DOMINO: a network‐based active module identification algorithm with reduced rate of false calls
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 repre...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2021-01-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20209593 |
| _version_ | 1797284903467876352 |
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| author | Hagai Levi Ran Elkon Ron Shamir |
| author_facet | Hagai Levi Ran Elkon Ron Shamir |
| author_sort | Hagai Levi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T17:54:27Z |
| format | Article |
| id | doaj.art-140b0bff945245639cc00108c6097504 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2024-03-07T17:54:27Z |
| publishDate | 2021-01-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-140b0bff945245639cc00108c60975042024-03-02T12:43:10ZengSpringer NatureMolecular Systems Biology1744-42922021-01-01171n/an/a10.15252/msb.20209593DOMINO: a network‐based active module identification algorithm with reduced rate of false callsHagai Levi0Ran Elkon1Ron Shamir2The Blavatnik School of Computer Science Tel Aviv University Tel Aviv IsraelDepartment of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv IsraelThe Blavatnik School of Computer Science Tel Aviv University Tel Aviv IsraelAbstract 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.https://doi.org/10.15252/msb.20209593biological networksenrichment analysisGO termsmodule discoveryomics |
| spellingShingle | Hagai Levi Ran Elkon Ron Shamir DOMINO: a network‐based active module identification algorithm with reduced rate of false calls Molecular Systems Biology biological networks enrichment analysis GO terms module discovery omics |
| title | DOMINO: a network‐based active module identification algorithm with reduced rate of false calls |
| title_full | DOMINO: a network‐based active module identification algorithm with reduced rate of false calls |
| title_fullStr | DOMINO: a network‐based active module identification algorithm with reduced rate of false calls |
| title_full_unstemmed | DOMINO: a network‐based active module identification algorithm with reduced rate of false calls |
| title_short | DOMINO: a network‐based active module identification algorithm with reduced rate of false calls |
| title_sort | domino a network based active module identification algorithm with reduced rate of false calls |
| topic | biological networks enrichment analysis GO terms module discovery omics |
| url | https://doi.org/10.15252/msb.20209593 |
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