Consistency of biological networks inferred from microarray and sequencing data

BMC Bioinformatics

Methodology Article

Veronica Vinciotti¹  Ernst C. Wit²  Peter A. C. ’t Hoen³  Brenda W. J. H. Penninx⁴  Rick Jansen⁴  Eco J. C. N. de Geus⁴  Dorret I. Boomsma⁴ 

[1] Department of Mathematics, Brunel University London, London, UK;Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands;Leiden University Medical Center, Leiden University, Leiden, The Netherlands;VU University Medical Center, Amsterdam, The Netherlands;

关键词: Gaussian graphical models;    Gene regulatory network;    Microarray;    Next-generation sequencing;   

DOI  :  10.1186/s12859-016-1136-0

 received in 2016-02-05, accepted in 2016-06-10,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundSparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples.ResultsOur analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups of interest, such as pathways, rather than at the level of individual edges.ConclusionsCareful pre-processing of transcriptional data and summaries of networks beyond individual edges can improve the consistency of network inference across platforms. However, caution is needed at this stage in the (over)interpretation of gene regulatory networks inferred from biological data.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

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