A Bayesian extension of the hypergeometric test for functional enrichment analysis

Biometrics

Volumn 70, Issue 1, 2014, Pages 84-94

  Cao, Jing ^a   Zhang, Song ^b  

^a SOUTHERN METHODIST UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

Functional enrichment analysis; Gene ontology; Hypergeometric P value; Modular enrichment analysis; Non central hypergeometric distribution

Indexed keywords

BAYESIAN NETWORKS; GENES; INFORMATION USE; STATISTICAL TESTS;

FUNCTIONAL ENRICHMENT ANALYSIS; GENE ONTOLOGY; HYPERGEOMETRIC; HYPERGEOMETRIC P-VALUE; MODULAR ENRICHMENT ANALYSE; MODULARS; NON-CENTRAL HYPERGEOMETRIC DISTRIBUTION; P-VALUES; SIZE CONSTRAINT;

GENE ONTOLOGY;

BAYESIAN ANALYSIS; DATA ACQUISITION; GENE EXPRESSION; INFORMATION MANAGEMENT; PARAMETERIZATION; PROBABILITY; PROTEIN; SIMULATION;

CD40 ANTIGEN; LYMPHOCYTE ANTIGEN RECEPTOR;

ARTICLE; B CELL LYMPHOMA; BAYES THEOREM; BIOLOGY; COMPUTER SIMULATION; DNA MICROARRAY; FUNCTIONAL ENRICHMENT ANALYSIS; GENE EXPRESSION PROFILING; GENE ONTOLOGY; GENETICS; HUMAN; HYPERGEOMETRIC P-VALUE; METHODOLOGY; MODULAR ENRICHMENT ANALYSIS; NON-CENTRAL HYPERGEOMETRIC DISTRIBUTION; STATISTICAL ANALYSIS; TUMOR CELL LINE; UPREGULATION;

FUNCTIONAL ENRICHMENT ANALYSIS; GENE ONTOLOGY; HYPERGEOMETRIC P-VALUE; MODULAR ENRICHMENT ANALYSIS; NON-CENTRAL HYPERGEOMETRIC DISTRIBUTION;

ANTIGENS, CD40; BAYES THEOREM; CELL LINE, TUMOR; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; GENE ONTOLOGY; HUMANS; LYMPHOMA, B-CELL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RECEPTORS, ANTIGEN, B-CELL; UP-REGULATION;

EID: 84895877065     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12122     Document Type: Article

References (43)

