Browse > Article
http://dx.doi.org/10.5351/KJAS.2016.29.3.437

Identifying statistically significant gene sets based on differential expression and differential coexpression  

Lee, Sunho (Division of Mathematics and Statistics, Sejong University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.3, 2016 , pp. 437-448 More about this Journal
Abstract
Gene set analysis utilizing biologic information is expected to produce more interpretable results because the occurrence of tumors (or diseases) is believed to be associated with the regulation of related genes. Many methods have been developed to identify statistically significant gene sets across different phenotypes; however, most focus exclusively on either the differential gene expression or the differential correlation structure in the gene set. This research provides a new method that simultaneously considers the differential expression of genes and differential coexpression with multiple genes in the gene set. Application of this NEW method is illustrated with real microarray data example, p53; subsequently, a simulation study compares its type I error rate and power with GSEA, SAMGS, GSCA and GSNCA.
Keywords
microarray experiment; gene set analysis; differential expression; differential coexpression;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Choi, Y. and Kendziorski, C. (2009). Statistical methods for gene set co-expression analysis, Bioinformatics, 25, 2780-2786.   DOI
2 Dinu, I., Potter, J. D., Mueller, T., Liu, Q., Adewale, A. J., Jhangri, G. S., Einecke, G., Famulski, K. S., Halloran, P. and Yasui, Y. (2007). Improving gene set analysis of microarray data by SAM-GS, BMC Bioinformatics, 8, 242.   DOI
3 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.   DOI
4 Efron, B. and Tibshirani, R. (2007). On testing the significance of sets of genes, Annals of Applied Statistics, 1, 107-129.   DOI
5 Goeman, J., van de Geer, S., de Kort, F., and Houwelingen, H. (2004). A global test for groups of genes: testing association with a clinical outcome, Bioinformatics, 20, 93-99.   DOI
6 Goeman, 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, Bioinformatics, 21, 1950-1957.   DOI
7 Jung, S. and Kim, S. (2014). EDDY: a novel statistical gene set test method to detect differential genetic dependencies, Nucleic Acids Research, 42, e60.   DOI
8 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.
9 Kim, B. S., Jang, J. S., Kim, S. C., and Lim, J. (2009). A report on the inter-gene correlations in cDNA microarray data sets, The Korean Journal of Applied Statistics, 22, 617-626.   DOI
10 Kim, S. Y. and Volsky, D. (2005). PAGE: parametric analysis of gene set enrichment, BMC Bioinformatics, 6, 1471-2105.
11 Klebanov, L. and Yakovlev, A. (2007). Diverse correlation structures in gene expression data and their utility in improving statistical inference, The Annals of Applied Statistics, 1, 538-559.   DOI
12 Lai, Y., Wu, B., Chen, L., Zhao, H. (2004). A statistical method for identifying differential gene-gene coexpression patterns, Bioinformatics, 20, 3146-3155.   DOI
13 Lee, S. H., Lee, S. K., and Lee, K. H. (2009). Developing a parametric method for testing the significance of gene sets in microarray data analysis, Communications for Statistical Applications and Methods, 397-408.   DOI
14 Ma, H., Schadt, E. E., Kaplan, L. M., and Zhao, H. (2011). COSINE: condition-specific sub-network identification using a global optimization method, Bioinformatics, 27, 1290-1298.   DOI
15 Maciejewski, H. (2014). Gene set analysis methods: statistical models and methodological differences, Briefings in Bioinformatics, 15, 504-518.   DOI
16 Meyer, C. (2001). Matrix Analysis and Applied Linear Algebra, Society for industrial and applied mathematics (SIAM), Philadelphia.
17 Rahmatallah, Y., Emmert-Streib, F. and Glazko, G. (2014). Gene sets net correlations analysis (GSNCA): a multivariate differential coexpression test for gene sets, Bioinformatics, 30, 360-368.   DOI
18 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-1-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nature Genetics, 34, 267-273.   DOI
19 Newton, M. A., Quintana, F. A., den Boon, J. A. (2007). Random set methods identify distinct aspects of the enrichment signal in gene-set analysis, Annals of Applied Statistics, 1, 85-106.   DOI
20 Qui, X., Klebanov, L., and Yakovlev, A. (2005). Correlation between gene expression levels and limitations of the empirical Bayes methodology for finding differentially expressed genes, Statistical Applications in Genetics and Molecular Biology, 4, Ariticle 34.
21 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, In Proceedings of the National Academy of Sciences, 102, 15545-15550.   DOI
22 Tesson, B. M., Breitling, R., and Jansen, R. C. (2010). DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules, BMC Bioinformatics, 11, 497.   DOI
23 Tusher, V. G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, In Proceedings of the National Academy of Sciences, 98, 5116-5121.   DOI