Cell Biology
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of large datasets while preserving as much variance as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA simplifies complex data structures, making it easier to visualize and analyze. This method is particularly important in fields like proteomics and genomics, where high-dimensional data is common.
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