title: Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.
abstract: BACKGROUND
  RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks.
  
  
  RESULTS
  We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are assessed for the tendency of co-expression to reflect shared function. Minimal experimental criteria to obtain performance on par with microarrays were >20 samples with read depth >10 M per sample. While the aggregate network constructed shows good performance (area under the receiver operator characteristic curve ∼0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples are required to obtain 'gold-standard' co-expression. We find a major topological difference between RNA-seq and microarray co-expression in the form of low overlaps between hub-like genes from each network due to changes in the correlation of expression noise within each technology.
  
  
  BACKGROUND
  jgillis@cshl.edu or sballouz@cshl.edu
  
  
  BACKGROUND
  Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online.
authors.affiliation: Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.
authors.forename: S
authors.initials: S
authors.lastname: Ballouz
authors.affiliation: Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.
authors.forename: W
authors.initials: W
authors.lastname: Verleyen
authors.affiliation: Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.
authors.forename: J
authors.initials: J
authors.lastname: Gillis
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D017423:Sequence Analysis, RNA / Q000379:methods*