Nanostring technology provides a new method to measure gene expressions. It\u27s more sensitive than microarrays and able to do more gene measurements than RT-PCR with similar sensitivity. This system produces counts for each target gene and tabulates them. Counts can be normalized by using an Excel macro or nSolver before analysis. Both methods rely on data normalization prior to statistical analysis to identify differentially expressed genes. Alternatively, we propose to model gene expressions as a function of positive controls and reference gene measurements. Simulations and examples are used to compare this model with Nanostring normalization methods. The results show that our model is more stable, efficient, and able to control false positive proportions. In addition, we also derive asymptotic properties of a normalized test of control versus treatment
