A rank-based algorithm of differential expression analysis for small cell line data with statistical control.

ABSTRACT: To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identi

SUBMITTER: Li X 

PROVIDER: S-EPMC6433897 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature