Final answer:
Principal Component Analysis (PCA) is used to reduce the dimensionality of complex data sets, retain important variation, and simplify analysis. It enables the visualization of data patterns and improves the efficiency of predictive models through the elimination of redundant information.
Step-by-step explanation:
The aim of Principal Component Analysis (PCA) is to reduce the dimensionality of a data set consisting of a large number of interrelated variables while retaining as much as possible of the variation present in the data set. This is achieved by transforming the original variables into a new set of variables, the principal components, which are uncorrelated and are ordered so that the first few retain most of the variation present in all of the original variables.
PCA is good for visualizing genetic distance and relatedness between populations, simplifying data to explore and visualize trends, making predictive models more efficient by eliminating noise or redundancy in the data, and identifying patterns in measurements, as seen in the example of the proteome analysis from Al-treated tomato radicle. In this case, PCA helps to identify protein samples that cluster together based on the distribution of log2 fold change values of peptides, indicating similar responses between biological samples.