Linked Life Data

21155026

Source:http://linkedlifedata.com/resource/pubmed/id/21155026

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-12-14
pubmed:abstractText
With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
8 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
161-75
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
DigOut: viewing differential expression genes as outliers.
pubmed:affiliation
Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P R China. yuhui@scbit.org
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Evaluation Studies