Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies Among Observations

REFERENCES

• Barski, A., Cuddapah, S., Cui, K., Roh, T.Y., Schones, D.E., Wang, Z., Wei, G., Chepelev, I., Zhao, K., et al. (2007), “High-Resolution Profiling of Histone Methylations in the Human Genome,” Cell, 129, 823–837.
   PubMed Web of Science ®Google Scholar
• Bernstein, B.E., Birney, E., Dunham, I., Green, E.D., Gunter, C., Snyder, M., et al. (2012), “An Integrated Encyclopedia of DNA Elements in the Human Genome,” Nature, 489, 57.
   PubMed Web of Science ®Google Scholar
• Bickel, P.J., Ritov, Y., and Ryden, T. (1998), “Asymptotic Normality of the Maximum-Likelihood Estimator for General Hidden Markov Models,” The Annals of Statistics, 26, 1614–1635.
   Web of Science ®Google Scholar
• Boyle, A.P., Guinney, J., Crawford, G.E., and Furey, T.S. (2008), “F-Seq: A Feature Density Estimator for High-Throughput Sequence Tags,” Bioinformatics, 24, 2537–2538.
   PubMedGoogle Scholar
• Cox, D.R., Gudmundsson, G., Lindgren, G., Bondesson, L., Harsaae, E., Laake, P., Juselius, K., and Lauritzen, S.L. (1981), “Statistical Analysis of Time Series: Some Recent Developments” (with discussion), Scandinavian Journal of Statistics, 8, 93–115.
   Web of Science ®Google Scholar
• Dunsmuir, W. T.M., and Streett, S.B. (2003), “Observation-Driven Models for Poisson Counts,” Biometrika, 90, 777–790.
   Google Scholar
• Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001), “Empirical Bayes Analysis of a Microarray Experiment,” Journal of the American Statistical Association, 96, 1151–1160.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360.
   Web of Science ®Google Scholar
• Fan, J., and Lv, J. (2010), “A Selective Overview of Variable Selection in High Dimensional Feature Space,” Statistica Sinica, 20, 101.
   PubMed Web of Science ®Google Scholar
• Felsher, D.W., Zetterberg, A., Zhu, J., Tlsty, T., and Bishop, J.M. (2000), “Overexpression of MYC Causes p53-Dependent g2 Arrest of Normal Fibroblasts,” Proceedings of the National Academy of Sciences, 97, 10544–10548.
   PubMed Web of Science ®Google Scholar
• Friedman, J.H. (2008), “Fast Sparse Regression and Classification,” International Journal of Forecasting, 28, 722–738.
   Google Scholar
• Garcia, R.I., Ibrahim, J.G., and Zhu, H. (2010), “Variable Selection for Regression Models With Missing Data,” Statistica Sinica, 20, 149.
   PubMed Web of Science ®Google Scholar
• Hilbe, J.M. (2011), Negative Binomial Regression, New York: Cambridge University Press.
   Google Scholar
• Ibrahim, J.G. (1990), “Incomplete Data in Generalized Linear Models,” Journal of the American Statistical Association, 85, 765–769.
   Web of Science ®Google Scholar
• Ji, H., Jiang, H., Ma, W., Johnson, D.S., Myers, R.M., and Wong, W.H. (2008), “An Integrated Software System for Analyzing Chip-Chip and Chip-Seq Data,” Nature Biotechnology, 26, 1293–1300.
   PubMed Web of Science ®Google Scholar
• Khalili, A., and Chen, J. (2007), “Variable Selection in Finite Mixture of Regression Models,” Journal of the American Statistical Association, 102, 1025–1038.
   Web of Science ®Google Scholar
• Kim, T.H., Abdullaev, Z.K., Smith, A.D., Ching, K.A., Loukinov, D.I., Green, R.D., Zhang, M.Q., Lobanenkov, V.V., and Ren, B. (2007), “Analysis of the Vertebrate Insulator Protein ctcf Binding Sites in the Human Genome,” Cell, 128, 1231.
   PubMed Web of Science ®Google Scholar
• Kolasinska-Zwierz, P., Down, T., Latorre, I., Liu, Liu, Ahringer, J. (2009), “Differential Chromatin Marking of Introns and Expressed Exons by h3k36me3,” Nature Genetics, 41, 376–381.
   PubMed Web of Science ®Google Scholar
• Kuan, P.F., Chung, D., Pan, G., Thomson, J.A., Stewart, R., and Keleş, S. (2011), “A Statistical Framework for the Analysis of Chip-Seq Data,” Journal of the American Statistical Association, 106, 891–903.
   PubMed Web of Science ®Google Scholar
• Li, B., Gogol, M., Carey, M., Lee, D., Seidel, C., and Workman, J.L. (2007), “Combined Action of phd and Chromo Domains Directs the rpd3s hdac to Transcribed Chromatin,” Science, 316, 1050–1054.
   PubMed Web of Science ®Google Scholar
• Li, F., Mao, G., Tong, D., Huang, J., Gu, L., Yang, W., and Li, G.-M. (2013), “The Histone Mark h3k36me3 Regulates Human DNA Mismatch Repair Through Its Interaction With mutsα,” Cell, 153, 590–600.
