Modeling biological variability in 2-D gel proteomic carcinogenesis experiments.

We propose a statistical method to model the underlying distribution of protein spot volumes in 2-D gels using a generalized model (GM). We apply this approach to discover mechanisms of chemical carcinogenesis in a rodent model. We generated 247 protein spots that were common to all gels (n = 18). Traditional statistical methods found 6.5% (13 out of 247) significant protein spots, our GM approach yielded a total of 53 (22.5%) differentially expressed protein spots.