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| pubmed-article:15469612 | lifeskim:mentions | umls-concept:C1122976 | lld:lifeskim |
| pubmed-article:15469612 | lifeskim:mentions | umls-concept:C0441833 | lld:lifeskim |
| pubmed-article:15469612 | lifeskim:mentions | umls-concept:C0876956 | lld:lifeskim |
| pubmed-article:15469612 | lifeskim:mentions | umls-concept:C1539721 | lld:lifeskim |
| pubmed-article:15469612 | lifeskim:mentions | umls-concept:C0439064 | lld:lifeskim |
| pubmed-article:15469612 | lifeskim:mentions | umls-concept:C0206031 | lld:lifeskim |
| pubmed-article:15469612 | pubmed:dateCreated | 2004-11-1 | lld:pubmed |
| pubmed-article:15469612 | pubmed:abstractText | Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed. | lld:pubmed |
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| pubmed-article:15469612 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:15469612 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:15469612 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:15469612 | pubmed:month | Oct | lld:pubmed |
| pubmed-article:15469612 | pubmed:issn | 1471-2105 | lld:pubmed |
| pubmed-article:15469612 | pubmed:author | pubmed-author:FassR JRJ | lld:pubmed |
| pubmed-article:15469612 | pubmed:author | pubmed-author:AldazC... | lld:pubmed |
| pubmed-article:15469612 | pubmed:author | pubmed-author:BaggerlyKeith... | lld:pubmed |
| pubmed-article:15469612 | pubmed:author | pubmed-author:MorrisJeffrey... | lld:pubmed |
| pubmed-article:15469612 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:15469612 | pubmed:day | 6 | lld:pubmed |
| pubmed-article:15469612 | pubmed:volume | 5 | lld:pubmed |
| pubmed-article:15469612 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:15469612 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:15469612 | pubmed:pagination | 144 | lld:pubmed |
| pubmed-article:15469612 | pubmed:dateRevised | 2011-4-12 | lld:pubmed |
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| pubmed-article:15469612 | pubmed:year | 2004 | lld:pubmed |
| pubmed-article:15469612 | pubmed:articleTitle | Overdispersed logistic regression for SAGE: modelling multiple groups and covariates. | lld:pubmed |
| pubmed-article:15469612 | pubmed:affiliation | Department of Biostatistics and Applied Mathematics, UT M. D. Anderson Cancer Center, Houston, TX, USA. kabagg@mdanderson.org | lld:pubmed |
| pubmed-article:15469612 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:15469612 | pubmed:publicationType | Comparative Study | lld:pubmed |
| pubmed-article:15469612 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
| pubmed-article:15469612 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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