Abstract
Large-scale high-dimensional omics data sets have been generate to survey complex biological systems. However, it is a challenge how to integrate multiple dimensions of biological data to biological causal networks where comprehensive knowledge can be derived in contexts. We developed a RIMBANet (Reconstructing Integrative Molecular Bayesian Networks) method to integrate diverse biological data. In this chapter, we disseminate results of applying our RIMBANet method on a series of simulated datasets. Two sets of networks are inferred with or without integrating genetic markers with gene expression data. We show that integration of genetic data into network reconstruction using RIMBANet approach improves network construction accuracy. Furthermore, false-positive links in reconstructed networks are not randomly distributed. More than 80 % of them connect nodes that are indirect neighbors.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322:881–888
Chen Y et al (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452:429–435
Emilsson V et al (2008) Genetics of gene expression and its effect on disease. Nature 452:423–428
Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620
Hsu YH et al (2010) An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits. PLoS Genet 6:e1000977. doi:10.1371/journal.pgen.1000977
Leonardson AS et al (2010) The effect of food intake on gene expression in human peripheral blood. Hum Mol Genet 19:159–169. doi:10.1093/hmg/ddp476 (ddp476 [pii])
Madigan DaYJ (1995) Bayesian graphical models for discrete data. Int Stat Rev 63:215–232
Marbach D et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9:796–804. doi:10.1038/nmeth.2016
Pearl J (1988) Probabilistic reasoning in intelligent systems : networks of plausible inference. Morgan Kaufmann Publishers, San Mateo
Schadt EE et al (2008) Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6:e107
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464
Vignes M et al (2011) Gene regulatory network reconstruction using Bayesian networks, the Dantzig selector, the Lasso and their meta-analysis. PLoS One 6:e29165. doi:10.1371/journal.pone.0029165
Witte JS (2010) Genome-wide association studies and beyond. Annu Rev Public Health 31:9–20. doi:10.1146/annurev.publhealth.012809.103723 (4 p following 20)
Zhang W, Zhu J, Schadt EE, Liu, JS (2010) A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules. PLoS Comput Biol 6:e1000642. doi:10.1371/journal.pcbi.1000642
Zhong H et al (2010) Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes. PLoS Genet 6:e1000932. doi:10.1371/journal.pgen.1000932
Zhu J et al (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40:854–861. doi:P10.1038/ng.167 (ng.167 [pii])
Zhu J et al (2010) Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput Biol 6:e1000671. doi:10.1371/journal.pcbi.1000671
Zhu J et al (2012) Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol 10:e1001301. doi:10.1371/journal.pbio.1001301 (PBIOLOGY-D-11-03979 [pii])
Zhu J et al (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105:363–374
Zhu J et al (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3:e69
Zhu J et al (2010) Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput Biol 6:e1000671. doi:10.1371/journal.pcbi.1000671
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lin, L., Zhu, J. (2013). Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data. In: de la Fuente, A. (eds) Gene Network Inference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45161-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-45161-4_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45160-7
Online ISBN: 978-3-642-45161-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)