A topology-based score for pathway enrichment

Journal of Computational Biology

Volumn 19, Issue 5, 2012, Pages 563-573

  Ibrahim, Maysson Al Haj ^a   Jassim, Sabah ^a   Cawthorne, Michael Anthony ^a   Langlands, Kenneth ^a  

^a UNIVERSITY OF BUCKINGHAM   (United Kingdom)

Author keywords

depth first algorithms; gene expression; microarray

Indexed keywords

ACUTE GRANULOCYTIC LEUKEMIA; ADIPOCYTE; ALGORITHM; ANIMAL; ARTICLE; COLORECTAL TUMOR; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETIC DATABASE; GENETICS; GENOMICS; HUMAN; MACROPHAGE; METABOLISM; METHODOLOGY; OVARY TUMOR; SIGNAL TRANSDUCTION;

ADIPOCYTES; ALGORITHMS; ANIMALS; COLORECTAL NEOPLASMS; DATABASES, GENETIC; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENOMICS; HUMANS; LEUKEMIA, MYELOID, ACUTE; MACROPHAGES; OVARIAN NEOPLASMS; SIGNAL TRANSDUCTION;

EID: 84860767027     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2011.0182     Document Type: Article

References (37)

1. Allison, D.B., Cui, X., Page, G.P., et al. 2006. Microarray data analysis: From disarray to consolidation and consensus. Nat. Gen. 7, 55-65. (Pubitemid 41828948)
2. Al-Shahrour, F., Díaz-Uriarte, R., and Dopazo, J. 2005. Discovering molecular functions significantly related to phenotypes by combining gene expression data. Bioinformatics 21, 2988-2993. (Pubitemid 40916423)
3. Arnold, C.N., Goel, A., Blum, H.E., et al. 2005. Molecular pathogenesis of colorectal cancer. Cancer 104, 2035-2047.
4. Ashburner, M., Ball, C.A., Blake, J.A., et al. 2000. Gene Ontology: Tool for the unification of biology. Nat. Gen. 25, 25-29. (Pubitemid 30257031)
5. Ben-Dor, A., Shamir, R.,and Yakhini, Z. 1999. Clustering gene expression patterns. J. Comput. Biol. 6, 281-298.
6. Benjamini, Y., and Hochberg, Y. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289-300.
7. Borovecki, F., Lovrecic, L., Zhou, J., et al. 2005. Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. Proc. Natl. Acad. Sci. USA 102, 11023-11028. (Pubitemid 41105986)
8. Bowen, N.J., Walker, L., Matyunina, L.V., et al. 2009. Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells. BMC Med. Genomics 2, 71-85.
9. Cheadle, C., Vawter, M.P., Freed, W.J., et al. 2003. Analysis of microarray data using Z score transformation. J. Mol. Diagn. 5, 73-81. (Pubitemid 36553428)
10. Curtis, R.K., Oresic M., and Vidal-Puig, A. 2005. Pathways to the analysis of microarray data. Trends Biotechnol. 23, 429-435. (Pubitemid 41032923)
11. Dahlquist, K.D., Salomonis, N., Vranizan, K., et al. 2002. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31, 19-20.
12. Dardousis, K., Voolstra, C., Roengvoraphoj, M., et al. 2007. Identification of differentially expressed genes involved in the formation of multicellular tumor spheroids by HT-29 colon carcinoma cells. Mol. Therapy 15, 94-102. (Pubitemid 46043654)
13. Dudoit, S., Yang, Y.H., Callow, M.J., et al. 2002. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12, 111-139.
14. Eisen, M.B., Spellman, P.T., Brown, P.O., et al. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863-14868. (Pubitemid 29003722)
15. Gao, S., and Wang, X. 2007. TAPPA: Topological analysis of pathway phenotype association. Bioinformatics 23, 3100-3102. (Pubitemid 350162880)
16. Gene Expression Omnibus database. Available at: Http://www.ncbi.nlm.nih. gov/geo/. Accessed March 2, 2011.
