View - ResearchGate

Association Analysis for Large-Scale Gene Set Data 31genome is used as a reference list, but the data set is derived from a subset of thehuman genome, then the different ontological distribution will skew the statisticalanalysis. One safeguard is to always use the reference set for the microarray usedto generate the expression data (if this is a gene expression study). If this chip isnot available a request can be sent for it to be added, or upload it on own. It is usefulto pick a convenient name, such as mychip_reference. Try not to include spacesand special symbols in the gene set name. Occasionally this will lead to a problem.Another option one can change is the statistical method used to analyze thedata. Currently there are two options: Fisher and hypergeometric tests.8. GOTree module visualization options: DAG and bar chart views are graphic interchangeformat (GIF) interactive pictures. Unlike DAG, the bar chart will workonly at one of the GOtree structure levels in one of the three main branches. Bydefault WebGestalt chooses level 4 in Biological processes.9. Tissue enrichment: the gene expression profile is derived from CGAP publiclyavailable data. A gene was considered for inclusion in the enriched set if it wasoverrepresented with p < 0.01 (with Bonferroni correction).AcknowledgmentWe would like to thank Suzanne Baktash for the technical help she providedin preparing this manuscript.References1. Schena, M., Shalon, D., Davis, R. W., and Brown, P. O. (1995) Quantitative monitoringof gene expression patterns with a complementary DNA microarray.Science. 270(5235), 467–470.2. Stoughton, R. B. (2005) Applications of DNA microarrays in biology. Annu. Rev.Biochem. 74, 53–82.3. Ashburner, M., Ball, C. A., Blake, J. A., et al. (2000) Gene ontology: tool for theunification of biology. The Gene Ontology Consortium. Nat. Genet. 25(1), 25–29.4. Bono, H., Nikaido, I., Kasukawa, T., Hayashizaki, Y., and Okazaki, Y. (2003)Comprehensive analysis of the mouse metabolome based on the transcriptome.Genome Res. 13(6B), 1345–1349.5. Kanehisa, M., Goto, S., Kawashima, S., Okunu, Y., and Hattori, M. (2004) TheKEGG resource for deciphering the genome. Nucleic Acids Res. 32(Databaseissue), D277–D280.6. Lin, B., White, J. T., Lu, W., et al. (2005) Evidence for the presence of diseaseperturbednetworks in prostate cancer cells by genomic and proteomic analyses:a systems approach to disease. Cancer Res. 65(8), 3081–3091.7. Kluger, Y., Tuck, D. P., Chang, J. T., et al. (2004) Lineage specificity of geneexpression patterns. Proc. Natl. Acad. Sci. USA 101(17), 6508–6513.8. Mi, H., Lazareva-Ulitsky, B., Loo, R., et al. (2005) The PANTHER database ofprotein families, subfamilies, functions and pathways. Nucleic Acids Res.33(Database issue), D284–D288.