title: Consistency of biological networks inferred from microarray and sequencing data.
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.
authors.affiliation: Department of Mathematics, Brunel University London, London, UK. veronica.vinciotti@brunel.ac.uk.
authors.forename: Veronica
authors.initials: V
authors.lastname: Vinciotti
authors.affiliation: Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands.
authors.forename: Ernst C
authors.initials: EC
authors.lastname: Wit
authors.affiliation: VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Rick
authors.initials: R
authors.lastname: Jansen
authors.affiliation: VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Eco J C N
authors.initials: EJ
authors.lastname: de Geus
authors.affiliation: VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Brenda W J H
authors.initials: BW
authors.lastname: Penninx
authors.affiliation: VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Dorret I
authors.initials: DI
authors.lastname: Boomsma
authors.affiliation: Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
authors.forename: Peter A C
authors.initials: PA
authors.lastname: 't Hoen
mesh_terms: D001007:Anxiety / Q000235:genetics; D003863:Depression / Q000235:genetics; D005260:Female; D053263:Gene Regulatory Networks; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA; D018433:Twin Studies as Topic
keywords: Gaussian graphical models; Gene regulatory network; Microarray; Next-generation sequencing