Cellular microarrays are a combination of well plate and microarray technologies that create an efficient high-throughput method to quantitate the phenotypes of cells in response to perturbations such as microenvironments, RNAi, and drugs. This thesis covers the development and use of an extensible computational pipeline to process content-screening data from cellular microarrays. Extensive Exploratory Data Analysis is performed to identify technical variations and inform normalizations that filter for biological variations. Robust rank product scoring combines replicate and channel data to prioritize hits for different biological questions. The pipeline has been applied to Cell Spot Microarrays and MEArray datasets, uncovering experiment design issues and generating hit lists for validation. A method to generate simulated datasets with variations that mimic those in actual microarrays has been developed and used to optimize the pipeline and predict the results for different experiment designs.