Manipulating large-scale Arabidopsis microarray expression data: identifying dominant expression patterns and biological process enrichment

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 553; p. 57
• Main Authors: Orlando, David A, Brady, Siobhan M, Koch, Jeremy D, Dinneny, José R, Benfey, Philip N
• Format: Journal Article
• Language: English
• Published: United States 2009
• Subjects: Arabidopsis - genetics; Arabidopsis - metabolism; Automatic Data Processing - methods; Cluster Analysis; Computational Biology - methods; Gene Expression Profiling - methods; Gene Expression Regulation, Plant; Genes, Plant; Multigene Family - genetics; Multigene Family - physiology; Oligonucleotide Array Sequence Analysis - methods; Plant Proteins - genetics; Plant Proteins - metabolism; Up-Regulation - genetics; Up-Regulation - physiology
• ISSN: 1940-6029
• DOI: 10.1007/978-1-60327-563-7_4

A series of large-scale Arabidopsis thaliana microarray expression experiments profiling genome-wide expression across different developmental stages, cell types, and environmental conditions have resulted in tremendous amounts of gene expression data. This gene expression is the output of complex transcriptional regulatory networks and provides a starting point for identifying the dominant transcriptional regulatory modules acting within the plant. Highly co-expressed groups of genes are likely to be regulated by similar transcription factors. Therefore, finding these co-expressed groups can reduce the dimensionality of complex expression data into a set of dominant transcriptional regulatory modules. Determining the biological significance of these patterns is an informatics challenge and has required the development of new methods. Using these new methods we can begin to understand the biological information contained within large-scale expression data sets.