DOMINO: a network‐based active module identification algorithm with reduced rate of false calls
Open Access
- 20 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Molecular Systems Biology
- Vol. 17 (1), e9593
- https://doi.org/10.15252/msb.20209593
Abstract
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.Keywords
Funding Information
- Israel Science Foundation (1339/18, 2118/19)
This publication has 48 references indexed in Scilit:
- Mammalian MAPK Signal Transduction Pathways Activated by Stress and Inflammation: A 10-Year UpdatePhysiological Reviews, 2012
- Underestimated Effect Sizes in GWAS: Fundamental Limitations of Single SNP Analysis for Dichotomous PhenotypesPLOS ONE, 2011
- REVIGO Summarizes and Visualizes Long Lists of Gene Ontology TermsPLOS ONE, 2011
- Network medicine: a network-based approach to human diseaseNature Reviews Genetics, 2010
- BioNet: an R-Package for the functional analysis of biological networksBioinformatics, 2010
- Automated Network Analysis Identifies Core Pathways in GlioblastomaPLOS ONE, 2010
- edgeR: a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics, 2009
- Fast unfolding of communities in large networksJournal of Statistical Mechanics: Theory and Experiment, 2008
- Protein networks in diseaseGenome Research, 2008
- Network biology: understanding the cell's functional organizationNature Reviews Genetics, 2004