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, 16 (3), 407-26

Analysis of Gene Sets Based on the Underlying Regulatory Network

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Analysis of Gene Sets Based on the Underlying Regulatory Network

Ali Shojaie et al. J Comput Biol.

Abstract

Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast.

Figures

FIG. 1.
FIG. 1.
Yeast galactose utilization pathway published in Ideker et al. (2001). (Printed with permission from Science and the American Association for the Advancement of Science.)
FIG. 2.
FIG. 2.
A simple gene network.
FIG. 3.
FIG. 3.
Illustration of the Network contrast vector on a simple network; dashed line indicates the interactions that are included in the contrast vector.
FIG. 4.
FIG. 4.
Design of the second simulation study. Solid arrows and boxes represent high positive association (0.6 here), dashed arrows and boxes represent high negative association (−0.6), and dotted arrows and boxes indicate low positive association (0.1). The root genes 1 and 2 are upregulated, while the expression level for gene 3 does not change.
FIG. 5.
FIG. 5.
Estimated powers of test of significance of subnetworks of Table 6 with random noise in weights of the adjacency matrix. Plots in gray represent the powers of subnetworks whose true adjacency matrices in control and treatment are the same.
FIG. 6.
FIG. 6.
Estimated powers of test of significance of subnetworks of Table 6 with systematic bias in weights of the adjacency matrix. Plots in gray represent the powers of subnetworks whose true adjacency matrices in control and treatment are the same.
FIG. 7.
FIG. 7.
Yeast gene network indicating the significant pathways; significant pathways have been marked with ovals.

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