DECODE: An integrated differential co-expression and differential expression analysis of gene expression data

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Lui, Thomas W H ^a   Tsui, Nancy B Y ^a   Chan, Lawrence W C ^a   Wong, Cesar S C ^a   Siu, Parco M F ^a   Yung, Benjamin Y M ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DECODING; GENE EXPRESSION REGULATION; GENES;

ANALYTICAL APPROACH; BIOLOGICAL FUNCTIONS; BREAST CANCER MICROARRAY DATUM; COMBINED FEATURES; DIFFERENTIAL EXPRESSIONS; GENE EXPRESSION DATA; GLUCOSE METABOLISM; OPTIMAL THRESHOLD;

GENE EXPRESSION;

ALGORITHM; BIOLOGY; BREAST TUMOR; COMPUTER PROGRAM; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; METABOLISM; PROCEDURES; PROTEIN PROTEIN INTERACTION;

ALGORITHMS; BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; PROTEIN INTERACTION MAPS; SOFTWARE;

EID: 84930633156     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0582-4     Document Type: Article

References (67)

1. Kulkarni H, Goring HHH, Diego V, Cole S, Walder KR, Collier GR, et al. Association of differential gene expression with imatinib mesylate and omacetaxine mepesuccinate toxicity in lymphoblastoid cell lines. BMC Med Genomics. 2012;5.
2. McCormick KP, Willmann MR, Meyers BC. Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. Silence. 2011;2(1):2.
3. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3).
4. Choi CH, Choi JJ, Park YA, Lee YY, Song SY, Sung CO, et al. Identification of differentially expressed genes according to chemosensitivity in advanced ovarian serous adenocarcinomas: expression of GRIA2 predicts better survival. Br J Cancer. 2012;107(1):91-9.
5. Lucas SM, Heath EI. Current challenges in development of differentially expressed and prognostic prostate cancer biomarkers. Prostate cancer. 2012;2012:640968.
6. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562-78.
7. Cui XQ, Churchill GA. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 2003;4(4).
8. Macgregor PF, Squire JA. Application of microarrays to the analysis of gene expression in cancer. Clin Chem. 2002;48(8):1170-7.
9. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL. Hierarchical organization of modularity in metabolic networks. Science. 2002;297(5586):1551-5.
10. Boutros PC, Okey AB. Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data. Brief Bioinform. 2005;6(4):331-43.
11. Do JH, Choi DK. Clustering approaches to identifying gene expression patterns from DNA microarray data. Mol Cells. 2008;25(2):279-88.
12. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95(25):14863-8.
13. Oliver S. Guilt-by-association goes global. Nature. 2000;403(6770):601-3.
14. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003;34(2):166-76.
15. Slonim DK. From patterns to pathways: gene expression data analysis comes of age. Nat Genet. 2002;32(Suppl):502-8.
16. Ben-Shaul Y, Bergman H, Soreq H. Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression. Bioinformatics. 2005;21(7):1129-37.
17. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1 alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267-73.
18. Ackermann M, Strimmer K. A general modular framework for gene set enrichment analysis. BMC Bioinformatics. 2009;10:47.
19. Goeman JJ, Buhlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics. 2007;23(8):980-7.
20. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57.
21. Minguez P, Dopazo J. Assessing the biological significance of gene expression signatures and co-expression modules by studying their network properties. PLoS One. 2011;6(3).
22. Nam D, Kim SY. Gene-set approach for expression pattern analysis. Brief Bioinform. 2008;9(3):189-97.
23. Ruan JH, Dean AK, Zhang WX. A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol. 2010;4.
24. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50.
25. Wei W, Hou J, Alder O, Ye X, Lee S, Cullum R, et al. Genome-wide microRNA and messenger RNA profiling in rodent liver development implicates mir302b and mir20a in repressing transforming growth factor-beta signaling. Hepatology. 2013;57(6):2491-501.
26. Anglani R, Creanza TM, Liuzzi VC, Piepoli A, Panza A, Andriulli A, et al. Loss of connectivity in cancer co-expression networks. PLoS One. 2014;9(1):e87075.
27. Choi JK, Yu US, Yoo OJ, Kim S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics. 2005;21(24):4348-55.
28. Choi Y, Kendziorski C. Statistical methods for gene set co-expression analysis. Bioinformatics. 2009;25(21):2780-6.
29. Watson M. CoXpress: differential co-expression in gene expression data. BMC Bioinformatics. 2006;7.
30. de la Fuente A. From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. Trends Genet. 2010;26(7):326-33.
31. Hudson NJ, Reverter A, Dalrymple BP. A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. Plos Comput Biol. 2009;5(5).
