Principal Component Analysis (PCA) is a technology for simplified analysis of data sets. PCA uses variance decomposition to reduce the dimensionality of multi-dimensional data, remove noise and redundancy, and reveal the most important elements and structures hidden behind complex data. In the field of life sciences, in proteomics and metabolomics research applications, it is usually necessary...
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