Performing a PCA

PCA is a statistical procedure that’s used to perform a reduction of the dimension of a number of variables to a smaller subset that is linearly uncorrelated. Its practical application in population genetics is assisting with the visualization of the relationships between the individuals that are being studied.

While most of the recipes in this chapter make use of Python as a glue language (Python calls external applications that actually do most of the work), with PCA, we have an option: we can either use an external application (for example, EIGENSOFT SmartPCA) or use scikit-learn and perform everything on Python. In this recipe, we will use SmartPCA – for a native machine learning experience with scikit-learn, see Chapter 10.

TIP

You actually have a third option: using sgkit. However, I want to show you alternatives on how to perform computations. There are two good reasons for this. Firstly, you might prefer not to use sgkit – while...