biDCG: A new method for discovering global features of DNA microarray data via an iterative re-clustering procedure

PLoS ONE

Volumn 9, Issue 7, 2014, Pages

  Chen, Chia Pei ^a   Fushing, Hsieh ^a   Atwill, Rob ^a   Koehl, Patrice ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

ANALYTICAL PARAMETERS; ARTICLE; BICLUSTERING TECHNIQUE; CANCER CLASSIFICATION; CANCER GENETICS; CARCINOID; CONTROLLED STUDY; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC ALGORITHM; GENETIC IDENTIFICATION; GENOTYPE PHENOTYPE CORRELATION; HUMAN; INFORMATION PROCESSING; LUNG ADENOCARCINOMA; LUNG SQUAMOUS CELL CARCINOMA; MAJOR CLINICAL STUDY; NOISE; ROBUSTNESS; SIMULATION; VALIDATION STUDY; ADENOCARCINOMA; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; GENETICS; LUNG TUMOR; PROCEDURES;

ADENOCARCINOMA; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; HUMANS; LUNG NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED;

EID: 84904575821     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0102445     Document Type: Article

References (52)

1. DeRisi J, Penland L, Brown P, Bittner M, Meltzer P, et al. (1996) Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics 14: 457-460. (Pubitemid 26414590)
2. Golub T, Slonim D, Tamayo C, Huard M, Gassenberg M, et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-537.
3. Alizadeh A, Eisen M, Davis R, Ma C, Lossos I, et al. (2000) Distinct types of diffuse lage b-cell lymphoma identified by gene expression profiling. Nature 403: 503-511. (Pubitemid 30082188)
4. Ross D, Scherf U, Eisen M, Perou C, Rees C, et al. (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 24: 227-235. (Pubitemid 30132189)
5. van't Veer L, Dai H, van de Vijver M, He Y, Hart A, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530-536. (Pubitemid 34130608)
6. MacGregor P, Squire J (2002) Application of microarrays to the analysis of gene expression in cancer. Clinical Chemistry 48: 1170-1177.
7. Cummins M, Chambers R, Simpson P, Lakhani S (2011) Molecular classification of breast cancer: is it time to pack up our microscopes. Pathology 43: 1-8.
8. Schena M, Shalon D, Davis R, Brown P (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270: 467-470.
9. Lashkari D, DeRisi J, McCusker J, Mamath A, Gentile C, et al. (1997) Yeast microarrays for genome wide parallel genetic and gene expresson analysis. Proc Natl Acad Sci (USA) 94: 13057-13062. (Pubitemid 27518475)
10. Churchill G (2002) Fundamentals of experimental design for cDNA microarrays. Nature Genetics 32: 490-495.
11. Eisen M, Spellman P, Brown P, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci (USA) 95: 14863-14868. (Pubitemid 29003722)
12. Alon U, Barkai N, Notterman D, Gish K, Ybarra S, et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci (USA) 96: 6745-6750. (Pubitemid 29274954)
13. Hartigan J (1975) Clustering algorithms. New York, NY: Wiley.
14. Clarke P, Poele R, Wooster R (2001) Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential. Biochem Pharmacol 62: 1311-1336. (Pubitemid 33044679)
15. Slonim D (2002) From patterns to pathways: gene expression data analysis comes of age. Nature Genet 32: 502-508.
16. Madeira S, Oliveira A (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinf 1: 24-45.
17. Belacel N, Wang Q, Cuperlovic-Culf M (2006) Clustering methods for microarray gene expression data. OMICS 10: 507-531. (Pubitemid 46174583)
18. Gollub J, Sherlock G (2006) Clustering microarray data. Methods Enzymol 411: 194-213.
19. Prelić A, Bleuler S, Zimmermann P, Wille A, Bühlmann P, et al. (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22: 1122-1129.
20. Wiltgen M, Tilz G (2007) DNA microarray analysis: principles and clinical impact. Hematology 12: 271-287. (Pubitemid 47180653)
21. DiMaggio P Jr, McAllister S, Floudas C, Feng XJ, Rabinowitz J, et al. (2008) Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9: 458.
22. Kerr G, Ruskin H, Crane M, Doolan P (2008) Techniques for clustering gene expression data. Comput Biol Med 38: 283-293.
23. de Souto M, Costa I, de Araujo D, Ludermir T, Schliep A (2008) Clustering cancer gene expression data: a comparative study. BMC Bioinformatics 9: 497.
