Linked Life Data

18537947

Source:http://linkedlifedata.com/resource/pubmed/id/18537947

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-4-1
pubmed:abstractText
Consider the ranking of genes using data from replicated microarray time course experiments, where there are multiple biological conditions, and the genes of interest are those whose temporal profiles differ across conditions. We derive a multisample multivariate empirical Bayes' statistic for ranking genes in the order of differential expression, from both longitudinal and cross-sectional replicated developmental microarray time course data. Our longitudinal multisample model assumes that time course replicates are independent and identically distributed multivariate normal vectors. On the other hand, we construct a cross-sectional model using a normal regression framework with any appropriate basis for the design matrices. In both cases, we use natural conjugate priors in our empirical Bayes' setting which guarantee closed form solutions for the posterior odds. The simulations and two case studies using published worm and mouse microarray time course datasets indicate that the proposed approaches perform satisfactorily.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1541-0420
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
65
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
40-51
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
On gene ranking using replicated microarray time course data.
pubmed:affiliation
Institute for Human Genetics, 513 Parnassus Avenue S965, University of California, San Francisco, California 94143-0794, USA. taiy@humgen.ucsf.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural