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A Flexible and Powerful Bayesian Hierarchical Model for ChIP–Chip Experiments

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  • Raphael Gottardo
  • Wei Li
  • W. Evan Johnson
  • X. Shirley Liu

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  • Raphael Gottardo & Wei Li & W. Evan Johnson & X. Shirley Liu, 2008. "A Flexible and Powerful Bayesian Hierarchical Model for ChIP–Chip Experiments," Biometrics, The International Biometric Society, vol. 64(2), pages 468-478, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:468-478
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00899.x
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    References listed on IDEAS

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    1. Sündüz Keleş, 2007. "Mixture Modeling for Genome-Wide Localization of Transcription Factors," Biometrics, The International Biometric Society, vol. 63(1), pages 10-21, March.
    2. Giovanni Parmigiani & Elizabeth S. Garrett & Ramaswamy Anbazhagan & Edward Gabrielson, 2002. "A statistical framework for expression‐based molecular classification in cancer," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 717-736, October.
    3. Sunduz Keles & Mark van der Laan & Sandrine Dudoit & Simon Cawley, 2004. "Multiple Testing Methods For ChIP-Chip High Density Oligonucleotide Array Data," U.C. Berkeley Division of Biostatistics Working Paper Series 1147, Berkeley Electronic Press.
    4. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
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    Cited by:

    1. Xuekui Zhang & Gordon Robertson & Martin Krzywinski & Kaida Ning & Arnaud Droit & Steven Jones & Raphael Gottardo, 2011. "PICS: Probabilistic Inference for ChIP-seq," Biometrics, The International Biometric Society, vol. 67(1), pages 151-163, March.
    2. Qianxing Mo & Faming Liang, 2010. "Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model," Biometrics, The International Biometric Society, vol. 66(4), pages 1284-1294, December.
    3. Wang, Dong, 2010. "Modeling epigenetic modifications under multiple treatment conditions," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1179-1189, April.
    4. Jonathan A. L. Gelfond & Mayetri Gupta & Joseph G. Ibrahim, 2009. "A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP-Chip Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1087-1095, December.

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