3/20/2023 0 Comments Voting blocs definition ap govDespite its widespread use in metabolomics, PCA has several shortcomings. The popularity of PCA in metabolomics is due to the fact that it is a simple non-parametric method which can project the NMR or MS spectra into lower dimensional space, revealing inherent data structure, and providing a reduced dimensional representation of the original data. Principal components analysis (PCA) is probably the most widely used technique for analyzing metabolomic data. In view of these difficulties, when analyzing metabolomic data there is a genuine need for multivariate dimension reducing techniques which can take into account the complexities of the data and expose any underlying relationships. Secondly, many metabolites may not have any relationship with the trait under study and they can induce variation which is not relevant, hampering comprehensive data analysis. In such a situation, the application of standard parametric statistical methods such as regression is not straight forward as there are insufficient data for parameter estimation. Firstly, the number of observations n in metabolomics experiments is typically much less than the number of peaks (or variables) p in a spectrum, n ≪ p. Typically, the data generated by these methods are large and complex. With respect to NMR-based metabolomics the data are usually in the form of spectra which are binned into regions of a specified width. The most commonly applied analytical technologies to metabolomic studies are nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS). Studies of the concentration levels of these molecules in biological samples aim to enhance understanding of the effect of a particular stimulus or treatment. Examples of such metabolites include lipids, amino acids, bile acids, keto-acids. Metabolomics is the term used to describe the study of small molecules or metabolites present in biological samples.
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