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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.


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Network Enrichment Analysis in Complex Experiments

Ali Shojaie1 / George Michailidis2

1University of Michigan - Ann Arbor

2University of Michigan - Ann Arbor

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1483, May 2010

Publication History

Published Online:
2010-05-22

Cellular functions of living organisms are carried out through complex systems of interacting components. Including such interactions in the analysis, and considering sub-systems defined by biological pathways instead of individual components (e.g. genes), can lead to new findings about complex biological mechanisms. Networks are often used to capture such interactions and can be incorporated in models to improve the efficiency in estimation and inference. In this paper, we propose a model for incorporating external information about interactions among genes (proteins/metabolites) in differential analysis of gene sets. We exploit the framework of mixed linear models and propose a flexible inference procedure for analysis of changes in biological pathways. The proposed method facilitates the analysis of complex experiments, including multiple experimental conditions and temporal correlations among observations. We propose an efficient iterative algorithm for estimation of the model parameters and show that the proposed framework is asymptotically robust to the presence of noise in the network information. The performance of the proposed model is illustrated through the analysis of gene expression data for environmental stress response (ESR) in yeast, as well as simulated data sets.

Keywords: gene network; enrichment analysis; gene set analysis; complex experiments; spatio-temporal model; mixed linear model; systems biology

Citing Articles

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[1]
Sen Zhao and Ali Shojaie
Biometrics, 2015, Page n/a
[2]
Donatello Telesca, Peter Müller, Steven M. Kornblau, Marc A. Suchard, and Yuan Ji
Journal of the American Statistical Association, 2012, Volume 107, Number 500, Page 1372
[3]
Tomás Eduardo Ceremuga, Stephanie Martinson, Jason Washington, Robert Revels, Jessica Wojcicki, Damali Crawford, Robert Edwards, Joshua Luke Kemper, William Luke Townsend, Geno M. Herron, George Allen Ceremuga, Gina Padron, and Michael Bentley
The Scientific World Journal, 2014, Volume 2014, Page 1

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