Cancer datasets are often high-dimensional, containing thousands of gene expression profiles, protein levels, or other molecular measurements. PCA helps to reduce the dimensionality of these datasets, making it easier to visualize and analyze the data. This is crucial for identifying biomarkers, understanding tumor heterogeneity, and predicting patient outcomes.
