Domain-enhanced analysis of microarray data using GO annotations.
BACKGROUND
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.


BACKGROUND
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/).


BACKGROUND
Supplementary data are available at Bioinformatics online.
Liu
Jiajun
J
Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA. jliu6@stat.ncsu.edu
Hughes-Oliver
Jacqueline M
JM
Menius
J Alan
JA
D002051:Burkitt Lymphoma / Q000235:genetics*; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D015459:Leukemia-Lymphoma, Adult T-Cell / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D012680:Sensitivity and Specificity; D012984:Software