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Bioinformatics Advance Access published online on March 22, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm092
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Domain Enhanced Analysis of Microarray Data Using GO Annotations

Jiajun Liu 1,*, Jacqueline M. Hughes-Oliver 1 and J. Alan Menius, Jr. 2

1Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.,
2GlaxoSmithKline Research and Development, Research Triangle Park, NC 27709-3398, USA.

*To whom correspondence should be addressed. Jiajun Liu, E-mail: jliu6{at}ncsu.edu


  Abstract

Motivation: New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g., the Gene Ontology, to guide analysis of such data. By focusing on domain-aggregated results at, say the molecular function level, increased interpretability is available to biological scientists beyond what is possible if results are presented at the gene level.

Results: We use a "top-down" approach to perform domain aggregation by first combining gene expressions before testing for differentially expressed patterns. This is in contrast to the more standard "bottom-up" approach where genes are first tested individually then aggregated by domain knowledge. The benefits are greater sensitivity for detecting signals. Our method, domain enhanced analysis (DEA) is assessed and compared to other methods using simulation studies and analysis of two publicly available leukemia data sets. Availability: Our DEA method uses functions available in R (http://www.r-project.org/) and SAS (http://www.sas.com/). The two experimental data sets used in our analysis are available in R as Bioconductor packages, "ALL" and "golubEsets" (http://www.bioconductor.org/).

Supplementary Information: Available at Bioinformatics online.

Associate Editor: Dr. Trey Ideker

Received on October 10, 2006; revised on January 10, 2007; accepted on March 5, 2007

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