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A Bayesian semiparametric approach for the differential analysis of sequence counts data

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Listed:
  • Michele Guindani
  • Nuno Sepúlveda
  • Carlos Daniel Paulino
  • Peter Müller

Abstract

type="main" xml:id="rssc12041-abs-0001"> Data obtained by using modern sequencing technologies are often summarized by recording the frequencies of observed sequences. Examples include the analysis of T-cell counts in immunological research and studies of gene expression based on counts of RNA fragments. In both cases the items being counted are sequences, of proteins and base pairs respectively. The resulting sequence abundance distribution is usually characterized by overdispersion. We propose a Bayesian semiparametric approach to implement inference for such data. Besides modelling the overdispersion, the approach takes also into account two related sources of bias that are usually associated with sequence counts data: some sequence types may not be recorded during the experiment and the total count may differ from one experiment to another. We illustrate our methodology with two data sets: one regarding the analysis of CD4+ T-cell counts in healthy and diabetic mice and another data set concerning the comparison of messenger RNA fragments recorded in a serial analysis of gene expression experiment with gastrointestinal tissue of healthy and cancer patients.

Suggested Citation

  • Michele Guindani & Nuno Sepúlveda & Carlos Daniel Paulino & Peter Müller, 2014. "A Bayesian semiparametric approach for the differential analysis of sequence counts data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 385-404, April.
    • Handle: RePEc:bla:jorssc:v:63:y:2014:i:3:p:385-404
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    File URL: http://hdl.handle.net/10.1111/rssc.2014.63.issue-3
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    Cited by:

    1. Cesari, Oriana & Favaro, Stefano & Nipoti, Bernardo, 2014. "Posterior analysis of rare variants in Gibbs-type species sampling models," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 79-98.
    2. Antonio Canale & Igor Prünster, 2017. "Robustifying Bayesian nonparametric mixtures for count data," Biometrics, The International Biometric Society, vol. 73(1), pages 174-184, March.
    3. Michele Guindani & Wesley O. Johnson, 2018. "More nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 239-251, June.
    4. Xiao Li & Michele Guindani & Chaan S. Ng & Brian P. Hobbs, 2021. "A Bayesian nonparametric model for textural pattern heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 459-480, March.
    5. Canale, Antonio & Lijoi, Antonio & Nipoti, Bernardo & Prünster, Igor, 2023. "Inner spike and slab Bayesian nonparametric models," Econometrics and Statistics, Elsevier, vol. 27(C), pages 120-135.

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