title: DigOut: viewing differential expression genes as outliers.
abstract: 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.
authors.affiliation: Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P R China. yuhui@scbit.org
authors.forename: Hui
authors.initials: H
authors.lastname: Yu
authors.forename: Kang
authors.initials: K
authors.lastname: Tu
authors.forename: Lu
authors.initials: L
authors.lastname: Xie
authors.forename: Yuan-Yuan
authors.initials: YY
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000494:pharmacology; D001412:Bacillus subtilis / Q000187:drug effects / Q000235:genetics; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005081:Exercise Therapy; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000235:genetics / Q000628:therapy; D046228:Microarray Analysis / Q000706:statistics & numerical data*; D015233:Models, Statistical; D015999:Multivariate Analysis; D012372:ROC Curve
chemical_list: D000596:Amino Acids