Multiplexed immunofluorescence imaging has made it possible to image tissue samples at a high spatial and marker resolution. Because of the field’s recent emergence, there are few rigorously evaluated contributions to the analysis pipeline. In this talk, I introduce the format of these high-dimensional imaging data and describe two statistical methods we have developed to perform image normalization and cell phenotyping, two critical steps in the processing pipeline. For image normalization, we introduce several new metrics that we use to evaluate 12 methods for normalization and data transformation of multiplexed imaging data. This approach identifies several effective methods that we now apply as a standard before downstream analyses. These normalized data are then be submitted to a single-cell phenotyping algorithm that is in active development. I will discuss a closed-form gamma mixture model that we recently developed that forms the backbone of our phenotyping algorithm.