Recent technology platforms in proteomics and genomics produce count data for quantitative analysis. Previous works on statistical significance analysis for count data have mainly focused on the independent sample setting, which does not cover the case where pairs of measurements are taken from individual patients before and after treatment. This experimental setting requires paired sample testing such as the paired t-test often used for continuous measurements. A state-of-the-art method uses a negative binomial distribution in a generalized linear model framework for paired sample testing. A paired sample design assumes that the relative change within each pair is constant across biological samples. This model can be used as an approximation to the true model in cases of heterogeneity of response in complex biological systems. We aim to specify the variation in response explicitly in combination with the inherent technical variation.