DOMINO: a network‐based active module identification algorithm with reduced rate of false calls.

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over‐representation of accrued activity signal ("active modules"), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation‐based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir‐Lab. SYNOPSIS: DOMINO is an algorithm for detecting active network modules with a low rate of false GO term calls. This merit is demonstrated by using EMP, a new procedure that validates GO terms empirically. Algorithms for active module identification (AMI) in a network based on gene activity scores tend to over‐report GO terms.A procedure that empirically calls out non‐specific GO terms is proposed.Five new criteria for evaluation of AMI algorithm solutions are developed.DOMINO outperforms six leading AMI algorithms based on these criteria. [ABSTRACT FROM AUTHOR]

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