Network hub gene detection using the entire solution path information.
Gene co-expression networks typically comprise modules and their associated hub genes, which are regulating numerous downstream interactions within the network. Methods for hub screening, as well as data-driven estimation of hub co-expression networks using graphical models, can serve as useful tool...
| Publicado en: | Genetics Vol. 229; no. 1; pp. 1 - 34 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182166175&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182166175 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: Jan2025 vid: 229 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 182166175 182166175 182166175 10.1093/genetics/iyae187 182166175 ppf: 1 ppct: 33 formats: tig: atl: Network hub gene detection using the entire solution path information. aug: au: Kuismin, Markku Sillanpää, Mikko J affil: Research Unit of Mathematical Sciences, University of Oulu, P.O. BOX 8000, Oulu FI-90014, Finland sug: subj: Genes Physiology Signal Transduction Gene Expression Profiling Methods Molecular Structure Models, Biological Genetic Engineering Human Genetics, Medical Genetic Screening Models, Statistical Simulations Empirical Research False Positive Results False Negative Results Gene Expression Funding Source ab: Gene co-expression networks typically comprise modules and their associated hub genes, which are regulating numerous downstream interactions within the network. Methods for hub screening, as well as data-driven estimation of hub co-expression networks using graphical models, can serve as useful tools for identifying these hubs. Graphical model-based penalization methods typically have one or multiple regularization terms, each of which encourages some favorable characteristics (e.g. sparsity, hubs, and power-law) to the estimated complex gene network. It is common practice to find a single optimal graphical model corresponding to a specific value of the regularization parameter(s). However, instead of doing this, one could aggregate information across several graphical models, all of which depend on the same data set, along the solution path in the hub gene detection process. We propose a novel method for detecting hub genes that utilizes the information available in the solution path. Our procedure is related to stability selection, but we replace resampling with a simple statistic. This procedure amalgamates information from each node of the data-driven graphical models into a single influence statistic, similar to Cook's distance. We call this statistic the Mean Degree Squared Distance (MDSD). Our simulation and empirical studies demonstrate that the MDSD statistic maintains a good balance between false positive and true positive hubs. An R package MDSD is publicly available on GitHub under the General Public License https://github.com/markkukuismin/MDSD. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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