17. GeneSifter-Analysis Edition. Available at: Http://www.genesifter.net. Accessed November 12, 2010.
18. Ingenuity-Systems. Available at: Http://www.ingenuity.com. Accessed July 1, 2010.
19. Khatri, P., and Draghici, S. 2005. Ontological analysis of gene expression data: Current tools, limitations, and open problems. Bioinformatics 21, 3587-3595. (Pubitemid 41264092)
20. Misman, M.F., Deris, S., Hashim, S.Z.M., et al. 2009. Pathway-based microarray analysis for defining statistical significant phenotype-related pathways: A review of common approaches. Proc. Int. Conf. Inform. Manage. Eng. 496-500.
21. Monteith, G.R., McAndrew, D., Faddy, H.M., et al. 2007. Calcium and cancer: Targeting Ca2 + transport. Nat. Rev. Cancer 7, 519-530. (Pubitemid 46985398)
22. O'Hara, A., Lim, F.L., Mazzatti, D.J., et al. 2009. Microarray analysis identifies matrix metalloproteinases (MMPs) as key genes whose expression is up-regulated in human adipocytes by macrophage-conditioned medium. Pflugers Arch. Eur. J. Physiol. 458, 1103-1114.
23. Page-McCaw, A., Ewald, A.J., and Werb, Z. 2007. Matrix metalloproteinases and the regulation of tissue remodelling. Nat. Reviews Mol. Cell Biol. 8, 221-233.
24. Pan, W. 2002. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 12, 546-554. (Pubitemid 34521044)
25. Pandey, R., Guru, R.K., and Mount, D.W. 2004. Pathway Miner: Extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data. Bioinformatics 20, 2156-2158. (Pubitemid 39236574)
26. Rawlings, J.S., Rosler, K.M., and Harrison, D.A. 2004. The JAK/STAT signaling pathway. J. Cell Sci. 117, 1281-1284.
27. Sabates-Bellver, J., Van de Flier, L.G., de Palo, M., et al. 2007. Transcriptome profile of human colorectal adenomas. Mol. Cancer Res. 5, 1263-1275.
28. Schoonjans, K., Staels, B., and Auwerx, J. 1996. Role of the peroxisome proliferator-activated receptor (PPAR) in mediating the effects of fibrates and fatty acids on gene expression. J. Lipid Res. 37, 907-925. (Pubitemid 26182326)
29. Steffen, B., Muller-Tidow, C., Schwable, J., et al. 2005. The molecular pathogenesis of acute myeloid leukemia. Crit. Rev. Oncol. Hematol. 56, 195-221. (Pubitemid 41565550)
30. Stirewalt, D.L., Meshinchi, S., Kopecky, K.J., et al. 2008. Identification of genes with abnormal expression changes in acute myeloid leukemia. Gene. Chromosomes Cancer 47, 8-20. (Pubitemid 350191459)
31. Subramanian, A., Tamayo, P., Mootha, V.K., et al. 2005. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102, 15545.
32. Tarca, A.L., Draghici, S., Khatri, P., et al. 2009. A novel signaling pathway impact analysis (SPIA). Bioinformatics 25, 75-82.
33. Tarjan, R. 1971. Depth-first search and linear graph algorithms. Proc. Symp. Switching Automata Theor. 114-121.
34. The Kyoto Encyclopaedia of Genes and Genomes database. Available at: Http://www.genome.jp/kegg/. Accessed February 21, 2010.
35. Tian, L., Greenberg, S.A., Kong, S.W., et al. 2005. Discovering statistically significant pathways in expression profiling studies. Proc. Natl. Acad. Sci. USA 102, 13544-13549. (Pubitemid 41420885)
36. Towatari, M., Iida, H., Tanimoto, M., et al. 1997. Constitutive activation of mitogen-activated protein kinase pathway in acute leukemia cells. Leukemia 11, 479-484. (Pubitemid 27173933)
37. Vert, J.P., and Kanehisa, M. 2003. Extracting active pathways from gene expression data. Bioinformatics 19, i238-i244.