MAD Bayes for Tumor Heterogeneity—Feature Allocation With Exponential Family Sampling

REFERENCES

• Andor, N., Harness, J.V., Müller, S., Mewes, H.W., Petritsch, C. (2014), Expands: Expanding Ploidy and Allele Frequency on Nested Subpopulations, Bioinformatics, 30, 50–60.
   PubMed Web of Science ®Google Scholar
• Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J. (2005), Clustering With Bregman Divergences, The Journal of Machine Learning Research, 6, 1705–1749.
   Web of Science ®Google Scholar
• Bregman, L.M. (1967), The Relaxation Method of Finding the Common Point of Convex Sets and Its Application to the Solution of Problems in Convex Programming, USSR Computational Mathematics and Mathematical Physics, 7, 200–217.
   Google Scholar
• Broderick, T., Jordan, M.I., Pitman, J. (2012a), Beta Processes, Stick-Breaking and Power Laws, Bayesian Analysis, 7, 439–476.
   Web of Science ®Google Scholar
• Broderick, T., Kulis, B., Jordan, M.I. (2012b), Mad-Bayes: Map-Based Asymptotic Derivations From Bayes,” in Proceedings of The 30th International Conference on Machine Learning, pp. 226–234.
   Google Scholar
• Dempster, A.P., Laird, N.M., Rubin, D.B. (1977), Maximum Likelihood From Incomplete Data via the em Algorithm, Journal of the Royal Statistical Society, Series B, 1–38.
   Google Scholar
• Ding, L., Ley, T. J., Larson, D. E., Miller, C. A., Koboldt, D. C., Welch, J. S., Ritchey, J. K., Young, M. A., Lamprecht, T., McLellan, M. D., McMichael, J. F., Wallis, J. W., Lu, C., Shen, D., Harris, C. C., Dooling, D. J., Fulton, R. S., Fulton, L. L., Chen, K., Schmidt, H., Kalicki-Veizer, J., Magrini, V. J., Cook, L., McGrath, S. D., Vickery, T. L., Wendl, M. C., Heath, S., Watson, M. A., Link, D. C., Tomasson, M. H., Shannon, W. D., Payton, J. E., Kulkarni, S., Westervelt, P., Walter, M. J., Graubert, T. A., Mardis, E. R., Wilson, R. K., and DiPersio, J. F. (2012), Clonal Evolution in Relapsed Acute Myeloid Leukaemia Revealed by Whole-Genome Sequencing, Nature, 481, 506–510.
   PubMed Web of Science ®Google Scholar
• Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., Varela, I., Phillimore, B., Begum, S., McDonald, N. Q., Butler, A., Jones, D., Raine, K., Latimer, C., Santos, C. R., Nohadani, M., Eklund, A. C., Spencer-Dene, B., Clark, G., Pickering, L., Stamp, G., Gore, M., Szallasi, Z., Downward, J., Futreal, P. A., and Swanton, C. (2012), Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing, New England Journal of Medicine, 366, 883–892.
   PubMed Web of Science ®Google Scholar
• Ghoshal, S. (2010), “The Dirichlet Process, Related Priors and Posterior Asymptotics,” in Bayesian Nonparametrics, eds. N. L. Hjort, C. Holmes, P. M., and S. G. Walker, Cambridge, UK: Cambridge University Press, pp. 22–34.
   Google Scholar
• Green, P.J. (1995), Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination, Biometrika, 82, 711–732.
   Web of Science ®Google Scholar
• Griffiths, T.L., and Ghahramani, Z. (2006), “Infinite Latent Feature Models and the Indian Buffet Process,” in Advances in Neural Information Processing Systems ( Vol. 18), eds. Y. Weiss, B. Schölkopf, and J. Platt. Cambridge, MA: MIT Press, pp. 475–482.
   Google Scholar
• Hartigan, J.A., Wong, M.A. (1979), Algorithm as 136: A k-Means Clustering Algorithm, Journal of the Royal Statistical Society, Series C, 28, 100–108.
   Google Scholar
• Hastie, T., Tibshirani, R., and Friedman, J. J. H. (2001), The Elements of Statistical Learning ( Vol. 1), New York: Springer.
   Google Scholar
• Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., Hirakawa, M. (2010), Kegg for Representation and Analysis of Molecular Networks Involving Diseases and Drugs, Nucleic Acids Research, 38, D355–D360.
   PubMed Web of Science ®Google Scholar
• Karlof, J.K. (2005), Integer Programming: Theory and Practice, Boca Raton, FL: CRC Press.
   Google Scholar
• Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., Jones, S., Marra, M. (2009), Circos: An Information Aesthetic for Comparative Genomics, Genome Research, 19, 1639–1645.
   PubMed Web of Science ®Google Scholar
• Kulis, B., Jordan, M.I. (2011), Revisiting k-Means: New Algorithms via Bayesian Nonparametrics, in Proceedings of the 29th International Conference on Machine Learning (ICML-12), pp. 513–520.
   Google Scholar
• Landau, D.A., Carter, S.L., Stojanov, P., McKenna, A., Stevenson, K., Lawrence, M.S., Sougnez, C., Stewart, C., Sivachenko, A., Wang, L., (2013), Evolution and Impact of Subclonal Mutations in Chronic Lymphocytic Leukemia, Cell, 152, 714–726.
   PubMed Web of Science ®Google Scholar
• Larson, N.B., Fridley, B.L. (2013), Purbayes: Estimating Tumor Cellularity and Subclonality in Next-Generation Sequencing Data, Bioinformatics, 29, 1888–1889.
   PubMed Web of Science ®Google Scholar
• Lee, J., Müller, P., Ji, Y., Gulukota, K. (2013), A Feature Allocation Model for Tumor Heterogeneity,” The Annals of Applied Statistics, forthcoming.
   PubMed Web of Science ®Google Scholar
• Liu, J.S. (2008), Monte Carlo Strategies in Scientific Computing, New York: Springer.
   Google Scholar
• McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., Mark, D. (2010), The Genome Analysis Toolkit: A MapReduce Framework for Analyzing Next-Generation DNA Sequencing Data, Genome Research, 20, 1297–1303.
   PubMed Web of Science ®Google Scholar
• Roth, A., Khattra, J., Yap, D., Wan, A., Laks, E., Biele, J., Ha, G., Aparicio, S., Bouchard-Côté, A., Shah, S.P. (2014), Pyclone: Statistical Inference of Clonal Population Structure in Cancer, Nature Methods, 11, 396–398.
   PubMed Web of Science ®Google Scholar
• Teh, Y.W., Görür, D., Ghahramani, Z. (2007), Stick-Breaking Construction for the Indian Buffet Process, in International Conference on Artificial Intelligence and Statistics, pp. 556–563.
   Google Scholar
• Thibaux, R., Jordan, M. (2007), Hierarchical Beta Processes and the Indian Buffet Process, in Proceedings of the 11th Conference on Artificial Intelligence and Statistics (AISTAT), Puerto Rico.
   Google Scholar