Principal Component Analysis (PCA) is a statistical technique used to simplify the complexity in high-dimensional data while retaining trends and patterns. It transforms the data into a new coordinate system such that the greatest variances are projected onto new axes called principal components. This method is widely used in cancer research for dimensionality reduction, visualization, and identifying underlying patterns.
