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dc.contributor.authorLingjærde, Camilla
dc.contributor.authorLien, Tonje G
dc.contributor.authorBorgan, Ørnulf
dc.contributor.authorBergholtz, Helga
dc.contributor.authorGlad, Ingrid K
dc.date.accessioned2021-11-22T14:41:49Z
dc.date.available2021-11-22T14:41:49Z
dc.date.issued2021-10-15
dc.identifier.issn1471-2105
dc.identifier.otherPMC8518261
dc.identifier.other34654363
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330849
dc.description.abstractBACKGROUND: Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. RESULTS: We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks. CONCLUSIONS: The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 1471-2105
dc.sourcenlmid: 100965194
dc.subjectGenomics
dc.subjectGene networks
dc.subjectIntegrative Analysis
dc.subjectCancer Genomics
dc.subjectProtein–protein Interaction Networks
dc.subjectGraphical Lasso
dc.subjectNetwork Models
dc.subjectMultiomics
dc.subjectHigh-dimensional Inference
dc.subjectWeighted Graphical Lasso
dc.titleTailored graphical lasso for data integration in gene network reconstruction.
dc.typeArticle
dc.date.updated2021-11-22T14:41:49Z
prism.issueIdentifier1
prism.publicationNameBMC Bioinformatics
prism.volume22
dc.identifier.doi10.17863/CAM.78292
dcterms.dateAccepted2021-09-30
rioxxterms.versionofrecord10.1186/s12859-021-04413-z
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidLingjærde, Camilla [0000-0003-2701-5686]
dc.identifier.eissn1471-2105
pubs.funder-project-idMedical Research Council (MCUU00002/10)
cam.issuedOnline2021-10-15


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International