The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

High-dimensional genomic and proteomic data are now commonplace in cancer research. This Review aims to help biologists understand the properties of high-dimensional data spaces and how these affect our ability to derive meaningful information from the data. High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.