1. Alexa, A., Rahnenfuhrer, J., and Lengauer, T. (2006). Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600-1607.
2. Basso, K., Margolin, A. A., Stolovitzky, G., Klein, U., Dalla-Favera, R., and Califano, A. (2005). Reverse engineering of regulatory networks in human B cells. Nature Genetics 37, 382-390.
3. Bauer, S., Gagneur, J., and Robinson, P. N. (2010). Going Bayesian: Model-based gene set analysis of genome-scale data. Nucleic Acids Research 38, 3523-3532.
4. Carmona-Saez, P., Chagoyen, M., Rodriguez, A., Trelles, O., Carazo, J. M., and Pascual-Montano, A. (2006). Integrated analysis of gene expression by Association Rules Discovery. BMC Bioinformatics 7, 54.
5. Do, K., Muller, P., and Tang, F. (2005). A Bayesian mixture model for differential gene expression. Journal of the Royal Statistical Society, Series C 54, 627-644.
6. Dutting, S., Brachs, S., and Mielenz, D. (2011). Fraternal twins: Swiprosin-1/EFhd2 and Swiprosin-2/EFhd1, two homologous EF-hand containing calcium binding adaptor proteins with distinct functions. Cell Communication and Signaling 9, 2.
7. Efron, B. and Tibshirani, B. (2007). On testing the significance of sets of genes. Annals of Applied Statistics 1, 107-129.
8. Falcon, S. and Gentleman, R. (2007). Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257-258.
9. Fog, A. (2008). Sampling methods for Wallenius' and Fisher's noncentral hypergeometric distributions. Communications in Statistics-Simulation and Computation 37, 241-257.
10. Fontanillo, C., Nogales-Cadenas, R., Pascual-Montano, A., and de Las Rivas, J. (2011). Functional analysis beyond enrichment: Non-redundant reciprocal linkage of genes and biological terms. PLoS ONE 6, e24289.
11. Gelfand, A. E. and Vounatsou, P. (2003). Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics 4, 11-25.
12. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2003). Bayesian Data Analysis. London: Chapman & Hall.
13. Goeman, J. J. and Buhlmann, P. (2007). Analyzing gene expression data in terms of gene sets: Methodological issues. Bioinformatics 23, 980-987.
14. Grossmann, S., Bauer, S., Robinson, P. N., and Vingron, M. (2007). Improved detection of overrepresentation of Gene-Ontology annotations with parent-child analysis. Bioinformatics 23, 3024-3031.
15. Harkness, W. L. (1965). Properties of the extended hypergeometric distribution. The Annals of Mathematical Statistics 36, 938-945.
16. Hosack, D. A., Dennis, G., Jr., Sherman, B. T., Lane, H. C., and Lempicki, R. A. (2003). Identifying biological themes within lists of genes with EASE. Genome Biology 4, R70.
17. Hsueh, R. and Scheuermann, R. H. (2000). Tyrosine kinase activation in the growth, differentiation and death responses initiated from the B cell antigen receptor. Advances in Immunology 75, 283-316.
18. Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009a). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research 37, 1-13.
19. Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44-57.
20. Kanehisa, M. and Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28, 27-30.
21. Katz, E., Deehan, M. R., Seatter, S., Lord, C., Sturrock, R. D., and Harnett, M. M. (2001). B cell receptor-simulated mitochondrial phospholipase A2 activation and resultant disruption of mitochondrial membrane potential correlate with the induction of apoptosis in WEHI-231 B cells. The Journal of Immunology 166, 137-147.
22. Khatri, P., Drghici, S., Ostermeier, G. C., and Krawetz, S. A. (2002). Profiling gene expression using Onto-Express. Genomics 79, 266-70.
23. Khatri, P. and Drghici, S. (2005). Ontological analysis of gene expression data: Current tools, limitations, and open problems. Bioinformatics 21, 3587-3595.
24. Lerman, G. and Shakhnovich, B. E. (2007). Defining functional distance using manifold embeddings of gene ontology annotations. Proceedings of the National Academy of Sciences 104, 11334-11339.
25. Lewin, A. M. and Grieve, I. C. (2006). Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data. BMC Bioinformatics 7, 426.
26. Lu, Y., Rosenfeld, R., Simon, I., Nau, G. J., and Bar-Joseph, Z. (2008). A probabilistic generative model for GO enrichment analysis. Nucleic Acids Research 36, e109.
27. Milne, C. D., Fleming, H. E., Zhang, Y., and Paige, C. J. (2004). Mechanisms of selection mediated by interleukin-7, the preBCR, and hemokinin-1 during B-cell development. Immunological Reviews 197, 75-88.
28. Muller, P., Parmigiani, G., and Rice, K. (2006). FDR and Bayesian multiple comparisons rules. In Proceedings of the Valencia/ISBA 8th World Meeting on Bayesian Statistics. Oxford: Oxford University Press.
29. Newton, M., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155-176.
30. Newton, M., 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.
31. Nogales-Cadenas, R., Carmona-Saez, P., Vazquez, M., Vicente, C., Yang, X., Tirado, F., Caraso, J. M., and Pascual-Montano, A. (2009). GeneCodis: Interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Research 37(Suppl. 2), W317-W322.
32. Schlicker, A., Domingues, F. S., Rahnenfuhrer, J., and Lengauer, T. (2006). A new measure for functional similarity of gene products based on gene ontology. BMC Bioinformatics 7, 302.
33. Scott, J. G. and Berger, J. O. (2010). Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem. The Annals of Statistics 38, 2587-2619.
34. Stingo, F. C., Chen, Y. A., Tadesse, M. G., and Vannucci, M. (2011). Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes. The Annals of Applied Statistics 5, 1978-2002.
35. Storey, J. D. (2002). A direct approach to false discovery rate. Journal of the Royal Statistical Society, Series B 64, 479-498.
36. Subramanian, A. P., 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. Proceedings of the National Academy of Sciences 102, 15545-15550.
37. The Gene Ontology Consortium. (2000). Gene ontology: Tool for the unification of biology. Nature Genetics 25, 25-29.
38. Tusher, V. G., Tibshirani, R., and Chu G. (2001). Significance analysis of microarrays applied to transcriptional responses to ionizing radiation. Proceedings of the National Academy of Sciences 98, 5116-5121.
39. Wang, D., Feng, J., Wen, R., Marine, J. C., Sangster, M. Y., Parganas, E., Hoffmeyer, A., Jackson, C. W., Cleveland, J. L., Murray, P. J., and Ihle, J. N. (2000). Phospholipase Cgamma2 is essential in the functions of B cell and several Fc receptors. Immunity 13, 25-35.
40. Wang, J. Huang, Q., Liu, Z. P., Wang, Y., Wu, L. Y., Chen, L., and Zhang, X. S. (2011). NOA: A novel network ontology analysis method. Nucleic Acids Research 39, e87.
41. Wei, P. and Pan, W. (2012). Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor. The Annals of Applied Statistics 6, 334-355.
42. Zhang, B., Schmoyer, D., Kirov, S., and Snoddy, J. (2004). GOTree Machine (GOTM): A web-based platform for interpreting sets of interesting genes using gene ontology hierarchies. BMC Bioinformatics 5, 16.
43. Zhang, S., Cao, J., Kong, M., and Scheuermann, R. H. (2010). GO-Bayes: Gene Ontology-based enrichment analysis using a Bayesian approach. Bioinformatics 26, 905-911.