Characterizing Cancer Subtypes Using Dual Analysis in Caleydo StratomeX

TLDR: Dual analysis uses statistics to describe both the dimensions and rows of a high-dimensional dataset and lets analysts create well-defined subtypes based on statistical properties, thus letting analysts characterize subtypes.

Abstract: Dual analysis uses statistics to describe both the dimensions and rows of a high-dimensional dataset. Researchers have integrated it into StratomeX, a Caleydo view for cancer subtype analysis. In addition, significant-difference plots show the elements of a candidate subtype that differ significantly from other subtypes, thus letting analysts characterize subtypes. Analysts can also investigate how data samples relate to their assigned subtype and other groups. This approach lets them create well-defined subtypes based on statistical properties. Three case studies demonstrate the approach's utility, showing how it reproduced findings from a published subtype characterization.