Statistical methods for gene expression analysis
Part 4. Bioinformatics
4.5. Computational Methods for High-throughput Genetic Analysis: Expression Profiling
Published Online: 15 NOV 2005
Copyright © 2005 John Wiley & Sons, Ltd
Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics
How to Cite
Deng, S., Chu, T.-M., Truong, Y. K. and Wolfinger, R. D. 2005. Statistical methods for gene expression analysis. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:53.
- Published Online: 15 NOV 2005
Microarray technology makes it possible to simultaneously study the expression levels from thousands of genes and is widely used in functional genomics research. The analysis of microarray data provides a challenge to researchers because of its high dimensionality and complexity. Extensive research has been devoted recently to address the statistical issues raised from the analysis of the microarray data, and a comprehensive review is not possible here given space limitations and the proliferation of new papers. Instead, this chapter reviews some of the more basic statistical methods used for microarray gene expression data analysis. We divide the methods into three major areas: differential expression, classification, and clustering.
- gene expression;
- differential expression;