Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1000607
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dc.titleDissecting early differentially expressed genes in a mixture of differentiating embryonic stem cells
dc.contributor.authorHong F.
dc.contributor.authorFang F.
dc.contributor.authorHe X.
dc.contributor.authorCao X.
dc.contributor.authorChipperfield H.
dc.contributor.authorXie D.
dc.contributor.authorWong W.H.
dc.contributor.authorNg H.H.
dc.contributor.authorZhong S.
dc.date.accessioned2019-11-06T09:34:54Z
dc.date.available2019-11-06T09:34:54Z
dc.date.issued2009
dc.identifier.citationHong F., Fang F., He X., Cao X., Chipperfield H., Xie D., Wong W.H., Ng H.H., Zhong S. (2009). Dissecting early differentially expressed genes in a mixture of differentiating embryonic stem cells. PLoS Computational Biology 5 (12) : e1000607. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1000607
dc.identifier.issn1553734X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161669
dc.description.abstractThe differentiation of embryonic stem cells is initiated by a gradual loss of pluripotency-associated transcripts and induction of differentiation genes. Accordingly, the detection of differentially expressed genes at the early stages of differentiation could assist the identification of the causal genes that either promote or inhibit differentiation. The previous methods of identifying differentially expressed genes by comparing different cell types would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting differentially expressed genes at the early stages of differentiation. Applying the proposed method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified Smarcad1 as a novel regulator of pluripotency and self-renewal. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes. © 2009 Hong et al.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectanimal cell
dc.subjectarticle
dc.subjectcell differentiation
dc.subjectcell separation
dc.subjectcell type
dc.subjectcontrolled study
dc.subjectembryo
dc.subjectembryonic stem cell
dc.subjectgene expression
dc.subjectmouse
dc.subjectnonhuman
dc.subjectpluripotent stem cell
dc.subjectanimal
dc.subjectbiological model
dc.subjectbiology
dc.subjectcytology
dc.subjectgene expression profiling
dc.subjectgene expression regulation
dc.subjectgene regulatory network
dc.subjectgenetics
dc.subjectmethodology
dc.subjectphysiology
dc.subjectPoisson distribution
dc.subjectMurinae
dc.subjectAnimals
dc.subjectCell Differentiation
dc.subjectComputational Biology
dc.subjectEmbryonic Stem Cells
dc.subjectGene Expression Profiling
dc.subjectGene Expression Regulation, Developmental
dc.subjectGene Regulatory Networks
dc.subjectMice
dc.subjectModels, Genetic
dc.subjectPoisson Distribution
dc.typeArticle
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.description.doi10.1371/journal.pcbi.1000607
dc.description.sourcetitlePLoS Computational Biology
dc.description.volume5
dc.description.issue12
dc.description.pagee1000607
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