Abstract
One of the challenges in the analysis of gene expression data is placing the results in the context of other data available about genes and their relationships to each other. Here, we approach this problem in the study of gene expression changes associated with age in two areas of the human prefrontal cortex, comparing two computational methods. The first method, “overrepresentation analysis” (ORA), is based on statistically evaluating the fraction of genes in a particular gene ontology class found among the set of genes showing age-related changes in expression. The second method, “functional class scoring” (FCS), examines the statistical distribution of individual gene scores among all genes in the gene ontology class and does not involve an initial gene selection step. We find that FCS yields more consistent results than ORA, and the results of ORA depended strongly on the gene selection threshold. Our findings highlight the utility of functional class scoring for the analysis of complex expression data sets and emphasize the advantage of considering all available genomic information rather than sets of genes that pass a predetermined “threshold of significance.”
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Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., and Sherlock, G. 2000. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25:25–29.
Kim, C. C. and Falkow, S. 2003. Significance analysis of lexical bias in microarray data. BMC Bioinformatics 4:12.
Zeeberg, B. R., Feng, W., Wang, G., Wang, M. D., Fojo, A. T., Sunshine, M., Narasimhan, S., Kane, D. W., Reinhold, W. C., Lababidi, S., Bussey, K. J., Riss, J., Barrett, J. C., and Weinstein, J. N. 2003. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 4:R28.
Draghici, S., Khatri, P., Martins, R. P., Ostermeier, G. C. and Krawetz, S. A. 2003. Global functional profiling of gene expression. Genomics 81:98–104.
Doniger, S. W., Salomonis, N., Dahlquist, K. D., Vranizan, K., Lawlor, S. C. and Conklin, B. R. 2003. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 4:R7.
Blalock, E. M., Chen, K. C., Sharrow, K., Herman, J. P., Porter, N. M., Foster, T. C., and Landfield, P. W. 2003. Gene microarrays in hippocampal aging: statistical profiling identifies novel processes correlated with cognitive impairment. J. Neurosci. 23:3807–3819.
Pavlidis, P., Lewis, D. P., and Noble, W. S. 2002. Exploring gene expression data with class scores. Pac. Symp. Biocomput. 474–485.
Middleton, F. A., Mirnics, K., Pierri, J. N., Lewis, D. A. and Levitt, P. 2002. Gene expression profiling reveals alterations of specific metabolic pathways in schizophrenia. J. Neurosci. 22:2718–2729.
Irizarry, R. A., Bolstad, B. M., Collin, F., Cope, L. M., Hobbs, B. and Speed, T. P. 2003. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31:e15.
Dudoit, S., Gentleman, R. C., and Quackenbush, J. 2003. Open source software for the analysis of microarray data. Biotechniques (Suppl.):45–51.
Zar, J. H. 1999. Biostatistical analysis. Prentice Hall, Upper Saddle River, NJ.
Efron, B. and Tibshirani, R. 1993. An introduction to the bootstrap. Chapman & Hall, New York.
Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B 57:289–300.
Tanaka, M. and Cyong, J. C. 1985. The development of complement activating ability as an age related factor in murine brains. Microbiol. Immunol. 29:1219–1227.
Terao, A., Apte-Deshpande, A., Dousman, L., Morairty, S., Eynon, B. P., Kilduff, T. S., and Freund, Y. R. 2002. Immune response gene expression increases in the aging murine hippocampus. J. Neuroimmunol. 132:99–112.
Lee, C. K., Weindruch, R. and Prolla, T. A. 2000. Gene-expression profile of the ageing brain in mice. Nat. Genet. 25:294–297.
Jiang, C. H., Tsien, J. Z., Schultz, P. G., and Hu, Y. 2001. The effects of aging on gene expression in the hypothalamus and cortex of mice. Proc. Natl. Acad. Sci. USA 98:1930–1934.
Mocchegiani, E., Giacconi, R., Cipriano, C., Muzzioli, M., Fattoretti, P., Bertoni-Freddari, C., Isani, G., Zambenedetti, P., and Zatta, P. 2001. Zinc-bound metallothioneins as potential biological markers of ageing. Brain Res. Bull. 55:147–153.
Miyazaki, I., Asanuma, M., Higashi, Y., Sogawa, C. A., Tanaka, K., and Ogawa, N. 2002. Age-related changes in expression of metallothionein-III in rat brain. Neurosci. Res. 43:323–333.
Jakt, L. M., Cao, L., Cheah, K. S., and Smith, D. K. 2001. Assessing clusters and motifs from gene expression data. Genome Res. 11:112–123.
Westfall, P. H. and Young, S. S. 1993. Resampling-based multiple testing. John Wiley & Sons, New York.
Yekutieli, D. and Benjamini, Y. 1999. Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. Journal of Statistical Planning and Inference 82:171–196.
Marin-Padilla, M. 1988. Embrionic vascularization of the cerebral cortex. Pages 479–510, in Peters, A. and Jones, E. G. (eds.), Cerebral cortex, vol. 7. Plenum, New York.
Chang, D. F., Belaguli, N. S., Iyer, D., Roberts, W. B., Wu, S. P., Dong, X. R., Marx, J. G., Moore, M. S., Beckerle, M. C., Majesky, M. W., and Schwartz, R. J. 2003. Cysteine-rich LIM-only proteins CRP1 and CRP2 are potent smooth muscle differentiation cofactors. Dev. Cell. 4:107–118.
Freeman, W. M., Brebner, K., Patel, K. M., Lynch, W. J., Roberts, D. C., and Vrana, K. E. 2002. Repeated cocaine self-administration causes multiple changes in rat frontal cortex gene expression. Neurochem. Res. 27:1181–1192.
Breen, M. A. and Ashcroft, S. J. 1997. A truncated isoform of Ca2+/calmodulin-dependent protein kinase II expressed in human islets of Langerhans may result from trans-splicing. FEBS Lett. 409:375–379.
Erraji-BenChekroun, L., Underwood, M. D., Arango, V., Galfalvy, H., Pavlidis, P., Smyrniotopoulos, P., Mann, J. J., and Sibille, E. (submitted) Active, continuous and extensive molecular aging in human prefrontal cortex.
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Pavlidis, P., Qin, J., Arango, V. et al. Using the Gene Ontology for Microarray Data Mining: A Comparison of Methods and Application to Age Effects in Human Prefrontal Cortex. Neurochem Res 29, 1213–1222 (2004). https://doi.org/10.1023/B:NERE.0000023608.29741.45
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DOI: https://doi.org/10.1023/B:NERE.0000023608.29741.45