State Space Model With Hidden Variables For Reconstruction of Gene Regulatory Networks

Authors

Xi Wu, University of Southern Mississippi
Peng Li, National Institutes of HealthFollow
Nan Wang, University of Southern MississippiFollow
Ping Gong, SpecPro, Inc.Follow
Edward J. Perkins, U.S. Army Engineer Research and Development CenterFollow
Youping Deng, Rush University Medical CenterFollow
Chaoyang Zhang, University of Southern MississippiFollow

Document Type

Article

Publication Date

12-1-2011

Department

Computing

School

Computing Sciences and Computer Engineering

Abstract

Background: State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.

Method: True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks.

Results: Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN.

Conclusion: This study provides useful information in handling the hidden variables and improving the inference precision.

Comments

Published by 'BMC Systems Biology' at 10.1186/1752-0509-5-S3-S3.

Publication Title

BMC Systems Biology

Volume

5

Recommended Citation

Wu, X., Li, P., Wang, N., Gong, P., Perkins, E. J., Deng, Y., Zhang, C. (2011). State Space Model With Hidden Variables For Reconstruction of Gene Regulatory Networks. BMC Systems Biology, 5.
Available at: https://aquila.usm.edu/fac_pubs/611