Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, Lin-Anen_US
dc.contributor.authorChen, Dung-Tsaen_US
dc.contributor.authorChan, Wenyawen_US
dc.date.accessioned2014-12-08T15:07:17Z-
dc.date.available2014-12-08T15:07:17Z-
dc.date.issued2010-03-01en_US
dc.identifier.issn0006-3444en_US
dc.identifier.urihttp://dx.doi.org/10.1093/biomet/asp075en_US
dc.identifier.urihttp://hdl.handle.net/11536/5740-
dc.description.abstractOutlier sums were proposed by Tibshirani & Hastie (2007) and Wu (2007) for detecting outlier genes where only a small subset of disease samples shows unusually high gene expression, but they did not develop their distributional properties and formal statistical inference. In this study, a new outlier sum for detection of outlier genes is proposed, its asymptotic distribution theory is developed, and the p-value based on this outlier sum is formulated. Its analytic form is derived on the basis of the large-sample theory. We compare the proposed method with existing outlier sum methods by power comparisons. Our method is applied to DNA microarray data from samples of primary breast tumors examined by Huang et al. (2003). The results show that the proposed method is more efficient in detecting outlier genes.en_US
dc.language.isoen_USen_US
dc.titleThe distribution-based p-value for the outlier sum in differential gene expression analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/biomet/asp075en_US
dc.identifier.journalBIOMETRIKAen_US
dc.citation.volume97en_US
dc.citation.issue1en_US
dc.citation.spage246en_US
dc.citation.epage253en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
Appears in Collections:Articles