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Article: Detecting differentially expressed genes by relative entropy

TitleDetecting differentially expressed genes by relative entropy
Authors
KeywordsDifferentially expressed genes
Gene selection
Microarray
Relative entropy
Issue Date2005
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjtbi
Citation
Journal Of Theoretical Biology, 2005, v. 234 n. 3, p. 395-402 How to Cite?
AbstractDNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types. © 2005 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/82905
ISSN
2015 Impact Factor: 2.049
2015 SCImago Journal Rankings: 1.089
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYan, Xen_HK
dc.contributor.authorDeng, Men_HK
dc.contributor.authorFung, WKen_HK
dc.contributor.authorQian, Men_HK
dc.date.accessioned2010-09-06T08:34:44Z-
dc.date.available2010-09-06T08:34:44Z-
dc.date.issued2005en_HK
dc.identifier.citationJournal Of Theoretical Biology, 2005, v. 234 n. 3, p. 395-402en_HK
dc.identifier.issn0022-5193en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82905-
dc.description.abstractDNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types. © 2005 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjtbien_HK
dc.relation.ispartofJournal of Theoretical Biologyen_HK
dc.subjectDifferentially expressed genesen_HK
dc.subjectGene selectionen_HK
dc.subjectMicroarrayen_HK
dc.subjectRelative entropyen_HK
dc.titleDetecting differentially expressed genes by relative entropyen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0022-5193&volume=234&spage=395&epage=402&date=2005&atitle=Detecting+differentially+expressed+genes+by+relative+entropyen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jtbi.2004.11.039en_HK
dc.identifier.pmid15784273-
dc.identifier.scopuseid_2-s2.0-15244346245en_HK
dc.identifier.hkuros104829en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-15244346245&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume234en_HK
dc.identifier.issue3en_HK
dc.identifier.spage395en_HK
dc.identifier.epage402en_HK
dc.identifier.isiWOS:000228221400008-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYan, X=15047195600en_HK
dc.identifier.scopusauthoridDeng, M=7202079431en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.scopusauthoridQian, M=7201846120en_HK

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