Principal component analysis (PCA) is a linear combination of all the metabolites originally identified to form a new group of comprehensive variables. At the same time, 2-3 comprehensive variables are selected according to the problems analyzed to make them reflect the information of the original variables as much as possible, to achieve the purpose of dimensionality reduction. The PCA on metabolites can reflect the degree of variation between and within groups as a whole. PCA of population samples observes the overall distribution trend among all groups of samples, finds out the possible discrete samples, and comprehensively considers various factors (sample number, sample precious degree, discrete degree) to determine whether the discrete points are removed or not.
CD Mitochondria uses XCMS software to extract the ion peaks of metabolites. The peaks extracted from experimental samples and QC samples are normalized and analyzed by PCA, and the experimental results with good stability and reliable data are obtained. The differences in the metabolic spectrum obtained in the experiment can reflect the biological differences among samples.
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