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Hierarchical probabilistic interaction modeling for multiple gene expression replicates

Authors:

Kristopher L. Patton,

James L. Norris,

Daniel R. Lewis,

Gloria K. MudayAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 11, Issue 2

Pages 336 - 346

https://doi.org/10.1109/TCBB.2014.2299804

Published: 01 March 2014 Publication History

Abstract

Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature.

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Cited By

Wang PWang DLu J(2019)Controllability Analysis of a Gene Network for Arabidopsis thaliana Reveals Characteristics of Functional Gene FamiliesIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2018.282114516:3(912-924)Online publication date: 15-Jul-2019
https://dl.acm.org/doi/10.1109/TCBB.2018.2821145

Index Terms

Hierarchical probabilistic interaction modeling for multiple gene expression replicates

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Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 11, Issue 2

March/April 2014

160 pages

ISSN:1545-5963

Editor:
Ying Xu

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 March 2014

Accepted: 27 December 2013

Revised: 15 November 2013

Received: 20 March 2013

Published in TCBB Volume 11, Issue 2

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Cited By

Wang PWang DLu J(2019)Controllability Analysis of a Gene Network for Arabidopsis thaliana Reveals Characteristics of Functional Gene FamiliesIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2018.282114516:3(912-924)Online publication date: 15-Jul-2019
https://dl.acm.org/doi/10.1109/TCBB.2018.2821145

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