Bayesian hierarchical graph-structured model for pathway analysis using gene expression data

Zhou Hui and Zheng Tian ()
Additional contact information
Zhou Hui: Department of Biostatistics, Columbia University, New York, NY 10032, USA
Zheng Tian: Department of Statistics, Columbia University, New York, NY 10027, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 3, 393-412

Abstract: In genomic analysis, there is growing interest in network structures that represent biochemistry interactions. Graph structured or constrained inference takes advantage of a known relational structure among variables to introduce smoothness and reduce complexity in modeling, especially for high-dimensional genomic data. There has been a lot of interest in its application in model regularization and selection. However, prior knowledge on the graphical structure among the variables can be limited and partial. Empirical data may suggest variations and modifications to such a graph, which could lead to new and interesting biological findings. In this paper, we propose a Bayesian random graph-constrained model, rGrace, an extension from the Grace model, to combine a priori network information with empirical evidence, for applications such as pathway analysis. Using both simulations and real data examples, we show that the new method, while leading to improved predictive performance, can identify discrepancy between data and a prior known graph structure and suggest modifications and updates.

Keywords: gene expression; network analysis; Bayesian anslysis (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0011 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:12:y:2013:i:3:p:393-412:n:1007

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2013-0011

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().