High-Dimensional Data Analysis in Cancer Research (2009)

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    In an era with a plethora of high-throughput biological technologies, biomedical researchers are investigating more comprehensive aspects of cancer with ever-finer resolution. Not only does this result in large amount of data but also data with hundreds if not thousands of dimensions. Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples or patients and columns attributes, to certain response or outcome variables. Classically, the sample size n is much larger than the number of attributes p. The theoretical properties of statistical models have mostly been discussed under the assumption of fixed p and infinite n. However, the advance of biological sciences and technologies has revolutionized the process of investigations in cancer. The biomedical data collection has become much more automatic and much more extensive. We are in the era of p as a large fraction of n, or even much larger than n, which poses challenges to the classical statistical paradigm. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has mostly been a laborious process, carried out one protein at a time. The advent of highthroughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high-dimensional data.

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