Abstract
Exploring biological meaning from microarray data is very important but remains a great challenge. Here, we developed three new statistics: linear combination test, quadratic test and de-correlation test to identify differentially expressed pathways from gene expression profile. We apply our statistics to two rheumatoid arthritis datasets. Notably, our results reveal three significant pathways and 275 genes in common in two datasets. The pathways we found are meaningful to uncover the disease mechanisms of rheumatoid arthritis, which implies that our statistics are a powerful tool in functional analysis of gene expression data.
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Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005)
Wang, K., Li, M., Bucan, M.: Pathway-Based Approaches for Analysis of Genome-wide Association Studies. Am. J. Hum. Genet. 81, 1278–1283 (2007)
Gao, S., Wang, X.: TAPPA: topological analysis of pathway phenotype association. Bioinformatics 23, 3100–3102 (2007)
Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/
Barnes, M.G., Aronow, B.J., Luyrink, L.K., Moroldo, M.B., et al.: Gene expression in juvenile arthritis and spondyloarthropathy: pro-angiogenic ELR+ chemokine genes relate to course of arthritis. Rheumatology (Oxford) 43(8), 973–979 (2004)
Stanford MicroArray Database, http://genome-www5.stanford.edu/
van der Pouw Kraan, T.C., Wijbrandts, C.A., van Baarsen, L.G., Voskuyl, A.E., Rustenburg, F., Baggen, J.M., Ibrahim, S.M., Fero, M., Dijkmans, B.A., Tak, P.P., Verweij, C.L.: Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patients. Ann. Rheum. Dis. 66(8), 1008–1014 (2007)
KEGG (Kyoto Encyclopedia of Genes and Genomes), http://www.genome.jp/kegg
BioCarta, http://www.biocarta.com
Benfey, P.N., Mitchell-Olds, T.: From genotype to phenotype: systems biology meets natural variation. Science 320, 495–497 (2008)
Curtis, R.K., Oresic, M., Vidal-Puig, A.: Pathways to the analysis of microarray data. Trends Biotechnol 23, 429–435 (2005)
Werner, T.: Bioinformatics applications for pathway analysis of microarray data. Curr. Opin. Biotechnol. 19(1), 50–54 (2008)
Curtis, R.K., Oresic, M., Vidal-Puig, A.: Pathways to the analysis of microarray data. Trends Biotechnol. 23(8), 429–435 (2005)
Adewale, A.J., Dinu, I., Potter, J.D., Liu, Q., Yasui, Y.: Pathway analysis of microarray data via regression. J. Comput. Biol. 15(3), 269–277 (2008)
Olsen, N., Sokka, T., Seehorn, C.L., Kraft, B., Maas, K., Moore, J., Aune, T.M.: A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells. Annals of the Rheumatic Diseases 63, 1387–1392 (2004)
Szodoray, P., Alex, P., Frank, M.B., Turner, M., Turner, S., Knowlton, N., Cadwell, C., Dozmorov, I., Tang, Y., Wilson, P.C., Jonsson, R., Centola, M.: A genome-scale assessment of peripheral blood B-cell molecular homeostasis in patients with rheumatoid arthritis. Rheumatology 45, 1466–1476 (2006)
Chang, M., Rowland, C.M., Garcia, V.E., Schrodi, S.J., Catanese, J.J., van der Helm-van Mil, A.H., Ardlie, K.G., Amos, C.I., Criswell, L.A., Kastner, D.L., Gregersen, P.K., Kurreeman, F.A., Toes, R.E., Huizinga, T.W., Seldin, M.F., Begovich, A.B.: A large-scale rheumatoid arthritis genetic study identifies association at chromosome 9q33.2. PLoS Genet. 27, 4(6), e1000107 (2008)
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Siu, H., Dong, H., Jin, L., Xiong, M. (2009). New Statistics for Testing Differential Expression of Pathways from Microarray Data. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_26
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DOI: https://doi.org/10.1007/978-3-642-02466-5_26
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