Manipulating Large-Scale Arabidopsis Microarray Expression Data: Identifying Dominant Expression Patterns and Biological Process Enrichment

Department of Biology, Duke University, Durham, NC, USA.
Methods in molecular biology (Clifton, N.J.) (Impact Factor: 1.29). 02/2009; 553:57-77. DOI: 10.1007/978-1-60327-563-7_4
Source: PubMed


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.

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    Physiologia Plantarum 06/2015; 155(1). DOI:10.1111/ppl.12357 · 3.14 Impact Factor
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    • "Branch length distribution of the HCL tree and the figure of merit (FOM) of iterative K-means clustering runs were used to gauge the expected number of clusters (Multiple Experiment Viewer). A Fuzzy K-means clustering search for dominant expression patterns was executed employing the R script by Orlando and co-workers for the manipulation of large-scale Arabidopsis microarray data sets (Orlando et al, 2009 "
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    Molecular Systems Biology 09/2013; 9(1):688. DOI:10.1038/msb.2013.40 · 10.87 Impact Factor
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    • "Organisms respond to external cues (biological perturbations) by synchronized changes in the expression levels of multiple genes, which together integrate into specific phenotypic outputs. The development of microarray technology, together with the development of a variety of bioinformatics approaches, has enabled the analysis of the simultaneous response of gene networks to various developmental, physiological, or external cues at the systems biology level (Loraine, 2009; Orlando et al., 2009; Sreenivasulu et al., 2010). As sessile organisms, plants adjust to environmental stresses through highly compound changes in gene expression programs. "
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