Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations
Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of...
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| Sprache: | Englisch |
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2013
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| Online-Zugang: | https://hdl.handle.net/20.500.12008/41817 https://doi.org/10.1371/journal.pcbi.1003068 |
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| _version_ | 1868890248142913536 |
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| author | Cai, Xiaodong |
| author2 | Bazerque, Juan Andrés Giannakis, Georgios B |
| author2_role | author author |
| author_browse | Bazerque, Juan Andrés Cai, Xiaodong Giannakis, Georgios B |
| author_facet | Cai, Xiaodong Bazerque, Juan Andrés Giannakis, Georgios B |
| author_role | author |
| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Cai, Xiaodong Bazerque, Juan Andrés Giannakis, Georgios B |
| dc.date.none.fl_str_mv | 2013 2023-12-11T19:57:54Z 2023-12-11T19:57:54Z 20231211 |
| dc.identifier.none.fl_str_mv | Cai X, Bazerque JA, Giannakis GB "Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations”. PLoS Computational Biology, 2013, v.9, no.5, e1003068. https://doi.org/10.1371/journal.pcbi.1003068 1553-734X https://hdl.handle.net/20.500.12008/41817 https://doi.org/10.1371/journal.pcbi.1003068 1553-7358 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Public Library of Science |
| dc.relation.none.fl_str_mv | PLoS Computational Biology, 2013, v.9, no.5 |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Sistemas y Control |
| dc.title.none.fl_str_mv | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_fc8a553ffc1a6c3f64bb1f266ff3b4ff |
| identifier_str_mv | Cai X, Bazerque JA, Giannakis GB "Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations”. PLoS Computational Biology, 2013, v.9, no.5, e1003068. https://doi.org/10.1371/journal.pcbi.1003068 1553-734X 1553-7358 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41817 |
| publishDate | 2013 |
| publishDateSort | 2013 |
| publisher.none.fl_str_mv | Public Library of Science |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbationsCai, XiaodongBazerque, Juan AndrésGiannakis, Georgios BSistemas y ControlIntegrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon requestPublic Library of Science2023-12-11T19:57:54Z2023-12-11T19:57:54Z201320231211Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionCai X, Bazerque JA, Giannakis GB "Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations”. PLoS Computational Biology, 2013, v.9, no.5, e1003068. https://doi.org/10.1371/journal.pcbi.10030681553-734Xhttps://hdl.handle.net/20.500.12008/41817https://doi.org/10.1371/journal.pcbi.10030681553-7358reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengPLoS Computational Biology, 2013, v.9, no.5Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)oai:colibri.udelar.edu.uy:20.500.12008/418172026-04-14T10:15:47Z |
| spellingShingle | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations Cai, Xiaodong Sistemas y Control |
| status_str | publishedVersion |
| title | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| title_full | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| title_fullStr | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| title_full_unstemmed | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| title_short | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| title_sort | Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations |
| topic | Sistemas y Control |
| url | https://hdl.handle.net/20.500.12008/41817 https://doi.org/10.1371/journal.pcbi.1003068 |