Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12947
Title: Consistency of biological networks inferred from microarray and sequencing data
Authors: Vinciotti, V
Wit, EC
Jansen, R
de Geus, EJCN
Penninx, BWJH
Boomsma, DI
't Hoen, PAC
Keywords: Gaussian graphical models;Gene regulatory network;Microarray;Next-generation sequencing
Issue Date: 2016
Publisher: BioMed Central
Citation: BMC Bioinformatics, 17(1): 254, (2016)
Abstract: Background: Sparse 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. Results: Our 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. Conclusions: Careful 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.
URI: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1136-0
http://bura.brunel.ac.uk/handle/2438/12947
DOI: http://dx.doi.org/10.1186/s12859-016-1136-0
ISSN: 1471-2105
Appears in Collections:Dept of Mathematics Research Papers

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