A review of statistical methods for preprocessing oligonucleotide microarrays.
Microarrays have become an indispensable tool in biomedical research. This powerful technology not only makes it possible to quantify a large number of nucleic acid molecules simultaneously, but also produces data with many sources of noise. A number of preprocessing steps are therefore necessary to convert the raw data, usually in the form of hybridisation images, to measures of biological meaning that can be used in further statistical analysis. Preprocessing of oligonucleotide arrays includes image processing, background adjustment, data normalisation/transformation and sometimes summarisation when multiple probes are used to target one genomic unit. In this article, we review the issues encountered in each preprocessing step and introduce the statistical models and methods in preprocessing.
Wu
Zhijin
Z
Center for Statistical Sciences and Department of Community Health, Brown University, RI 02912, USA. zhijin_wu@brown.edu
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*