EGAD: ultra-fast functional analysis of gene networks

Abstract

Summary: Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many computational experiments are difficult to apply exhaustively in network analysis due to run-times. To permit high-throughput analysis of gene networks, we have implemented a set of very efficient tools to calculate functional properties in networks based on guilt-by-association methods. ( xtending ' uilt-by- ssociation' by egree) allows gene networks to be evaluated with respect to hundreds or thousands of gene sets. The methods predict novel members of gene groups, assess how well a gene network groups known sets of genes, and determines the degree to which generic predictions drive performance. By allowing fast evaluations, whether of random sets or real functional ones, provides the user with an assessment of performance which can easily be used in controlled evaluations across many parameters.

Availability and implementation: The software package is freely available at https://github.com/sarbal/EGAD and implemented for use in R and Matlab. The package is also freely available under the LGPL license from the Bioconductor web site ( http://bioconductor.org ).

Contact: JGillis@cshl.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Performance of the neighbor-voting (NV) algorithm compared to GeneMANIA. (GM). (A) Each black point represents the average performance across the GO groups (with the SD). The grey points are the average performances as the network is thresholded at 50%, 75%, etc. for GM. Although there is a speed increase for the sparsest network, all performance is lost (AUROC ∼ 0.5). The R implementation is also faster than GM, and can also be sped up substantially with other packages (MRAN). (B) The actual AUROCs between both methods are highly correlated (0.9)

Fig. 2.

The effect of only considering orthologs as candidates in predicting gene function. AUROCs are the reported average performances for all GO groups evaluated. (A) Node degree performance shift between human (H. sapiens, grey) and human genes having yeast orthologs (S. cerevisiae, black) (B) and change in multifunctionality performance. (C) The trends across species are consistent for both node degree (C) and multifunctionality (D)