Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations, in this case cytometry based events, into new variables called principal components. The transformation is defined in such a way that the first two principal components generally define the maximum variance while each succeeding component maximizes the variance at a 90 degree rotation. Principal components defined and applied to a plot in FCS Express are accessible for analysis as new parameters on plots.

 

To begin working with PCA analysis in FCS Express please see the topics on:

 

•Defining a PCA Transformation

•Applying a PCA Transformation