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 of samples, to some response
variables, e.g., patients outcome. Classically, the sample size n
is much larger than p, the number of variables. The properties of
statistical models have been mostly discussed under the assumption
of fixed p and infinite n. The advance of biological sciences and
technologies has revolutionized the process of investigations of
cancer. The biomedical data collection has become more automatic
and more extensive. We are in the era of p as a large fraction of
n, and even much larger than n. 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 been
mostly a laborious process, carried out one protein at a time. The
advent of high throughput 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. In this volume, we will
present the systematic and analytical approaches and strategies
from both biostatistics and bioinformatics to the analysis of
correlated and high-dimensional data.
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