Abstract
Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises two challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure performs best when considering both accuracy and speed. We applied the method to a proteomics data set from acute myeloid leukemia patients. Proteins in the apoptosis pathway could be explained using six PCs. By clustering the proteins in PC space, we were able to replace the PCs by six “biological components”, three of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable.
