DIGOUT: VIEWING DIFFERENTIAL EXPRESSION GENES AS OUTLIERS
With regards to well-replicated two-conditional microarray datasets, the selection of di.erentially expressed (DE) genes is a well-studied computational topic, but for multiconditional 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,. nding that it performs signi.cantly 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.
Microarray replication-lacking di.erential expression genes outliers
HUI YU KANG TU LU XIE YUAN-YUAN LI
Bioinformatics Center, Key Lab of Systems Biology Shanghai Institutes for Biological Sciences Chines National Heart Lung and Blood Institute National Institutes of Health Bldg 10, 9000 Rockville Pike B Shanghai Center for Bioinformation Technology 100 Qinzhou Road, Shanghai 200235, P. R. China
国际会议
上海
英文
314-328
2010-12-06（万方平台首次上网日期，不代表论文的发表时间）