The vast explosion of the amount of information generated by clinical researchers through analysis of genome, transcriptome, and proteome data have outpaced the speed with which these data can be analyzed and understood. New software tools are required to transform these gigabytes of data into knowledge towards improving patients' outcomes. Because of the ability of the visual system to process enormous amounts of data in a very short time, advanced visualization techniques that make sense of large-scale complex datasets are developed in many different scientific areas. The adaptation of visualization tools, such as parallel coordinates, matrix methods, and multilayer graphics to molecular and clinical data allows clinical researchers to get an overview of the data, to extract patterns, and to highlight important features in these large datasets. Linking of multiple visualization tools allows the clinical researcher to look at the data from multiple viewpoints and simultaneously extract features of the data that exist across multiple dimensions. These visualization techniques dramatically speed up the analysis of large datasets and provide results that could not have been reached by researchers using conventional methods.