Principal Component Analysis (PCA) takes a data matrix of n objects by p variables, which may be correlated, and summarizes it by uncorrelated axes (principal components or principal axes) that are …

Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box.

Principal Component Analysis, or simply PCA, is a statistical procedure concerned with elucidating the covari-ance structure of a set of variables. In particular it allows us to identify the principal directions …

Summary PCA detects genetic structures in a sample of genomes. PCA is agnostic to the structure detected, which makes interpretation challenging. The type of structure depends on the set of …

Principal component analysis (PCA) is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components.

In this vignette we’ll walk through the computational and mathematical steps needed to carry out PCA. If you are not familiar with PCA from a conceptual point of view, we strongly recommend you read the …

PCA is a powerful tool for dimensionality reduction and visualization. By identifying directions of maximum variance, PCA helps capture the essence of the data in a smaller number of dimensions, …