Analysis of scaling methods to minimize experimental variations in two-dimensional electrophoresis quantitative data: application to the comparison of maize inbred lines.

The analysis of two-dimensional (2-D) electrophoresis quantitative data from a design involving 21 maize genotypes revealed a significant experimental variation. In order to minimize this variation, we investigated the possible causes and found that it was essentially due to global effects, affecting all the spots in a gel in a similar way, and occurring during the 2-D run/staining procedure. Three scaling methods to discard these experimental variations were analyzed: the linear scaling method, a method based on principal component analysis, and a combined method that unites the advantages of both of the former. Comparing these three methods, we found that they led to consistent results with regard to the factor under study, i.e. the genetic factor in our case. However, the combined scaling method was the most efficient in reducing experimental variations.