32. Hu R, Qiu X, Glazko G, Klebanov L, Yakovlev A. Detecting intergene correlation changes in microarray analysis: a new approach to gene selection. BMC Bioinformatics. 2009;10:20.
33. Mentzen WI, Floris M, de la Fuente A. Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor. BMC Genomics. 2009;10:601.
34. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.
35. Bockmayr M, Klauschen F, Gyorffy B, Denkert C, Budczies J. New network topology approaches reveal differential correlation patterns in breast cancer. BMC Syst Biol. 2013;7.
36. Cho SB, Kim J, Kim JH. Identifying set-wise differential co-expression in gene expression microarray data. BMC Bioinformatics. 2009;10.
37. Mo WJ, Fu XP, Han XT, Yang GY, Zhang JG, Guo FH, et al. A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression. BMC Genomics. 2009;10.
38. Yu H, Lin CC, Li YY, Zhao ZM. Dynamic protein interaction modules in human hepatocellular carcinoma progression. BMC Syst Biol. 2013;7.
39. Gayen AK. The frequency distribution of the product-moment correlation coefficient in random samples of any size drawn from non-normal universes. Biometrika (Biometrika Trust). 1951;38(1/2):219-47.
40. Sachs L. Applied statistics, a handbook of techniques, 2nd Edition. Percept Motor Skill. 1985;60(3):1011.
41. Ching JY, Wong AKC, Chan KCC. Class-dependent discretization for inductive learning from continuous and mixed-mode data. Ieee T Pattern Anal. 1995;17(7):641-51.
42. Bonferroni CE. Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze. 1936;8:3-62.
43. Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995;57(1):289-300.
44. Agresti A. An introduction to categorical data analysis. New York: Wiley; 1996.
45. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-9.
46. Dolinski K, Botstein D. Automating the construction of gene ontologies. Nat Biotechnol. 2013;31(1):34-5.
47. Croft D, O'Kelly G, Wu GM, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39:D691-7.
48. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(D1):D109-14.
49. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27(1):29-34.
50. Fisher RA. On the interpretation of χ2 from contingency tables, and the calculation of P. J Roy Statist Soc. 1922;85:87-94.
51. Suresh K, Chandrashekara S. Sample size estimation and power analysis for clinical research studies. J Hum Reprod Sci. 2012;5(1):7-13.
52. Zar JH. Biostatistical analysis. 5th ed. Englewood Cliffs: Prentice Hall; 2010. p. 393.
53. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346-52.
54. Hervas-Stubbs S, Perez-Gracia JL, Rouzaut A, Sanmamed MF, Le Bon A, Melero I. Direct effects of type I interferons on cells of the immune system. Clin Cancer Res. 2011;17(9):2619-27.
55. Fuertes MB, Woo SR, Burnett B, Fu YX, Gajewski TF. Type I interferon response and innate immune sensing of cancer. Trends Immunol. 2013;34(2):67-73.
56. Slaney CY, Moller A, Hertzog PJ, Parker BS. The role of Type I interferons in immunoregulation of breast cancer metastasis to the bone. Oncoimmunology. 2013;2(1):e22339.
57. Bendas G, Borsig L. Cancer cell adhesion and metastasis: selectins, integrins, and the inhibitory potential of heparins. Int J Cell Biol. 2012;2012:676731.
58. Baenke F, Peck B, Miess H, Schulze A. Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis Model Mech. 2013;6(6):1353-63.
59. Carracedo A, Cantley LC, Pandolfi PP. Cancer metabolism: fatty acid oxidation in the limelight. Nat Rev Cancer. 2013;13(4):227-32.
60. Koppenol WH, Bounds PL, Dang CV. Otto Warburg's contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011;11(5):325-37.
61. Duan ZJ, Gao B, Xu W, Xiong SD. Identification of TRIM22 as a RING finger E3 ubiquitin ligase. Biochem Bioph Res Co. 2008;374(3):502-6.
62. Barr SD, Smiley JR, Bushman FD. The interferon response inhibits HIV particle production by induction of TRIM22. PLoS Pathog. 2008;4(2), e1000007.
63. Singh R, Gaiha G, Werner L, McKim K, Mlisana K, Luban J, et al. Association of TRIM22 with the type 1 interferon response and viral control during primary HIV-1 infection. J Virol. 2011;85(1):208-16.
64. Singh R, Patel V, Mureithi MW, Naranbhai V, Ramsuran D, Tulsi S, et al. TRIM5alpha and TRIM22 are differentially regulated according to HIV-1 infection phase and compartment. J Virol. 2014;88(8):4291-303.
65. Tissot C, Mechti N. Molecular-cloning of a New interferon-induced factor that represses human-immunodeficiency-virus type-1 long terminal repeat expression. J Biol Chem. 1995;270(25):14891-8.
66. Obad S, Brunnstrom H, Vallon-Christersson J, Borg A, Drott K, Gullberg U. Staf50 is a novel p53 target gene conferring reduced clonogenic growth of leukemic U-937 cells. Oncogene. 2004;23(23):4050-9.
67. Pau Ni IB, Zakaria Z, Muhammad R, Abdullah N, Ibrahim N, Aina Emran N, et al. Gene expression patterns distinguish breast carcinomas from normal breast tissues: the Malaysian context. Pathol Res Pract. 2010;206(4):223-8.