Portraying the expression landscapes of B-Cell lymphoma-intuitive detection of outlier samples and of molecular subtypes

Biology

Volumn 2, Issue 4, 2013, Pages 1411-1437

  Hopp, Lydia ^a   Lembcke, Kathrin ^a   Binder, Hans ^a   Wirth, Henry ^a  

^a UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

Classifying cancer; Co regulated genes; Machine learning; Molecular function; Network analysis

Indexed keywords

EID: 84901354658     PISSN: None     EISSN: 20797737     Source Type: Journal    
DOI: 10.3390/biology2041411     Document Type: Article

References (49)

1. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008, 455, 1061-1068.
2. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012, 487, 330-337.
3. Barretina, J.; Caponigro, G.; Stransky, N.; Venkatesan, K.; Margolin, A.A.; Kim, S.; Wilson, C.J.; Lehár, J.; Kryukov, G.V.; Sonkin, D.; et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012, 483, 603-607.
4. Hudson, T.J.; Anderson, W.; Artez, A.; Barker, A.D.; Bell, C.; Bernabé, R.R.; Bhan, M.K.; Calvo, F.; Eerola, I.; Gerhard, D.S.; et al. International network of cancer genome projects. Nature 2010, 464, 993-998.
5. Fernald, G.H.; Capriotti, E.; Daneshjou, R.; Karczewski, K.J.; Altman, R.B. Bioinformatics challenges for personalized medicine. Bioinformatics 2011, 27, 1741-1748.
6. Pop, M.; Salzberg, S.L. Bioinformatics challenges of new sequencing technology. Trends Genet. 2008, 24, 142-149.
7. Sboner, A.; Mu, X.J.; Greenbaum, D.; Auerbach, R.K.; Gerstein, M.B. The real cost of sequencing: Higher than you think! Genome Biol. 2011, 12, 125.
8. Mardis, E.R. The $1,000 genome, the $100,000 analysis? Genome Med. 2010, 2, 84.
9. Kohonen, T. Self Organizing Maps; Springer: Berlin, Heidelberg, Germany, New York, NY, USA, 1995.
10. Hummel, M.; Bentink, S.; Berger, H.; Klapper, W.; Wessendorf, S.; Barth, T.F.E.; Bernd, H.-W.; Cogliatti, S.B.; Dierlamm, J.; Feller, A.C.; et al. A biologic definition of lymphoma from transcriptional and genomic profiling. N. Engl. J. Med. 2006, 354, 2419-2430.
11. Wirth, H.; Loffler, M.; von Bergen, M.; Binder, H. Expression cartography of human tissues using self organizing maps. BMC Bioinform. 2011, 12, 306.
12. Wirth, H.; von Bergen, M.; Binder, H. Mining SOM expression portraits: Feature selection and integrating concepts of molecular function. BioData Min. 2012, 5, 18.
13. Binder, H.; Preibisch, S. A-calibration of GeneChip-microarrays: Theory and algorithm. Algorithms Mol. Biol. 2008, 3, 12.
14. Binder, H.; Krohn, K.; Preibisch, S. A-calibration of GeneChip-microarrays: Chip characteristics and expression measures. Algorithms Mol. Biol. 2008, 3, 11.
15. Bolstad, B.M.; Irizarry, R.A.; Astrand, M.; Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185-193.
16. Wirth, H. Analysis of large-scale molecular biological data using self-organizing maps. Available online: http://www.qucosa.de/fileadmin/data/qucosa/documents/10129/Dissertation%20Henry% 20Wirth.pdf (accessed on 14 November 2013).
17. Binder, H.; Hopp, L.; Cakir, V.; Fasold, M.; von Bergen, M.; Wirth, H. Molecular phenotypic portraits-Exploring the OMES' with individual resolution. In Proceedings of the 6th International Symposium Health Informatics and Bioinformatics (HIBIT), Izmir, Turkey, 2-5 May 2011; pp. 99-107.
18. Vesanto, J.; Himberg, J.; Alhoniemi, E.; Parhankangas, J. Self-organizing map in Matlab: The SOM toolbox. In Proceedings of the Matlab DSP Conference, Espoo, Finland, 16-17 November, 1999; pp. 35-40.
19. Yan, J. Som: Self-Organizing Map 2010. Available online: http://cran.r-project.org/web/packages/som/ (accessed on 14 November 2013).
20. Wirth, H.; von Bergen, M.; Murugaiyan, J.; Rösler, U.; Stokowy, T.; Binder, H. MALDI-typing of infectious algae of the genus Prototheca using SOM portraits. J. Microbial. Methods 2012, 88, 83-97.
21. Hopp, L.; Wirth, H.; Fasold, M.; Binder, H. Portraying the expression landscapes of cancer subtypes: A glioblastoma multiforme and prostate cancer case study. Syst. Biomed.2013, 1, in press.
