Robust unmixing of tumor states in array comparative genomic hybridization data

Bioinformatics

Volumn 26, Issue 12, 2010, Pages

  Tolliver, David ^a   Tsourakakis, Charalampos ^a   Subramanian, Ayshwarya ^b   Shackney, Stanley ^c   Schwartz, Russell ^a,b  

^a Carnegie Mellon University   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c DREXEL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; COMPARATIVE GENOMIC HYBRIDIZATION; DNA MICROARRAY; GENE DOSAGE; GENE EXPRESSION PROFILING; GENETICS; METHODOLOGY; NEOPLASM;

ALGORITHMS; COMPARATIVE GENOMIC HYBRIDIZATION; GENE DOSAGE; GENE EXPRESSION PROFILING; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 77954182959     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq213     Document Type: Article

References (33)

1. Atkins, J.H. and Gershell, L.J. (2002) From the analyst's couch: selective anticancer drugs. Nat. Rev. Cancer, 2, 645-646.
2. Beerenwinkel, N. et al. (2005) Mtreemix: a software package for learning and using mixture models of mutagenetic trees. Bioinformatics, 21, 2106-2107.
3. Bild, A.H. et al. (2006) Opinion: linking oncogenic pathways with therapeutic opportunities. Nat. Rev. Cancer, 6, 735-741.
4. Boyd, S. and Vandenberghe, L. (2004) Convex Optimization. Cambridge University Press, New York, NY.
5. Chan, T. et al. (2009) A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. IEEE Trans. Signal Proc., 57, 4418-4432.
6. Comon, P. (1994) Independent component analysis. Signal Proc., 36, 287-314.
7. Desper, R. et al. (1999) Inferring tree models for oncogenesis from comparative genome hybridization data. J. Comp. Biol., 6, 37-51.
8. Ehrlich, R. and Full, W. (1987) Sorting out geology - unmixing mixtures. In Use and Abuse of Statistical Methods in the Earth Sciences, Oxford University Press, pp. 33-46.
9. Etzioni, R. et al. (2005) Analyzing patterns of staining in immunohistochemical studies: application to a study of prostate cancer recurrence. Cancer Epidemiol Biomarkers Prev., 14, 1040-1046.
10. Gerstung, M. et al. (2009) Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics, 25, 2809-2815.
11. Golub, T.R. et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
12. Guha, S. et al. (2006) Bayesian hidden Markov modeling of array CGH data. Paper 24. Harvard University.
13. Hglund, M. et al. (2001) Multivariate analyses of genomic imbalances in solid tumors reveal distinct and converging pathways of karyotypic evolution. Genes Chromosomes Cancer.
14. Kamb, A. et al. (2007) Why is cancer drug discovery so difficult? Nat. Rev. Drug Discov., 6, 115-120.
15. Lamy, P. et al. (2007)Ahidden Markov model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays. BMC Bioinformatics, 8, 434.
16. Lee, D. and Seung, H. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788-791.
17. Liu, J. et al. (2006) Distance-based clustering of CGH data. Bioinformatics, 22, 1971-1978.
18. Navin, N. et al. (2010) Inferring tumor progression from genomic heterogeneity. Genome Res., 20, 68-80.
19. Olshen, A.B. et al. (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5, 557-572.
20. Packer, A. (2002) NP-hardness of largest contained and smallest containing simplices for v-and h-polytopes. Discrete Comput. Geom., 28, 349-377.
21. Paik, S. et al. (2008) Her2 status and benefit from adjuvant trastuzumab in breast cancer. N. Engl. J. Med., 358, 1409-1411.
22. Pearson, K. (1901) On lines and planes of closest fit to systems of points in space. Philos. Mag., 2, 559-572.
23. Pegram, M.D. et al. (2000) The molecular and cellular biology of her2/neu gene amplification/overexpression and the clinical development of herceptin (trastuzumab) therapy for breast cancer. Cancer Treat. Res., 103, 57-75.
24. Pennington, G. et al. (2007) Reconstructing tumor phylogenies from single-cell data. J. Bioinform. Comput. Biol., 5, 407-427.
25. Perou, C.M. et al. (2000) Molecular portraits of human breast tumors. Nature, 406, 747-752.
26. Schölkopf, B. and Smola, A.J. (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
27. Schölkopf, B. et al. (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput., 10, 1299-1319.
28. Schwartz, R. and Shackney, S. (2010) Applying unmixing to gene expression data for tumor phylogeny inference. BMC Bioinformatics, 11, 42.
29. Shackney, S.E. et al. (2004) Intracellular patterns of Her-2/neu, ras, and ploidy abnormalities in primary human breast cancers predict postoperative clinical disease-free survival. Clin. Cancer Res., 10, 3042-3052.
30. Sorlie, T. et al. (2001) Gene expression profiles of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA, 98, 10869-10864.
31. Sorlie, T. et al. (2003) Repeated observation of breast tumor subtypes in indepednent gene expression data sets. Proc. Natl Acad. Sci. USA, 100, 8418-8423.
32. Sotiriou, C. et al. (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl Acad. Sci. USA, 100, 10393-10398.
33. 3. Proceedings of the Eleventh Annual ACM/SIAM Symposium on Discrete Algorithms, pp. 500-509.