Bioimage computing is rapidly emerging as an important area in image based systems biology with an emphasis on spatiotemporal localization of subcellular bio-molecules, most importantly proteins. A key problem in this domain is analysis of protein co-localization or co-expression of protein molecules. Imaging techniques, such as the Toponome Imaging System (TIS) [1], with the ability to localize several different proteins in the same tissue specimen are only becoming available recently. Traditional co-localization studies and some of the modern co-expression studies have serious limitations when analyzing this kind of data. Here we present a framework for the analysis of molecular co-expression patterns (MCEPs) in TIS image data.