Linked Life Data

17542776

Source:http://linkedlifedata.com/resource/pubmed/id/17542776

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-6-4
pubmed:abstractText
An objective of microarray data analysis is to identify gene expressions that are associated with a disease related outcome. For each gene, a test statistic is computed to determine if an association exists, and this statistic generates a marginal p-value. In an effort to pool this information across genes, a p-value density function is derived. The p-value density is modeled as a mixture of a uniform (0,1) density and a scaled ratio of normal densities derived from the asymptotic normality of the test statistic. The p-values are assumed to be weakly dependent and a quasi-likelihood is used to estimate the parameters in the mixture density. The quasi-likelihood and the weak dependence assumption enables estimation and asymptotic inference on the false discovery rate for a given rejection region, and its inverse, the p-value threshold parameter for a fixed false discovery rate. A false discovery rate analysis on a localized prostate cancer data set is used to illustrate the methodology. Simulations are performed to assess the performance of this methodology.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1544-6115
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
Article14
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Inference on the limiting false discovery rate and the p-value threshold parameter assuming weak dependence between gene expression levels within subject.
pubmed:affiliation
Memorial Sloan-Kettering Cancer Center, USA. hellerg@mskcc.org
pubmed:publicationType
Journal Article