State-of-the-art of Cluster Analysis of Gene Expression Data

2009 ◽  
Vol 34 (2) ◽  
pp. 113-120 ◽  
Author(s):  
Feng YUE
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Gene Expression Data ◽  
State Of The Art ◽  
Expression Data

scholarly journals Pattern Discovery in Gene Expression Data

2009 ◽  
pp. 45-64
Author(s):  
Gráinne Kerr ◽  
Heather Ruskin ◽  
Martin Crane
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Cluster Analysis ◽  
Gene Regulation ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Mrna Levels ◽  
Expression Data ◽  
Single Experiment ◽  
Gene Regulation Networks

Microarray technology1 provides an opportunity to monitor mRNA levels of expression of thousands of genes simultaneously in a single experiment. The enormous amount of data produced by this high throughput approach presents a challenge for data analysis: to extract meaningful patterns, to evaluate its quality, and to interpret the results. The most commonly used method of identifying such patterns is cluster analysis. Common and sufficient approaches to many data-mining problems, for example, Hierarchical, K-means, do not address well the properties of “typical” gene expression data and fail, in significant ways, to account for its profile. This chapter clarifies some of the issues and provides a framework to evaluate clustering in gene expression analysis. Methods are categorised explicitly in the context of application to data of this type, providing a basis for reverse engineering of gene regulation networks. Finally, areas for possible future development are highlighted.

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