Analysis of similarity of electrophoretic patterns in mRNA differential display

We present a novel method of statistical analysis for the comparison of ele ctrophoretic data. The method is based on the squared Euclidian distance of normalized signal data vectors of electrophoretic lanes. The differences i n the electrophoretic patterns are evaluated by a statistical test based on Hubert's statistics which measures the significance of the signal grouping . We demonstrate the validity and applicability of the method in a large da ta set derived from automated fluorescent mRNA differential display analysi s of the expression of acute-phase proteins during experimental Escherichia coli infection in mice. The current testing method is capable of finding t heoretically similar natural groupings to be similar in a statistically sig nificant way whereas theoretically dissimilar or random groupings can be re cognized to be artifactual. We also show how the calculated pairwise signal distances can be utilized in methodological problem solving. These analyti cal methods can be applied to the study of other related problems of simila rity analysis of electrophoretic patterns, and also provide useful tools fo r the development of automated recognition of differentially expressed mRNA s.