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.