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Biological pathway selection through Bayesian integrative modeling

Author

Listed:
  • Zheng Lingling

    (Duke Univeristy – Computational Biology and Bioinformatics, Durham, North Carolina 27708, USA)

  • Yan Xiao

    (Department of Pharmacology, Duke University, Durham, North Carolina, USA)

  • Suchindran Sunil

    (Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA)

  • Dressman Holly

    (Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA)

  • Chute John P.

    (Division of Hematology/Oncology, Broad Stem Cell Research Center, University of California, Los Angeles, California, USA)

  • Lucas Joseph

    (Quintiles, Durham, North Carolina, USA)

Abstract

Pathway analysis has become a central approach to understanding the underlying biology of differentially expressed genes. As large amounts of microarray data have been accumulated in public repositories, flexible methodologies are needed to extend the analysis of simple case-control studies in order to place them in context with the vast quantities of available and highly heterogeneous data sets. To address this challenge, we have developed a two-level model, consisting of 1) a joint Bayesian factor model that integrates multiple microarray experiments and ties each factor to a predefined pathway and 2) a point mass mixture distribution that infers which factors are relevant/irrelevant to each dataset. Our method can identify pathways specific to a particular experimental trait which are concurrently induced/repressed under a variety of interventions. In this paper, we describe the model in depth and provide examples of its utility in simulations as well as real data from a study of radiation exposure. Our analysis of the radiation study leads to novel insights into the molecular basis of time- and dose- dependent response to ionizing radiation in mice peripheral blood. This broadly applicable model provides a starting point for generating specific and testable hypotheses in a pathway-centric manner.

Suggested Citation

  • Zheng Lingling & Yan Xiao & Suchindran Sunil & Dressman Holly & Chute John P. & Lucas Joseph, 2014. "Biological pathway selection through Bayesian integrative modeling," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 435-457, August.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:4:p:23:n:4
    DOI: 10.1515/sagmb-2013-0043
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    References listed on IDEAS

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    1. Lucas Joseph & Carvalho Carlos & West Mike, 2009. "A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-28, February.
    2. A. Bhattacharya & D. B. Dunson, 2011. "Sparse Bayesian infinite factor models," Biometrika, Biometrika Trust, vol. 98(2), pages 291-306.
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