JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Developing a Parametric Method for Testing the Significance of Gene Sets in Microarray Data Analysis
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Developing a Parametric Method for Testing the Significance of Gene Sets in Microarray Data Analysis
Lee, Sun-Ho; Lee, Seung-Kyu; Lee, Kwang-Hyun;
  PDF(new window)
 Abstract
The development of microarray technology makes possible to analyse many thousands of genes simultaneously. While it is important to test each gene whether it shows changes in expression associated with a phenotype, human diseases are thought to occur through the interactions of multiple genes within a same functional cafe-gory. Recent research interests aims to directly test the behavior of sets of functionally related genes, instead of focusing on single genes. Gene set enrichment analysis(GSEA), significance analysis of microarray to gene-set analysis(SAM-GS) and parametric analysis of gene set enrichment(PAGE) have been applied widely as a tool for gene-set analyses. We describe their problems and propose an alternative method using a parametric analysis by adopting normal score transformation of gene expression values. Performance of the newly derived method is compared with previous methods on three real microarray datasets.
 Keywords
Microarray experiment;single gene analysis;gene set analysis;normal score;
 Language
Korean
 Cited by
1.
Identifying statistically significant gene sets based on differential expression and differential coexpression, Korean Journal of Applied Statistics, 2016, 29, 3, 437  crossref(new windwow)
 References
1.
이광현, 이선호 (2008). 절대치와 절삭을 이용한 유전자 집단 분석, <응용통계연구>, 21, 523-535

2.
Barry, W. T., Nobel, A. B. and Wright, F. A. (2005). Significance analysis of functional categories in gene expression studies: A structured permutation approach, Bioinformatics, 21, 1943-1949 crossref(new window)

3.
Blom, G. (1958). Statistical Estimates and Transformed Beta- Variables, John Wiley & Sons, New York

4.
Curtis, R. K., Oresic, M. and Vidal-Puig, A. (2005). Pathways to the analysis of microarray data, Trends in Biotechnology, 23, 429-435 crossref(new window)

5.
Damian, D. and Gorfine, M. (2004). Statistical concerns about the GSEA procedure, Nature genetics, 36, 663 crossref(new window)

6.
Dinu, I., Potter, J. D., Mueller, T., Adewale, A. J., Jhangri, G. S., Einecke, G., Famulski, K. S., Halloran, P. and Yasui, Y. (2007). Improving GSEA for analysis of biologic pathways for differential gene expression across a binary phenotype, COBRA Preprint Series, Article 16

7.
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 Biology, 4, R7 crossref(new window)

8.
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 crossref(new window)

9.
Efron, B. and Tibshirani, R. (2007). On testing the significance of sets of genes, The Annals of Applied Statistics, 1, 107-129 crossref(new window)

10.
Goeman, J. J., van de Geer, S. A., de Kort, F. and van Houwelingen, H. C. (2004). A global test for groups of genes: Testing association with a clinical outcome, Bioinformatics, 20, 93-99 crossref(new window)

11.
Goeman, J. J., Oosting, J., Cleton-Jansen, A. M., Anninga, J. K. and van Houwelingen, H. C. (2005). Testing association of a pathway with survival using gene expression data, Bioinforrnatics, 21, 1950-1957 crossref(new window)

12.
Golub, T. R,, Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D. and Lander, E. S. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science, 286, 531-537 crossref(new window)

13.
Khatri, P., Bhavsar, P., Bawa, G. and Draghici, S. (2004). Onto-Tools: An ensemble of web-accessible, ontology- based tools for the functional design and interpretation of high- throughput gene expression experiments, Nucleic Acids Research, 32, 449-456 crossref(new window)

14.
Kim, S. Y. and Voisky, D. J. (2005). PAGE: Parametric analysis of gene set enrichment, BMC Bioinfor-matics, 6, 1471-2105 crossref(new window)

15.
Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D. and Groop, L. C. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nature Genetics, 34, 267-273 crossref(new window)

16.
Newton, M. A., Quintana, F. A., den Boon, J. A., Sengupta, S. and Ahlquist, P. (2007). Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis, The Annals of Applied Statistics, 1, 85-106 crossref(new window)

17.
Pavlidis, P., Lewis, D. P. and Noble, W. S. (2002). Exploring gene expression data with class scores, In Proceedings of the Pacific Symposium on Biocomputing, 474-485

18.
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. and Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge- based approach for interpreting genome-wide expression profiles, PNAS, 102, 15545-15550 crossref(new window)

19.
Tibshirani, R., Hastie, T., Narasimhan, B. and Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression, PNAS, 99, 6567-6572 crossref(new window)

20.
Tusher, V. G., Tibshirani, R. and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, PNAS, 98, 5116-5121 crossref(new window)