Graphic Depiction of Bioinformatics Data

Dimension reduction Dimension reduction techniques, such as principal components analysis or metric multidimensional scaling, can also be used to reduce high-dimensional data to its most salient features. [...]interactive data visualization has become a compelling complement to all types of static visualization. An interactive graphic that starts with a summary display (overview) and then allows for the removal of uninteresting items (filtering) while focusing on data points and trends of interest (zooming and details-on-demand) gives researchers the ability to investigate complex data with intuitive tools.11 This pattern mirrors biomedical research in general and scales well to bioinformatics data. [...]recently, specialized technical knowledge was needed to configure interactive graphics for bioinformatics data, but many standalone packages that provide simple ways to configure and share sophisticated interactive graphics have been released in recent years, significantly lowering the bar for entry (see Table E1 in this article's Online Repository at www.jacionline.org). Supplementary data 1 X. Wang, C. Baumgartner, D.C. Shields, H.W. Deng, J.S. Beckmann, Application of clinical bioinformatics, 2016, Springer, New York 2 S.Q. Ye, Big data analysis for bioinformatics and biomedical discoveries, 2015, Boca Raton (FL) 3 A.W. Burks, S.M. Jones, R.A. Wood, D.M. Fleischer, S.H. Sicherer, R.W. Lindblad, Oral immunotherapy for treatment of egg allergy in children, N Engl J Med, Vol. 367, 2012, 233-243 4 M. Friendly, Milestones in the history of data visualization: a case study in statistical historiography.