ABSTRACT
In this chapter, we introduce principal component analysis (PCA), one of the most established data-driven modeling methods, and one that has been used to gain novel biological insights from large immunological data sets. In addition, we will also discuss two useful variations of PCA, partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLSR). PLSDA and PLSR not only aid in condensing and visualizing large data sets but also allow for data prediction.
