Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data.
Background: Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic inter...
| Publicado en: | BioMed Research International Vol. 2015; pp. 573956 - 573957 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
1/1/2015
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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=109621915&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109621915 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/1/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109621915 109621915 NLM26273633 2013130193 10.1155/2015/573956 NLM26273633 PMC4529927 109621915 ppf: 573956 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data. aug: au: Wang, Yishu Yang, Dejie Deng, Minghua sug: ab: Background: Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology "low-rank and sparse decomposition" (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part.Results: LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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