Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model

Biometrics

Volumn 66, Issue 4, 2010, Pages 1284-1294

  Mo, Qianxing ^a   Liang, Faming ^b  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Affymetrix tiling arrays; Agilent promoter arrays; Bayesian hierarchical; ChIP chip; Gibbs sampler; Hidden Markov random field; Ising model; Spatial statistics

Indexed keywords

ALKYLATION; BAYESIAN NETWORKS; DNA; GENOME; HIERARCHICAL SYSTEMS; ISING MODEL; METHYLATION; NUMERICAL METHODS; PROBES; PROTEINS;

AFFYMETRIX; AFFYMETRIX TILING ARRAY; AGILEND PROMOTER ARRAY; AGILENT; BAYESIAN; BAYESIAN HIERARCHICAL; CHROMATIN IMMUNOPRECIPITATION; CHROMATIN IMMUNOPRECIPITATION-CHIP; GIBBS SAMPLERS; HIDDEN MARKOV RANDOM FIELDS; SPATIAL STATISTICS; TILING ARRAY;

HIDDEN MARKOV MODELS;

BAYESIAN ANALYSIS; CHROMOSOME; DATA SET; EXPERIMENTAL STUDY; GENOME; METHYLATION; NUMERICAL MODEL; PROTEIN; SAMPLER; SPATIAL ANALYSIS;

EID: 78650053836     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01379.x     Document Type: Article

References (17)

1. Bernstein, B. E., Kamal, M., Lindblad-Toh, K., Bekiranov, S., Bailey, D. K., Huebert, D. J., McMahon, S., Karlsson, E. K., Kulbokas, E. J. 3rd, Gingeras, T. R., Schreiber, S. L., and Lander, E. S. (2005). Genomic maps and comparative analysis of histone modifications in human and mouse. Cell 120, 169-181.
2. Besag, J. and Kooperberg, C. (1995). On conditional and intrinsic autoregressions. Biometrika 82, 733-746.
3. Blatt, M., Wiseman, S., and Domany, E. (1996). Clustering data through an analogy to the Potts model. Advances in Neural Information Processing Systems 8, 416-422.
4. Boyer, L. A., Lee, T. I., Cole, M. F., Johnstone, S. E., Levine, S. S., Zucker, J. P., Guenther, M. G., Kumar, R. M., Murray, H. L., Jenner, R. G., Gifford, D. K., Melton, D. A., Jaenisch, R., and Young, R. A. (2005). Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947-956.
5. Buck, M. J. and Lieb, J. D. (2004). ChIP-chip: Considerations for the design, analysis and application of genome-wide chromatin immunoprecipitation experiments. Genomics 83, 349-360.
6. Cawley, S., Bekiranov, S., Ng, H. H., Kapranov, P., Sekinger, E. A., Kampa, D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A. J., Wheeler, R., Wong, B., Drenkow, J., Yamanaka, M., Patel, S., Brubaker, S., Tammana, H., Helt, G., Struhl, K., and Gingeras, T. R. (2004). Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116, 499-509.
7. Fouse, S., Shen, Y., Pellegrini, M., Cole, S., Meissner, A., Van Neste, L., Jaenisch, R., and Fan, G. (2008). Promoter CpG methylation contributes to ES cell gene regulation in parallel with Oct4/Nanog, PcG complex, and histone H3 K4/K27 trimethylation. Cell Stem Cell 2, 160-169.
8. Gottardo, R., Li, W., Johnson, W. E., and Liu, X. S. (2008). A flexible and powerful Bayesian hierarchical model for ChIP-chip experiments. Biometrics 64(2), 468-478.
9. Huber, M. (2002). A bounding chain for Swendsen-Wang. Random Structures and Algorithms 22(1), 53-59.
10. Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., and Speed, T. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264.
11. Ji, H. and Wong, W. (2005). Tilemap: Create chromosomal map of tiling array hybridizations. Bioinformatics 18, 3629-3636.
12. Keles, S. (2007). Mixture modeling for genome-wide localization of transcription factors. Biometrics 63, 10-21.
13. Keles, S., Van der Laan, M. J., and Cawley, S. E. (2006). Multiple testing methods for ChIP-chip high density oligonucleotide array data. Journal of Computational Biology 13(3), 579-613.
14. Möller, J., Pettitt, A., Berthelsen, K., and Reeves, R. (2006). An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika 93, 451-458.
15. Newton, M., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155-176.
16. Park, P. (2009). ChIP-seq: Advantages and challenges of a maturing technology. Nature Reviews Genetics 10(10), 669-680.
17. Smyth, G. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, Article 3.