Visualization is essential to genomic data analysis. We distinguish among three main scenarios, each having different requirements. The first is rapid interactive data exploration in “discovery mode”. The second is the recording, reporting and discussion of initial results among research collaborators, often done via web pages with interlinked plots and tool-tips providing interactive functionality. Scripts are often provided alongside to document what was done. The third is graphics for scientific publications and presentations that show essential messages in intuitive and attractive forms. The R environment offers powerful support for all these flavors of visualization—using either the various R graphics devices or HTML5-based visualization interfaces that offer more interactivity—and Bioconductor fully exploits these facilities. Visualization in practice often requires that users perform computations on the data, for instance, data transformation and filtering, summarization and dimension reduction, or fitting of a statistical model. The needed expressivity is not always easy to achieve in a point-and-click interface but is readily realized in a high-level programming language. Moreover, many visualizations, such as heat maps or principal-component analysis plots, are linked to mathematical and statistical models—for which access to a scientific computing library is needed.
