RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. Researchers at Cold Spring Harbor Laboratory assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks.
The researchers examined RNA-seq co-expression data generated from 1,970 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 >10M per sample. While the aggregate network constructed shows good performance (AUROC~0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples to obtain ‘gold-standard‘ co-expression. The researchers found 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.
Availability – Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/