   PubMed Web of Science ®Google Scholar
• Liu, C.-J., Prazak, L., Fajardo, M., Yu, S., Tyagi, N., and Cesare, P.E. (2004), “Leukemia/Lymphoma-Related Factor, a POZ Domain-Containing Transcriptional Repressor, Interacts With Histone Deacetylase-1 and Inhibits Cartilage Oligomeric Matrix Protein Gene Expression and Chondrogenesis,” Journal of Biological Chemistry, 279, 47081–47091.
   PubMedGoogle Scholar
• Lozzio, C.B., and Lozzio, B.B. (1975), “Human Chronic Myelogenous Leukemia Cell-Line With Positive Philadelphia Chromosome,” Blood, 45, 321–334.
   PubMed Web of Science ®Google Scholar
• Lystig, T.C., and Hughes, J.P. (2002), “Exact Computation of the Observed Information Matrix for Hidden Markov Models,” Journal of Computational and Graphical Statistics, 11, 678–689.
   Web of Science ®Google Scholar
• McLachlan, G.J. (1997), “On the EM Algorithm for Overdispersed Count Data,” Statistical Methods in Medical Research, 6, 76–98.
   PubMedGoogle Scholar
• Meng, X.-L., and Rubin, D.B. (1993), “Maximum Likelihood Estimation via the ECM Algorithm: A General Framework,” Biometrika, 80, 267–278.
   Web of Science ®Google Scholar
• Qin, Z.S., Yu, J., Shen, J., Maher, C.A., Hu, M., Kalyana-Sundaram, S., Yu, J., and Chinnaiyan, A.M. (2010), “Hpeak: An HMM-Based Algorithm for Defining Read-Enriched Regions in ChIP-Seq Data,” BMC Bioinformatics, 11, 369.
   PubMedGoogle Scholar
• Rashid, N.U., Giresi, P.G., Ibrahim, J.G., Sun, W., and Lieb, J.D. (2011), “ZINBA Integrates Local Covariates With DNA-Seq Data to Identify Broad and Narrow Regions of Enrichment, Even Within Amplified Genomic Regions,” Genome Biology, 12, R67.
   PubMed Web of Science ®Google Scholar
• Roberts, C. W.M., and Orkin, S.H. (2004), “The swi/snf Complex chromatin and Cancer,” Nature Reviews Cancer, 4, 133–142.
   PubMed Web of Science ®Google Scholar
• Rozowsky, J., Euskirchen, G., Auerbach, R.K., Zhang, Z.D., Gibson, T., Bjornson, R., Carriero, N., Snyder, M., and Gerstein, M.B. (2009), “Peakseq Enables Systematic Scoring of ChIP-Seq Experiments Relative to Controls,” Nature Biotechnology, 27, 66–75.
   PubMed Web of Science ®Google Scholar
• Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461–464.
   Web of Science ®Google Scholar
• Spyrou, C., Stark, R., Lynch, A.G., and Tavaré, S. (2009), “Bayespeak: Bayesian Analysis of ChIP-Seq Data,” BMC Bioinformatics, 10, 299.
   PubMedGoogle Scholar
• Sun, W., Ibrahim, J.G., and Zou, F. (2010), “Genomewide Multiple-Loci Mapping in Experimental Crosses by Iterative Adaptive Penalized Regression,” Genetics, 185, 349.
   PubMed Web of Science ®Google Scholar
• Suvà, M.L., Riggi, N., and Bernstein, B.E. (2013), “Epigenetic Reprogramming in Cancer,” Science, 339, 1567–1570.
   PubMed Web of Science ®Google Scholar
• Thurman, R.E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M.T., Haugen, E., Sheffield, N.C., Stergachis, A.B., Wang, H., Vernot, B., et al. (2012), “The Accessible Chromatin Landscape of the Human Genome,” Nature, 489, 75–82.
   PubMed Web of Science ®Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
   Google Scholar
• Wang, G.G., Cai, L., Pasillas, M.P., and Kamps, M.P. (2007), “Nup98–nsd1 Links h3k36 Methylation to hox-a Gene Activation and Leukaemogenesis,” Nature Cell Biology, 9, 804–812.
   PubMed Web of Science ®Google Scholar
• Xu, H., Wei, C.L., Lin, F., and Sung, W.K. (2008), “An HMM Approach to Genome-Wide Identification of Differential Histone Modification Sites From ChIP-Seq Data,” Bioinformatics, 24, 2344–2349.
   PubMed Web of Science ®Google Scholar
• Zeger, S.L. (1988), “A Regression Model for Time Series of Counts,” Biometrika, 75, 621.
   Web of Science ®Google Scholar
• Zeger, S.L., and Qaqish, B. (1988), “Markov Regression Models for Time Series: A Quasi-Likelihood Approach,” Biometrics, 44, 1019–1031.
   PubMed Web of Science ®Google Scholar
• Zhang, C.H. (2010), “Nearly Unbiased Variable Selection Under Minimax Concave Penalty,” The Annals of Statistics, 38, 894–942.
   Web of Science ®Google Scholar
• Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nusbaum, C., Myers, R.M., Brown, M., Li, W., et al. (2008a), “Model-Based Analysis of ChIP-Seq (MACS),” Genome Biology, 9, R137.
   PubMed Web of Science ®Google Scholar
• Zhang, Z.D., Rozowsky, J., Snyder, M., Chang, J., and Gerstein, M. (2008b), “Modeling ChIP Sequencing in Silico With Applications,” PLoS Comput Biol, 4, e1000158.
   Google Scholar
• Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
   Web of Science ®Google Scholar