Metabolomics - Statistical Methods

Statistical Methods

The data generated in metabolomics usually consist of measurements performed on subjects under various conditions. These measurements may be digitized spectra, or a list of metabolite levels. In its simplest form this generates a matrix with rows corresponding to subjects and columns corresponding to metabolite levels. Several statistical programs are currently available for analysis of both NMR and mass spectrometry data. For mass spectrometry data, software is available that identifies molecules that vary in subject groups on the basis of mass and sometimes retention time depending on the experimental design. The first comprehensive software to analyze global mass spectrometry-based metabolomics datasets was developed by the Siuzdak laboratory at The Scripps Research Institute in 2006. This software, called XCMS, is freely available, has over 20,000 downloads since its inception in 2006, and is one of the most widely cited mass spectrometry-based metabolomics software programs in scientific literature. Other popular metabolomics programs for mass spectral analysis are MZmine, MetAlign, MathDAMP, which also compensate for retention time deviation during sample analysis. LCMStats is another R package for detailed analysis of liquid chromatography mass spectrometry(LCMS)data and is helpful in identification of co-eluting ions especially isotopologues from a complicated metabolic profile. It combines xcms package functions and can be used to apply many statistical functions for correcting detector saturation using coates correction and creating heat plots. Metabolomics data may also be analyzed by statistical projection (chemometrics) methods such as principal components analysis and partial least squares regression.