Linked Life Data

16078371

Source:http://linkedlifedata.com/resource/pubmed/id/16078371

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2005-8-3
pubmed:abstractText
A class of nonparametric statistical methods, including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarrays (SAM) and the mixture model method (MMM) have been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic, for example, a t-statistic, and then using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB and SAM.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
989-1006
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Using weighted permutation scores to detect differential gene expression with microarray data.
pubmed:affiliation
Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455-0378, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Evaluation Studies, Research Support, N.I.H., Extramural