24. Kossenkov A, Ochs M (2010) Matrix factorisation methods applied in microarray data analysis. Int J Data Min Bioinform 4: 72-90.
25. Nugent R, Meila M (2010) An overview of clustering applied to molecular biology. Methods Mol Biol 620: 369-404.
26. Xu R, II DW (2010) Clustering algorithms in biomedical research. IEEE Rev Biomed Eng 3: 120-153.
27. Eren K, Deveci M, Küçüktunç O, Çatalyürek U (2013) A comparative analysis of biclustering algorithms for gene expression data. Briefings in Bioinformatics 14: 279-292.
28. Getz G, Levine E, Domany E (2000) Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci (USA) 97: 12079-12084.
29. Getz G, Gal H, Kela I, Notterman D, Domany E (2003) Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data. Bioinformatics 19: 1079-1089. (Pubitemid 36790009)
30. Getz G, Domany E (2003) Coupled two-way clustering server. Bioinformatics 19: 1153-1154. (Pubitemid 36790018)
31. Tang C, Zhang L, Zhang A, Ramanathan M (2001) Interrelated two-way clustering: an unsupervised approach for gene expression data analysis. In: Proc. 2nd IEEE International Symposium on Bioinformatics and Bioengineering (BIBE). pp.41-48.
32. Chandra B, Shanker S, Mishra S (2006) A new approach: interrelated two-way clustering of gene expression data. Statistical Methodology 3: 93-102. (Pubitemid 43339344)
33. Blatt M, Wiseman S, Domany E (1996) Supermagnetic clustering of data. Phys Rev Letters 76: 3253-3256.
34. Blatt M, Wiseman S, Domany E (1997) Data clustering using a model granular magnet. Neural Computation 9: 1805-1842. (Pubitemid 127462307)
35. Fushing H, McAssey M (2010) Time, temperature and data cloud geometry. Phys Rev E 82: 061110.
36. Fushing H, Wang H, der Waal KV, McCowan B, Koehl P (2013) Multi-scale clustering by building a robust and self-correcting ultrametric topology on data points. PLoS One 8: e56259.
37. Wang H, Chen C, Fushing H (2012) Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders. PLoS ONE 7: e45502.
38. Chen C, Fushing H (2012) Multiscale community geometry in a network and its application. Phys Rev E 86: 041120.
39. Bhattacharjee A, Richards W, Staunton J, Li C, Monti S, et al. (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci (USA) 98: 13790-13795. (Pubitemid 33115977)
40. Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, et al. (2002) Revealing modular organization in the yeast transcriptional network. Nature Genetics 31: 370-377. (Pubitemid 35154445)
41. Serin A, Vingron M (2011) DeBi: discovering differentially expressed biclusters using a frequent itemset approach. Algorithm Mol Biol 6: 18.
42. Flores J, Inza I, Larranaga P, Calvo B (2013) A new measure for gene expression biclustering based on non-parametric correlation. Comput Meth Prog Bio 112: 367-397.
43. Tanay A, Sharan R, Shamir R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18: S136-S144. (Pubitemid 41185647)
44. Cheng Y, Church G (2000) Biclustering of expression data. In: Proc. Int. Conf. Intell. Syst. Mol. Biol. (ISMB). pp 93-103.
45. Ben-Dor A, Chor B, Karp R, Yakhini Z (2002) Discovering local structure in gene expression data: the order-preserving sub-matrix problem. In: Proceedings of the 6th Annual International Conference on Computational Biology. pp. 49-57.
46. Murali T, Kasif S (2003) Extracting conserved gene expression motifs from gene expression data. In: Proceedings of the 8th Pacific Symposium on Biocomputing. pp. 77-88.
47. Consortium TGO (2000) Gene Ontology: tool for the unification of biology. Nature Genet 25: 25-29.
48. Tchagang A, Gawronski A, Bérubé H, Phan S, Famili F, et al. (2010) GOAL: a software tool for assessing biological significance of genes groups. BMC Bioinformatics 11: 229.
49. Dennis G Jr, Sherman B, Hosack D, Yang J, Baseler M, et al. (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biology 4: P3.
50. Hochberg Y, Benjamini Y (1990) More powerful procedures for multiple significance testing. Stat Med 9: 811-818. (Pubitemid 20343989)
51. Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): Theory and results. In: Advances in knowledge discovery and data mining. Cambridge, MA: MIT Press, pp. 153-180.
52. Lazzeroni L, Owen A (2000) Plaid models for gene expression data. Statistica Sinica 12: 61-86.