Microarray data normalization and transformation.
Underlying every microarray experiment is an experimental question that one would like to address. Finding a useful and satisfactory answer relies on careful experimental design and the use of a variety of data-mining tools to explore the relationships between genes or reveal patterns of expression. While other sections of this issue deal with these lofty issues, this review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.
Quackenbush
John
J
The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, Maryland 20850, USA. johnq@tigr.org
D000818:Animals; D003627:Data Interpretation, Statistical; D005544:Forecasting; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012107:Research Design