Linked Life Data

12016052

Source:http://linkedlifedata.com/resource/pubmed/id/12016052

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2002-5-17
pubmed:abstractText
MOTIVATION: A common task in analyzing microarray data is to determine which genes are differentially expressed across two kinds of tissue samples or samples obtained under two experimental conditions. Recently several statistical methods have been proposed to accomplish this goal when there are replicated samples under each condition. However, it may not be clear how these methods compare with each other. Our main goal here is to compare three methods, the t-test, a regression modeling approach (Thomas et al., Genome Res., 11, 1227-1236, 2001) and a mixture model approach (Pan et al., http://www.biostat.umn.edu/cgi-bin/rrs?print+2001,2001a,b) with particular attention to their different modeling assumptions. RESULTS: It is pointed out that all the three methods are based on using the two-sample t-statistic or its minor variation, but they differ in how to associate a statistical significance level to the corresponding statistic, leading to possibly large difference in the resulting significance levels and the numbers of genes detected. In particular, we give an explicit formula for the test statistic used in the regression approach. Using the leukemia data of Golub et al. (Science, 285, 531-537, 1999), we illustrate these points. We also briefly compare the results with those of several other methods, including the empirical Bayesian method of Efron et al. (J. Am. Stat. Assoc., to appear, 2001) and the Significance Analysis of Microarray (SAM) method of Tusher et al. (PROC: Natl Acad. Sci. USA, 98, 5116-5121, 2001).
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:volume
18
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
546-54
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments.
pubmed:affiliation
Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo, MMC 303, 420 Delaware Street SE, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu
pubmed:publicationType
Journal Article, Comparative Study