Using eigenanalysis to classify proteins and protein motifs

Eigenanalyis is a common name for linear algebra based multivariate analysis procedures like Principal Components Analysis (PCA), Correspondence Analysis (CA), Factor Analysis (FA), etc. The common idea in those methods is to compute eigenvalues and corresponding eigenvectors of a real symmetric matrix. The orthogonality of eigenvectors insures that the information contained in one vector is excluded from all other vectors and provides the basis for ordaining and filtering the information from original data set. We applied this methodology and freely accessible sequence information in open access biological data bases to classify proteins and their motifs in variety of situations like families of functionally related proteins, classifying functionally unknown proteins and/or finding new member of a known protein family. The performance of proposed methodology is illustrated on the analysis of nuclear receptor proteins family.