Gene set enrichment analysis: Performance evaluation and usage guidelines

Briefings in Bioinformatics

Volumn 13, Issue 3, 2012, Pages 281-291

  Hung, Jui Hung ^c   Yang, Tun Hsiang ^c   Hu, Zhenjun ^c   Weng, Zhiping ^a   DeLisi, Charles ^b  

^a UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

^b BOSTON UNIVERSITY   (United States)

^c Boston University ^*

Author keywords

CMC; Controlled mutual coverage; Expression analysis; Gene set enrichment analysis; GSEA; Pathway enrichment analysis; Performance evaluation; PWEA

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGY; EVALUATION; GENE EXPRESSION; GENETIC DATABASE; HUMAN; METHODOLOGY; PRACTICE GUIDELINE;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE EXPRESSION; GUIDELINES AS TOPIC; HUMANS;

EID: 84861793773     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr049     Document Type: Article

References (54)

1. Molecular Signatures Database v3.0. Available from: http://www.broadinstitute.org/gsea/msigdb/index.jsp (9 September 2010, date last accessed).
2. Moriya Y, Itoh M, Okuda S, et al. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007;35:W182-5.
3. Gilchrist A, Au CE, Hiding J, et al. Quantitative proteomics analysis of the secretory pathway. Cell 2006;127(6): 1265-81.
4. Koller A, Washburn MP, Lange BM, etal. Proteomic survey of metabolic pathways in rice. ProcNatl Acad SciUSA 2002; 99(18):11969-74.
5. Ye Y, Doak TG. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoSComput Biol 2009;5(8):e1000465.
6. Khatri P, Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 2005;21(18):3587-95.
7. Cho RJ, Huang M, Campbell MJ, et al. Transcriptional regulation and function during the human cell cycle. Nat Genet 2001;27(1):48-54.
8. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102(43):15545-50.
9. Hung JH, Whitfield TW, Yang TH, et al. Identification of functional modules that correlate with phenotypic difference: the influence of network topology. Genome Biol 2010;11(2):R23.
10. Efron B, Tibshirani R. On testing the significance of sets of genes. Ann Appl Stat 2007;1(1):107-29.
11. Nam D, Kim SY. Gene-set approach for expression pattern analysis. Brief Bioinform 2008;9(3):189-97.
12. Dinu I, Potter JD, Mueller T, et al. Gene-set analysis and reduction. Brief Bioinform 2009;10(1):24-34.
13. Ackermann M, Strimmer K. A general modular framework for gene set enrichment analysis. BMC Bioinform 2009;10:47.
14. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37(1):1-13.
15. Lu C. Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays. BMC Bioinformatics 2004;5:103.
16. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4(2):249-64.
17. Steinhoff C, Vingron M. Normalization and quantification of differential expression in gene expression microarrays. Brief Bioinform 2006;7(2):166-77.
18. Irizarry RA, Bolstad BM, Collin F, et al. Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res 2003;31(4):e15.
19. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5(7):621-8.
20. Celton M, Malpertuy A, Lelandais G, et al. Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments. BMCGenomics 2010;11:15.
21. Brock GN, Shaffer JR, Blakesley RE, et al. Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes. BMC Bioinformatics 2008;9:12.
22. Bo TH, Dysvik B, Jonassen I. LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res 2004;32(3):e34.
23. Saxena V, Orgill D, Kohane I. Absolute enrichment: gene set enrichment analysis for homeostatic systems. Nucleic Acids Res 2006;34(22):e151.
24. Leone FC, Nelson LS, Nottingham RB. The folded normal distribution. Technometrics 1961;3(4):543-50.
25. Irizarry RA, Wang C, Zhou Y, et al. Gene set enrichment analysis made simple. Stat Methods Med Res 2009;18(6): 565-75.
