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Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions

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  • Suvo Chatterjee
  • Shrabanti Chowdhury
  • Duchwan Ryu
  • Sanjib Basu

Abstract

We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.

Suggested Citation

  • Suvo Chatterjee & Shrabanti Chowdhury & Duchwan Ryu & Sanjib Basu, 2023. "Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions," Biometrics, The International Biometric Society, vol. 79(4), pages 3294-3306, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3294-3306
    DOI: 10.1111/biom.13902
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    References listed on IDEAS

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    1. Duchwan Ryu & Faming Liang & Bani K. Mallick, 2013. "Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 111-123, March.
    2. Duchwan Ryu & Erning Li & Bani K. Mallick, 2011. "Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 67(2), pages 454-466, June.
    3. Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 577-613, June.
    4. Murray Aitkin, 1998. "Simpson’s paradox and the Bayes factor," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 269-270.
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