomarkers, which can be considered as a process of variable selection. So far, several methods, such as regression coefficient (RC), weights or variable importance in projection (VIP), have been widely used to assess the variable importance when building the partial least squares linear discriminant analysis PLS-LDA classification model. Then a set of metabolites can be selected by fixing a threshold value considering the rank of metabolites. However, they do not take into account the combination effect among a subset of variables, which will lead to bias within the results. In this work, a strategy named as variable importance analysis based on random variable combination (VIAVC), is developed for statistical assessment of variable importance. The framework of VIAVC includes mainly three parts: (1) employ a novel variables sampling method, called binary matrix resampling, which can guarantee that each variable has been selected with the same probability and generate a population of different variable combinations; (2) the importance of each variable is assessed by percent decrease or increase of the area under the receiver operating characteristic curve when the variable is excluded for the modeling by PLS-LDA; (3) iteratively retain and output the rank of the final remaining informative variables. The results of the applications to three metabolic datasets illustrate that VIAVC has better performance compared with other methods including RC, VIP and subwindow permutation analysis. The MATLAB code for implementing VIAVC is available in the supplemental materials. [ABSTRACT FROM AUTHOR]

Abstract: The importance of 5′AMP-activated protein kinase (AMPK) in regulating glucose and fatty acid metabolism is increasing. Thus, it is regarded as a new pharmacological target for treatment of obesity, insulin resistance and type 2 diabetes mellitus (T2DM). In order to explore the relationships between AMPK and diabetes mellitus, urines samples from four groups of C57 mice, i.e., the normal male and female C57 mice, female C57-AMPK gene knocked-out mice, and male C57-AMPK gene knocked-out mice, were studied by coupling GC/MS with a powerful machine learning method, random forest. The experimentation has been designed as two steps: firstly, the normal male and female mice were compared with male and female C57-AMPK gene knocked-out mice, respectively; then the differences between male C57-AMPK gene knocked-out mice and female C57-AMPK gene knocked-out mice were further detected. Finally, not only the differences between the normal C57 mice and C57-AMPK gene knocked-out mice were observed, but also the gender-related metabolites differences of the C57-AMPK gene knocked-out mice were obviously visualized. The results obtained with this research demonstrate that combining GC/MS profiling with random forest is a useful approach to analyze metabolites and to screen the potential biomarkers for exploring the relationships between AMPK and diabetes mellitus. [Copyright &y& Elsevier]

Abstract: One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex ‘omics’ data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets. According to the contribution to the classification accuracy, the metabolites were classified into three kinds: informative, no-informative, and interfering metabolites. Based on the proposed method, some informative metabolites were selected for three datasets; further analyses of these metabolites between healthy and diseased groups were then performed, showing by T-test that the P values for all these selected metabolites were lower than 0.05. Moreover, the informative metabolites identified by the current method were demonstrated to be correlated with the clinical outcome under investigation. The source codes of MPA-RF in Matlab can be freely downloaded from http://code.google.com/p/my-research-list/downloads/list [Copyright &y& Elsevier]