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dc.contributor.authorLemieux, Sébastien
dc.date.accessioned2007-01-05T21:56:40Z
dc.date.available2007-01-05T21:56:40Z
dc.date.issued2006
dc.identifier.citationLemieux, S. (2006). Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression. BMC Bioinformatics, 7(1), 391.
dc.identifier.urihttp://hdl.handle.net/1866/655
dc.identifier.urihttp://www.biomedcentral.com/1471-2105/7/391
dc.format.extent526468 bytes
dc.format.mimetypeapplication/pdf
dc.rightsCeci est un article en accès libre diffusé sous une licence Creative Commons Paternité laquelle permet une libre utilisation, diffusion et reproduction de l'article sous toutes formes, à la condition de l'attribuer à l'auteur en citant son nom. This is an open access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.titleProbe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
dc.typeArticle
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. Département d'informatique et de recherche opérationnellefr
dc.identifier.doi10.1186/1471-2105-7-391
dcterms.abstractBACKGROUND:The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication.RESULTS:On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition.CONCLUSION:The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.en
dcterms.descriptionAffiliation: Institut de recherche en immunologie et en cancérologie, Université de Montréal
dcterms.isPartOfurn:ISSN:1471-2105
UdeM.VersionRioxxVersion acceptée / Accepted Manuscript


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Ceci est un article en accès libre diffusé sous une licence Creative Commons Paternité laquelle permet une libre utilisation, diffusion et reproduction de l'article sous toutes formes, à la condition de l'attribuer à l'auteur en citant son nom. This is an open access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
RightsCeci est un article en accès libre diffusé sous une licence Creative Commons Paternité laquelle permet une libre utilisation, diffusion et reproduction de l'article sous toutes formes, à la condition de l'attribuer à l'auteur en citant son nom. This is an open access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.