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dc.contributor.authorTom, Brianen
dc.contributor.authorGilks, Walter Ren
dc.contributor.authorBrooke-Powell, Elizabeth Ten
dc.contributor.authorAjioka, Jamesen
dc.date.accessioned2011-06-16T16:50:41Z
dc.date.available2011-06-16T16:50:41Z
dc.date.issued2005-09-26en
dc.identifier.citationBMC Bioinformatics 2005, 6:234
dc.identifier.issn1471-2105
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/238090
dc.description.abstractAbstract Background A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing (imputing) missing data and/or weighting poor quality spots are required. Results We present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot arising from each channel of a two-dye (two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution – the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol ®) treatment on a human cervical cancer derived cell line (HeLa). Conclusion As the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a method of repairing the arrays by either imputation or assigning quality weights to the spots. This repaired data set would be less biased and can be analysed using any of the appropriate statistical methods found in the microarray literature.
dc.languageEnglishen
dc.language.isoen
dc.titleQuality determination and the repair of poor quality spots in array experimentsen
dc.typeArticle
dc.date.updated2011-06-16T16:50:41Z
dc.description.versionRIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.en
dc.rights.holderTom et al.; licensee BioMed Central Ltd.
prism.publicationDate2005en
dcterms.dateAccepted2005-09-26en
rioxxterms.versionofrecord10.1186/1471-2105-6-234en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2005-09-26en
dc.contributor.orcidTom, Brian [0000-0002-3335-9322]
dc.identifier.eissn1471-2105
rioxxterms.typeJournal Article/Reviewen


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