Separate enrichment analysis of pathways for up- And downregulated genes

Journal of the Royal Society Interface

Volumn 11, Issue 92, 2014, Pages

  Hong, Guini ^a   Zhang, Wenjing ^a   Li, Hongdong ^a   Shen, Xiaopei ^a   Guo, Zheng ^a,b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b FUJIAN MEDICAL UNIVERSITY   (China)

Author keywords

Enrichment analysis; Gene expression; Microarray; RNA seq

Indexed keywords

MICROARRAYS; RNA;

ANALYSIS STRATEGIES; EXPRESSION LEVELS; FUNCTIONAL LINKS; GENE EXPRESSION PROFILES; PHENOTYPIC DIFFERENCES; RNA-SEQ; SEPARATE ANALYSIS; STATISTICAL POWER;

GENE EXPRESSION;

DNA; PROTEIN P53; TRANSCRIPTOME;

ARTICLE; BINOMIAL DISTRIBUTION; CELL CYCLE ARREST; CLEAR CELL CARCINOMA; COLORECTAL ADENOMA; CONTROLLED STUDY; DNA REPAIR; EXTRACELLULAR MATRIX; FOCAL ADHESION; GENE ONTOLOGY; GENETIC ASSOCIATION; LUNG ADENOCARCINOMA; PHENOTYPE; RECEPTOR DOWN REGULATION; RECEPTOR UPREGULATION; SEPARATION TECHNIQUE; STOMACH CANCER; BIOLOGICAL MODEL; BIOLOGY; DOWN REGULATION; ENRICHMENT ANALYSIS; GENE; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENETIC DATABASE; GENETICS; HUMAN; METABOLISM; MICROARRAY; MICROARRAY ANALYSIS; NEOPLASM; PHYSIOLOGY; RNA-SEQ; SEQUENCE ANALYSIS; SIGNAL TRANSDUCTION; UPREGULATION;

ENRICHMENT ANALYSIS; GENE EXPRESSION; MICROARRAY; RNA-SEQ;

COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; DOWN-REGULATION; GENE EXPRESSION REGULATION; GENES; HUMANS; MICROARRAY ANALYSIS; MODELS, GENETIC; NEOPLASMS; SEQUENCE ANALYSIS, RNA; SIGNAL TRANSDUCTION; TRANSCRIPTOME; UP-REGULATION;

EID: 84893123546     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2013.0950     Document Type: Article

References (52)

