Covering events in eigenimages of biomolecules

Multivariate statistical analysis of a large set of micrographs of biological macromolecules involves the computation of eigenimages representing principal features, on the basis of which similar views of the complexes can be grouped. It is not generally clear what these eigenimages represent physically and which ones should be used in the classification process. In this paper, hierarchical maximum entropy discretisation and event covering are used to (1) detect statistically significant relationships in the eigenimages, (2) select the most relevant eigenimages for classifying biomolecular projections, and (3) build a prototype of the biomolecular complex under study.