Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL): A Robust Method for Selection of Cluster Number, K

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Sweeney, Timothy E ^a,b   Chen, Albert C ^a   Gevaert, Olivier ^b  

^a STANFORD UNIVERSITY   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGY; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENETIC DATABASE; GENETICS; HUMAN; HUMAN GENOME; NEOPLASM; PROCEDURES; REPRODUCIBILITY;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, GENETIC; GENOME, HUMAN; HUMANS; NEOPLASMS; REPRODUCIBILITY OF RESULTS;

EID: 84947550092     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep16971     Document Type: Article

References (30)

1. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98-110, doi: 10.1016/j.ccr.2009.12.020 (2010).
2. Phillips, H. S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157-173, doi: 10.1016/j.ccr.2006.02.019 (2006).
3. Patel, V. N. et al. Network signatures of survival in glioblastoma multiforme. PLoS Comput Biol 9, e1003237, doi: 10.1371/journal. pcbi.1003237 (2013).
4. Rousseeuw, P. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics 20, 53-65 (1987).
5. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572-1573, doi: 10.1093/bioinformatics/btq170 (2010).
6. Laan, M. & Pollard, K. Hybrid clustering of gene expression data with visualization and the bootstrap. Journal of Statistical Planning and Infererence 117, 275-303 (2003).
7. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. Cluster: Cluster Analysis Basics and Extensions. R package version 2.0.3. (http://cran.r-project.org/web/packages/cluster/, Accessed 10-15-2014).
8. Brock, G., Pihur, V., Datta, S. & Datta, S. clValid: An R Package for Cluster Validation. Journal of Statistical Software 25(4), 1-22 (2008).
9. Kohonen, T. Self-organizing maps. 3rd edn, (Springer, 2001).
10. Kaufman, L. & Rousseeuw, P. J. Finding groups in data: An introduction to cluster analysis. (Wiley, 2005).
11. Herrero, J., Valencia, A. & Dopazo, J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17, 126-136, doi: 10.1093/bioinformatics/17.2.126 (2001).
12. Hennig, C. R package fpc (http://cran.r-project.org/web/packages/fpc/, accessed 10-15-2015).
13. Calinski, T. A Dendrite Method for Cluster Analysis. Biometrics 24, 207-& (1968).
14. Handl, J., Knowles, J. & Kell, D. Computational cluster validation in post-genomic data analysis. Bioinformatics 21, 3201-3212, doi: 10.1093/bioinformatics/bti517 (2005).
15. Halkidi, M., Batistakis, Y. & Vazirgiannis, M. On clustering validation techniques. Journal of Intelligent Information Systems 17, 107-145, doi: 10.1023/A:1012801612483 (2001).
16. Meila, M. Comparing clusterings-An information based distance. Journal of Multivariate Analysis 98, 873-895, doi: 10.1016/j. jmva.2006.11.013 (2007).
17. Milligan, G. & Cooper, M. An Examination of Procedures for Determining the number of Clusters in a Data Set. Psychometrika 50, 159-179, doi: 10.1007/BF02294245 (1985).
18. Gordon, A. D. Classification. 2nd edn, (Chapman & Hall/CRC, 1999).
19. Hennig, C. & Liao, T. How to find an appropriate clustering for mixed-Type variables with application to socio-economic stratification. Journal of the Royal Statistical Society Series C-Applied Statistics 62, 309-369, doi: 10.1111/j.1467-9876.2012.01066.x (2013).
20. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society Series B-Statistical Methodology 63, 411-423, doi: 10.1111/1467-9868.00293 (2001).
21. Adler D. & Murdoch D. rgl: 3D visualization device system using OpenGL (http://cran.r-project.org/web/packages/rgl/, accessed 10-15-2015).
22. Maitra, R. & Melnykov, V. Simulating data to study performance of finite mixture modelingand clustering algorithms. The Journal of Computational and Graphical Statistics 2(19), 354-376 (2010).
23. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999).
24. Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367, doi: 10.1186/1471-2105-11-367 (2010).
25. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61-70, doi: 10.1038/nature11412 (2012).
26. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330-337, doi: 10.1038/nature11252 (2012).
27. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061-1068, doi: 10.1038/nature07385 (2008).
28. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43-49, doi: 10.1038/nature12222 (2013).
29. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519-525, doi: 10.1038/nature11404 (2012).
30. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615, doi: 10.1038/nature10166 (2011).