Muscular dystrophy is a degenerative disease that destroys muscles and ultimately kills its victims. Researchers worldwide are racing to find a cure by trying to uncover the genetic processes that cause it. Given that a key process is muscle development, researchers at a consortium of 10 institutions are studying 1,000 men and women, ages 18 to 40 years, to see how their muscles enlarge with exercise. The 150 variables collected for each participant will make this data analysis task challenging for users of traditional statistical software tools. However, a new approach to visual data analysis is helping these researchers speed up their work. At the University of Maryland's Human-Computer Interaction Library, we developed an interactive approach to let researchers explore high-dimensional data in an orderly manner, focusing on specific features one at a time. The rank-by-feature framework lets them adjust controls to specify what they're looking for, and then, with only a glance, they can spot strong relationships among variables, find tight data clusters, or identify unexpected gaps. Sometimes surprising outliers invite further study as to whether they represent errors or an unusual outcome. Similar data analysis problems come up in meteorology, finance, chemistry, and other sciences in which complex relationships among many variables govern outcomes. The rank-by-feature framework could be helpful to many researchers, engineers, and managers because they can then steer their analyses toward the action