22. Wirth, H.; Cakir, V.; Hopp, L.; Binder, H. Analysis of miRNA expression using machine learning. Methods Mol. Biol. 2014, 1107, in press.
23. Cakir, V.; Wirth, H.; Hopp, L.; Binder, H. miRNA expression landscapes in stem cells, tissues and cancer. Methods Mol. Biol. 2014, 1107, in press.
24. Quackenbush, J. Genomics. Microarrays-Guilt by association. Science 2003, 302, 240-241.
25. Goeman, J.J.; Bühlmann, P. Analyzing gene expression data in terms of gene sets: Methodological issues. Bioinformatics 2007, 23, 980-987.
26. Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000, 25, 25-29.
27. Haider, S.; Ballester, B.; Smedley, D.; Zhang, J.; Rice, P.; Kasprzyk, A. BioMart Central Portal-Unified access to biological data. Nucleic Acids Res. 2009, 37, W23-W27.
28. Ackermann, M.; Strimmer, K. A general modular framework for gene set enrichment analysis. BMC Bioinformatics 2009, 10, 47.
29. Zhang, B.; Kirov, S.; Snoddy, J. WebGestalt: An integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005, 33, W741-W748.
30. Vêncio, R.Z.N.; Shmulevich, I. ProbCD: Enrichment analysis accounting for categorization uncertainty. BMC Bioinformatics 2007, 8, 383.
31. Noble, W.S. How does multiple testing correction work? Nat. Biotechnol. 2009, 27, 1135-1137.
32. Törönen, P.; Ojala, P.J.; Marttinen, P.; Holm, L. Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function. BMC Bioinformatics 2009, 10, 307.
33. Guo, Y.; Eichler, G.S.; Feng, Y.; Ingber, D.E.; Huang, S. Towards a holistic, yet gene-centered analysis of gene expression profiles: A case study of human lung cancers. J. Biomed. Biotechnol. 2006, 2006, 69141.
34. Liebermeister, W. Linear modes of gene expression determined by independent component analysis. Bioinformatics 2002, 18, 51-60.
35. Hyvärinen, A.; Oja, E. Independent component analysis: Algorithms and applications. Neural Netw. 2000, 13, 411-430.
36. Paradis, E.; Claude, J.; Strimmer, K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 2004, 20, 289-290.
37. Saitou, N.; Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 1987, 4, 406-425.
38. Lloyd, S. Least squares quantization in PCM. Inf. Theory IEEE Trans. 1982, 28, 129-137.
39. Arthur, D.; Vassilvitskii, S. K-Means++: The Advantages of Careful Seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA, 7-9 January 2007.
40. Monti, S.; Tamayo, P. Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 2003, 52, 91-118.
41. Wilkerson, M.D.; Hayes, D.N. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics 2010, 26, 1572-1573.
42. Rosolowski, M.; Läuter, J.; Abramov, D.; Drexler, H.; Hummel, M.; Klapper, W.; MacLeod, R.; Pellissery, S.; Horn, F.; Siebert, R.; et al. Diffuse large B-cell lymphomas exhibit different functional and metabolic activation patterns independent of the cell of origin signature. PLoS One 2013, in press.
43. Klapper, W.; Kreuz, M.; Kohler, C.W.; Burkhardt, B.; Szczepanowski, M.; Salaverria, I.; Hummel, M.; Loeffler, M.; Pellissery, S.; Woessmann, W.; et al. Patient age at diagnosis is associated with the molecular characteristics of diffuse large B-cell lymphoma. Blood 2012, 119, 1882-1887.
44. Guengerich, F. Cytochrome p450 and chemical toxicology. Chem. Res. Toxicol. 2007, 21, 70-83.
45. Wright, G.; Tan, B.; Rosenwald, A.; Hurt, E.H.; Wiestner, A.; Staudt, L.M. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc. Natl. Acad. Sci. USA 2003, 100, 9991-9996.
46. Stein, H.; Hummel, M. Burkitt's and Burkitt-like lymphoma. Molecular definition and value the World Health Organisation's diagnostic criteria. Pathology 2007, 28, 41-45.
47. Lu, Y.; Yi, Y.; Liu, P.; Wen, W.; James, M.; Wang, D.; You, M. Common human cancer genes discovered by integrated gene-expression analysis. PLoS One 2007, 2, e1149.
48. Wolfer, A.; Wittner, B.S.; Irimia, D.; Flavin, R.J.; Lupien, M.; Gunawardane, R.N.; Meyer, C.A.; Lightcap, E.S.; Tamayo, P.; Mesirov, J.P.; et al. MYC regulation of a "poor-prognosis" metastatic cancer cell state. Proc. Natl. Acad. Sci. USA 2010, 107, 3698-3703.
49. Kaplan, E.; Meier, P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 1958, 53, 457-481.