Linked Life Data

18840309

Source:http://linkedlifedata.com/resource/pubmed/id/18840309

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2008-10-8
pubmed:abstractText
We propose an empirical Bayes method based on the extreme value theory (EVT) (BE) for the analysis of data from spotted microarrays where the interest of the investigator (e.g. to identify up-regulated gene markers of a disease) or the design of the experiment (e.g. in certain 'wild-type versus mutant' experiments) limits identification of differentially expressed genes to those regulated in a single direction (either up or down). In such experiments, unlike in genome-wide microarrays, analysis is restricted to the tail of the distribution (extremes) of all the genes in the genome. The EVT provides a platform to account for this extreme behaviour, and is therefore a natural candidate for inference about differential expression. We compared the performance of the developed BE method with two other empirical Bayes methods on two real 'wild-type versus mutant' datasets where a single direction of regulation was expected due to experimental design, and in a simulation study. The BE method appears to have a better fit to the real data. In the analysis of simulated data, the BE method showed better accuracy and precision while being robust to different characteristics of microarray experiments. The BE method, therefore, seems promising and useful for inference about differential expression in microarrays where either only up- or down-regulated genes are relevant or expected.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1469-5073
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
90
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
347-61
pubmed:dateRevised
2010-12-28
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Extreme value theory in analysis of differential expression in microarrays where either only up- or down-regulated genes are relevant or expected.
pubmed:affiliation
Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA. ri25@cornell.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural