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Cells in an organism share the same genetic information in their DNA, but have very different forms and behavior becauseof the selective expression of subsets of their genes. The widely used approach of measuring gene expression over time froma tissue sample using techniques such as microarrays or sequencing do not provide information about the spatial position withinthe tissue where these genes are expressed. In contrast, we are working with biologists who use techniques that measure geneexpression in every individual cell of entire fruitfly embryos over an hour of their development, and do so for multiple closely-relatedsubspecies of Drosophila. These scientists are faced with the challenge of integrating temporal gene expression data with the spatiallocation of cells and, moreover, comparing this data across multiple related species. We have worked with these biologists overthe past two years to develop MulteeSum, a visualization system that supports inspection and curation of data sets showing geneexpression over time, in conjunction with the spatial location of the cells where the genes are expressed — it is the first tool tosupport comparisons across multiple such data sets. MulteeSum is part of a general and flexible framework we developed with ourcollaborators that is built around multiple summaries for each cell, allowing the biologists to explore the results of computations thatmix spatial information, gene expression measurements over time, and data from multiple related species or organisms. We justifyour design decisions based on specific descriptions of the analysis needs of our collaborators, and provide anecdotal evidence of theefficacy of MulteeSum through a series of case studies.
spatial data, temporal data, gene expression

T. Munzner, M. Meyer, A. DePace and H. Pfister, "MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 16, no. , pp. 908-917, 2010.
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