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Bioinformatics. 2015 Jul 1;31(13):2123-30. doi: 10.1093/bioinformatics/btv118. Epub 2015 Feb 24.

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.

Author information

1
Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.

Abstract

MOTIVATION:

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.

CONTACT:

jgillis@cshl.edu or sballouz@cshl.edu

SUPPLEMENTARY INFORMATION:

Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online.

PMID:
25717192
DOI:
10.1093/bioinformatics/btv118
[Indexed for MEDLINE]

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