A PCA is also capable of ranking variables/parameters (such as markers or cell counts) based on their contribution to the variability across a dataset in an extremely fast manner. For individuals (such as samples or patients), a PCA can group them based on their similarities. This function will run a PCA calculation (extremely fast) and generate plots (takes time). PCA is capable of reducing the number of dimensions (i.e. parameters) with minimal effect on the variation of the given dataset.
In this workflow, we provide an example on how to run and make sense of a principal component analysis (PCA).
