Clustering of Multidimensional Data Sets with Applications to Spatial Distributions of Ribosomal Proteins

Consider ribosomal proteins, each with a three-dimensional spatial location. Proteins related to the cofactor phenotype may be randomly or non-randomly distributed within the ribosome. To investigate this question, the Mahalanobis distance is computed between each pair of protein locations, and the optimal pairing is determined by minimizing the sum of the within-pair distances. Since no single code exists that allows for the computation of Mahalanobis distances, determining the optimal pairing, and determining whether the two groups are statistically different, we created a code that allows a user to do just this. The user can also compute an exact p-value for this distribution rather than rely on an approximation.