1. Huang da W, Sherman BT, Lempicki RA. 2009 Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1-13. (doi:10.1093/nar/gkn923)
2. Emmert-Streib F, Glazko GV. 2011 Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Comput. Biol. 7, e1002053. (doi:10.1371/journal.pcbi.1002053)
3. Wang J, Zhou X, Zhu J, Gu Y, Zhao W, Zou J, Guo Z. 2011 GO-function: deriving biologically relevant functions from statistically significant functions. Brief Bioinform. 13, 216-227. (doi:10.1093/bib/bbr041)
4. Huang da W et al. 2007 DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35(Suppl. 2), W169-W175. (doi:10.1093/nar/gkm415)
5. Zeeberg BR et al. 2003 GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 4, R28. (doi:10.1186/gb-2003-4-4- r28)
6. Khatri P, Draghici S, Ostermeier GC, Krawetz SA. 2002 Profiling gene expression using Onto-Express. Genomics 79, 266-270. (doi:10.1006/geno.2002. 6698)
7. Maere S, Heymans K, Kuiper M. 2005 BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448-3449. (doi:10.1093/bioinformatics/bti551)
8. Young MD, Wakefield MJ, Smyth GK, Oshlack A. 2010 Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14. (doi:10.1186/gb-2010-11-2-r14)
9. Curtis RK, Oresic M, Vidal-Puig A. 2005 Pathways to the analysis of microarray data. Trends Biotechnol. 23, 429-435. (doi:10.1016/j.tibtech.2005.05. 011)
10. Fisher RA. 1935 The logic of inductive inference. J. R. Stat. Soc. 98, 39-54. (doi:10.2307/2342435)
11. Roscoe JT, Byars JA. 1971 An investigation of the restraints with respect to sample size commonly imposed on the use of the chi-square statistic. J. Am. Stat. Assoc. 66, 755-759. (doi:10.1080/01621459.1971.10482341)
12. Kemp CD, Kemp AW. 1956 Generalized hypergeometric distributions. J. R. Stat. Soc. Ser. B 18, 202-211.
13. Brunk HD, Holstein JE, Williams FW. 1968 A comparison of binomial approximations to the hypergeometric distribution. Am. Stat. 2, 24-26. (doi:10.1080/00031305.1968.10480437)
14. Khatri P, Draghici S. 2005 Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21, 3587-3595. (doi:10.1093/bioinformatics/bti565)
15. Sebollela A et al. 2012 Amyloid-beta oligomers induce differential gene expression in adult human brain slices. J. Biol. Chem. 287, 7436-7445. (doi:10.1074/jbc.M111.298471)
16. Libalova H, Uhlirova K, Klema J, Machala M, Sram RJ, Ciganek M, Topinka J. 2012 Global gene expression changes in human embryonic lung fibroblasts induced by organic extracts from respirable air particles. Part Fibre Toxicol. 9, 1. (doi:10.1186/1743-8977-9-1)
17. Yoshihara K et al. 2011 High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by down-regulation of antigen presentation pathway. Clin. Cancer Res. 18, 1374-1385. (doi:10.1158/1078-0432. CCR-11-2725)
18. Shigemizu D, Hu Z, Hung JH, Huang CL, Wang Y, Delisi C. 2011 Using functional signatures to identify repositioned drugs for breast, myelogenous leukemia and prostate cancer. PLoS Comput. Biol. 8, e1002347. (doi:10.1371/journal.pcbi.1002347)
19. Synnergren J, Akesson K, Dahlenborg K, Vidarsson H, Ameen C, Steel D, Lindahl A, Olsson B, Sartipy P. 2008 Molecular signature of cardiomyocyte clusters derived from human embryonic stem cells. Stem Cells 26, 1831-1840. (doi:10.1634/stemcells.2007-1033)
20. Desagher S, Severac D, Lipkin A, Bernis C, Ritchie W, Le Digarcher A, Journot L. 2005 Genes regulated in neurons undergoing transcription-dependent apoptosis belong to signaling pathways rather than the apoptotic machinery. J. Biol. Chem. 280, 5693-5702. (doi:10.1074/jbc.M408971200)
21. Kanehisa M, Goto S, Kawashima S, Nakaya A. 2002 The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42-46. (doi:10.1093/nar/30.1.42)
22. Grutzmann R, Boriss H, Ammerpohl O, Luttges J, Kalthoff H, Schackert HK, Kloppel G, Saeger HD, Pilarsky C. 2005 Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes. Oncogene 24, 5079-5088. (doi:10.1038/sj.onc.1208696)
23. Wang D et al. 2012 Extensive up-regulation of gene expression in cancer: the normalised use of microarray data. Mol. Biosyst. 8, 818-827. (doi:10.1039/c2mb05466c)
24. Boer JM et al. 2001 Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31 500-element cDNA array. Genome Res. 11, 1861-1870. (doi:10.1101/gr. 184501)
25. Ashburner M et al. 2000 Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29. (doi:10.1038/75556)
26. Barrett T et al. 2009 NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 37(Database issue), D885-D890. (doi:10.1093/nar/gkn764)
