Handle URI:
http://hdl.handle.net/10754/598620
Title:
Inferring gene networks from discrete expression data
Authors:
Zhang, L.; Mallick, B. K.
Abstract:
The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.
Citation:
Zhang L, Mallick BK (2013) Inferring gene networks from discrete expression data. Biostatistics 14: 708–722. Available: http://dx.doi.org/10.1093/biostatistics/kxt021.
Publisher:
Oxford University Press (OUP)
Journal:
Biostatistics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
18-Jul-2013
DOI:
10.1093/biostatistics/kxt021
PubMed ID:
23873894
Type:
Article
ISSN:
1465-4644; 1468-4357
Sponsors:
The research was supported by grants from the National Science Foundation DMS grant 0914951 and the KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, L.en
dc.contributor.authorMallick, B. K.en
dc.date.accessioned2016-02-25T13:33:16Zen
dc.date.available2016-02-25T13:33:16Zen
dc.date.issued2013-07-18en
dc.identifier.citationZhang L, Mallick BK (2013) Inferring gene networks from discrete expression data. Biostatistics 14: 708–722. Available: http://dx.doi.org/10.1093/biostatistics/kxt021.en
dc.identifier.issn1465-4644en
dc.identifier.issn1468-4357en
dc.identifier.pmid23873894en
dc.identifier.doi10.1093/biostatistics/kxt021en
dc.identifier.urihttp://hdl.handle.net/10754/598620en
dc.description.abstractThe modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.en
dc.description.sponsorshipThe research was supported by grants from the National Science Foundation DMS grant 0914951 and the KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherOxford University Press (OUP)en
dc.subjectDiscrete graphical modelen
dc.subjectGene expression networken
dc.subjectGene ontologyen
dc.subjectRNA-Seqen
dc.subjectSAGEen
dc.titleInferring gene networks from discrete expression dataen
dc.typeArticleen
dc.identifier.journalBiostatisticsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-CI-016-04en

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