DIGOUT: VIEWING DIFFERENTIAL EXPRESSION GENES AS OUTLIERS

摘要

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.