27. Cancer Genome Atlas Research N. 2008 Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061-1068. (doi:10.1038/nature07385)
28. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. 2003 Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264. (doi:10.1093/biostatistics/4.2.249)
29. Diehn M et al. 2003 SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 31, 219-223. (doi:10.1093/nar/gkg014)
30. Robinson MD, Oshlack A. 2010 A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25. (doi:10.1186/gb-2010-11-3-r25)
31. Robinson MD, McCarthy DJ, Smyth GK. 2010 edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140. (doi:10.1093/bioinformatics/btp616)
32. Benjamini Y, Hochberg Y. 1995 Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 57, 289-300. (doi:10.2307/2346101)
33. Tusher VG, Tibshirani R, Chu G. 2001 Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116-5121. (doi:10.1073/pnas.091062498)
34. Loven J, Orlando DA, Sigova AA, Lin CY, Rahl PB, Burge CB, Levens DL, Lee TI, Young RA. 2012 Revisiting global gene expression analysis. Cell 151, 476-482. (doi:10.1016/j.cell.2012.10.012)
35. Benjamini Y, Yekutieli D. 2001 The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165-1188. (doi:10.1214/aos/1013699998)
36. Zou J, Hao C, Hong G, Zheng J, He L, Guo Z. 2012 Revealing weak differential gene expressions and their reproducible functions associated with breast cancer metastasis. Comput. Biol. Chem. 39, 1-5. (doi:10.1016/j. compbiolchem.2012.04.002)
37. Cui Q et al. 2007 A map of human cancer signaling. Mol. Syst. Biol. 3, 152. (doi:10.1038/msb4100200)
38. Geistlinger L, Csaba G, Kuffner R, Mulder N, Zimmer R. 2011 From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems. Bioinformatics 27, i366-i373. (doi:10.1093/bioinformatics/btr228)
39. Wu D, Lim E, Vaillant F, Asselin-Labat ML, Visvader JE, Smyth GK. 2010 ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176-2182. (doi:10.1093/bioinformatics/btq401)
40. Petit V, Thiery JP. 2000 Focal adhesions: structure and dynamics. Biol. Cell. 92, 477-494. (doi:10.1016/S0248-4900(00)01101-1)
41. Atwal GS et al. 2009 Altered tumor formation and evolutionary selection of genetic variants in the human MDM4 oncogene. Proc. Natl Acad. Sci. USA 106, 10 236-10 241. (doi:10.1073/pnas.0901298106)
42. Croker BA, Kiu H, Nicholson SE. 2008 SOCS regulation of the JAK/STAT signalling pathway. Semin. Cell Dev. Biol. 19, 414-422. (doi:10.1016/j.semcdb. 2008.07.010)
43. Sakai Y, Honda M, Fujinaga H, Tatsumi I, Mizukoshi E, Nakamoto Y, Kaneko S. 2008 Common transcriptional signature of tumor-infiltrating mononuclear inflammatory cells and peripheral blood mononuclear cells in hepatocellular carcinoma patients. Cancer Res. 68, 10 267-10 279. (doi:10.1158/0008-5472.CAN- 08-0911)
44. Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R. 2007 A systems biology approach for pathway level analysis. Genome Res. 17, 1537-1545. (doi:10.1101/gr.6202607)
45. Ocak S et al. 2010 DNA copy number aberrations in small-cell lung cancer reveal activation of the focal adhesion pathway. Oncogene 29, 6331-6342. (doi:10.1038/onc.2010.362)
46. Chang JT et al. 2009 A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol. Cell 34, 104-114. (doi:10.1016/j.molcel.2009. 02.030)
47. Subramanian A et al. 2005 Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15 545-15 550. (doi:10.1073/pnas.0506580102)
48. Tripathi S, Emmert-Streib F. 2012 Assessment method for a power analysis to identify differentially expressed pathways. PLoS ONE 7, e37510. (doi:10.1371/journal.pone.0037510)
49. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. 2011 Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740. (doi:10.1093/bioinformatics/btr260)
50. Yang D et al. 2008 Gaining confidence in biological interpretation of the microarray data: the functional consistence of the significant GO categories. Bioinformatics 24, 265-271. (doi:10.1093/bioinformatics/btm558)
51. Nilsson B, Hakansson P, Johansson M, Nelander S, Fioretos T. 2007 Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies. Genome Biol. 8, R74. (doi:10.1186/gb-2007-8-5-r74)
52. Shen X, He Z, Li H, Yao C, Zhang Y, He L, Li S, Huang J, Guo Z. 2012 Distinct functional patterns of gene promoter hypomethylation and hypermethylation in cancer genomes. PLoS ONE 7, e44822. (doi:10.1371/journal. pone.0044822)