Linked Life Data

19297690

Source:http://linkedlifedata.com/resource/pubmed/id/19297690

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2009-3-17
pubmed:abstractText
Microarray analysis has become a popular and routine method in functional genomics. It is typical for such experiments to involve a small number of replicates, which causes unreliable estimates of the sample variance. Microarrays have fostered the development of new statistical methods to analyze data resulting from experiments with small sample sizes. In this study, we tackle the problem of evaluating the performance of statistical tests for generating ranked gene lists from two-channel direct comparisons. We propose an evaluation method based on a oligonucleotide microarray with a large number of replicate spots yielding a maximum of 400 replicates per gene. We apply Spearman's rank correlation coefficient to ranked gene-lists generated by eight widely used microarray specific test statistics, which are applied to small random samples. We could show that variance stabilizing methods such as Cyber-T, SAM, and LIMMA can be beneficial for very small sample sizes and that SAM and the t-test provide stronger control of the type I error rate than the other methods. Specifically, we report that for four replicates all methods reach a high to very high correlation with our reference standard.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1873-4863
pubmed:author
pubmed:issnType
Electronic
pubmed:day
10
pubmed:volume
140
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
18-26
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
An evaluation framework for statistical tests on microarray data.
pubmed:affiliation
Center for Biotechnology, Bielefeld University, Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de
pubmed:publicationType
Journal Article