Random data set generation to support microarray analysis.
As microarray analyses become increasingly routine, involving the simultaneous investigation of huge numbers of genes, researchers can easily search for and uncover what appear to be promising patterns in their data. In such circumstances tools are needed to help decide the extent to which these patterns are meaningful or can be explained by chance alone. The purpose of this chapter is to describe examples of the use of microarray analysis for inferential purposes and how validation of inference is addressed by Monte-Carlo techniques, which essentially amounts to investigation of statistical methods on synthetic or random data sets.
Naiman
Daniel Q
DQ
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
D000818:Animals; D003627:Data Interpretation, Statistical; D006801:Humans; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*