Developmental differences between species commonly result from changes in the tissue-specific expression of genes. Clustering algorithms are a powerful means to detect co-expression across tissues in single species, but are not often applied to multi-dimensional datasets, such as gene expression across tissues in multiple species. As next-generation sequencing approaches enable interspecific analyses, methods to visualize and explore such datasets will be required. Here, we analyze a dataset comprising gene expression profiles across six different tissue types in domesticated tomato (Solanum lycopersicum) and a wild relative (S. pennellii). We find that Self-Organizing Maps (SOMs) are a useful means to analyze interspecies data, as orthologs can be assigned to independent levels of a “super SOM.” We compare various clustering approaches using a Principal Component Analysis (PCA) in which the expression of orthologous pairs is indicated by two points. We leverage the expression profile differences between orthologs to look at tissue-specific changes in gene expression between species. Clustering based on expression differences between species (rather than absolute expression profiles) yields groups of genes with large tissue by species interactions. The changes in expression profiles of genes we observe reflect differences in developmental architecture, such as changes in meristematic activity between domesticated tomato and S. pennellii. Together, our results offer a suite of data exploration methods that will be important to visualize and make biological sense of next-generation sequencing experiments designed explicitly to discover tissue by species interactions in gene expression data.