Show simple item record

dc.contributor.author Armagan, Prof Artin en_US
dc.date.accessioned 2011-06-21T17:29:32Z
dc.date.available 2011-06-21T17:29:32Z
dc.date.issued 2010 en_US
dc.identifier.citation Zaretzki,Russell L.;Gilchrist,Michael A.;Briggs,William M.;Armagan,Artin. 2010. Bias correction and Bayesian analysis of aggregate counts in SAGE libraries. Bmc Bioinformatics 11( ): 72-72. en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://hdl.handle.net/10161/4340
dc.description.abstract Background: Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results: Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context. Conclusions: Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression. en_US
dc.language.iso en_US en_US
dc.publisher BIOMED CENTRAL LTD en_US
dc.relation.isversionof doi:10.1186/1471-2105-11-72 en_US
dc.subject gene-expression en_US
dc.subject differential expression en_US
dc.subject serial analysis en_US
dc.subject model en_US
dc.subject supersage en_US
dc.subject biochemical research methods en_US
dc.subject biotechnology & applied microbiology en_US
dc.subject mathematical & computational biology en_US
dc.title Bias correction and Bayesian analysis of aggregate counts in SAGE libraries en_US
dc.title.alternative en_US
dc.description.version Version of Record en_US
duke.date.pubdate 2010-2-3 en_US
duke.description.endpage 72 en_US
duke.description.issue en_US
duke.description.startpage 72 en_US
duke.description.volume 11 en_US
dc.relation.journal Bmc Bioinformatics en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record