26. Leong HS, Yates T, Wilson C, et al. ADAPT: a database of affymetrix probesets and transcripts. Bioinformatics 2005; 21(10):2552-3.
27. Harbig J, Sprinkle R, Enkemann SA. A sequence-based identification of the genes detected by probesets on the Affymetrix U133 plus 2.0 array. Nucleic Acids Res 2005; 33(3):e31.
28. Hong F, Breitling R. A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics 2008;24(3):374-82.
29. Fisher R. Statistical Methods for ResearchWorkers. 4th edn. London: Oliver and Boyd, 1932.
30. Rosenthal R, Hiller JB, Bornstein RF, et al. Meta-analytic methods, the Rorschach, and the MMPI. Psychol Assess 2001;13(4):449-51.
31. Fundel K, Kuffner R, Aigner T, et al. Normalization and gene p-value estimation: issues in microarray data processing. Bioinform Biol Insights 2008;2:291-305.
32. Li B, Ruotti V, Stewart RM, et al. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 2010;26(4):493-500.
33. Faulkner GJ, Forrest AR, Chalk AM, et al. A rescue strategy for multimapping short sequence tags refines surveys of transcriptional activity by CAGE. Genomics 2008;91(3): 281-8.
34. Tian L, Greenberg SA, Kong SW, et al. Discovering statistically significant pathways in expression profiling studies. ProcNatl Acad SciUSA 2005;102(38):13544-9.
35. Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics 2007;23(3):306-13.
36. Kitami T, Nadeau JH. Biochemical networking contributes more to genetic buffering in human and mouse metabolic pathways than does gene duplication. Nat Genet 2002;32(1): 191-4.
37. Tarca AL, Draghici S, Khatri P, et al. A novel signaling pathway impact analysis. Bioinformatics 2009;25(1):75-82.
38. Rahnenfuhrer J, Domingues FS, Maydt J, et al. Calculating the statistical significance of changes in pathway activity from gene expression data. Stat Appl GenetMol Biol 2004;3: Article 16.
39. Keller A, Backes C, Lenhof HP. Computation of significance scores of unweighted Gene Set Enrichment Analyses. BMC Bioinformatics 2007;8:290.
40. Shaffer J. Multiple hypothesis testing: a review. Annu Rev Psychol 1995;46:561-84.
41. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J RStatist Soc B 1995;57:289-300.
42. Storey JD. The positive false discovery rate: A Bayesian interpretation and the q-value. Ann Stat 2003;31:2013-35.
43. Tang Y, Ghosal S, Roy A. Nonparametric bayesian estimation of positive false discovery rates. Biometrics 2007;63(4): 1126-34.
44. Storey JD, Tibshirani R. Statistical significance for genomewide studies. ProcNatl Acad SciUSA 2003;100(16):9440-5.
45. Thomas Sellke MJB, Berger JO. Calibration of p values for testing precise null hypotheses. Amer Statistician 2001;55(1): 62-71.
46. Fodor AA, Tickle TL, Richardson C. Towards the uniform distribution of null P values on Affymetrix microarrays. Genome Biol 2007;8(5):R69.
47. Hever A, Roth RB, Hevezi P, etal. Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator. Proc Natl Acad Sci USA 2007; 104(30):12451-6.
48. Draghici S, Khatri P, Tarca AL, et al. A systems biology approach for pathway level analysis. Genome Res 2007; 17(10):1537-45.
49. Kim TM, Jung YC, Rhyu MG, et al. GEAR: genomic enrichment analysis of regional DNA copy number changes. Bioinformatics 2008;24(3):420-1.
50. Kong SW, Pu WT, Park PJ. A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics 2006;22(19):2373-80.
51. Benjamini Y, Drai D, Elmer G, et al. Controlling the false discovery rate in behavior genetics research. Behav BrainRes 2001;125(1-2):279-84.
52. Kim SY, Volsky DJ. PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 2005;6:144.
53. Scheer M, Klawonn F, Munch R, et al. JProGO: a novel tool for the functional interpretation of prokaryotic microarray data using Gene Ontology information. Nucleic Acids Res 2006;34(Web Server issue):W510-W515.
54. Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005;21(9): 1943-9.