Identification of population-level differentially expressed genes in one-phenotype data.

ABSTRACT:

Motivation

For some specific tissues, such as the heart and brain, normal controls are difficult to obtain. Thus, studies with only a particular type of disease samples (one phenotype) cannot be analyzed using common methods, such as significance analysis of microarrays, edgeR and limma. The RankComp algorithm, which was mainly developed to identify individual-level differentially expressed genes (DEGs), can be applied to identify population-level DEGs for the one-phenotype data but cannot identify the dysregulation directions of DEGs.

Results

Here, we optimized the RankComp algorithm, termed PhenoComp. Compared with RankComp, PhenoComp provided the dysregulation directions of DEGs and had more robust detection power in both simulated and real one-phenotype data. Moreover, u

SUBMITTER: Xie J 

PROVIDER: S-EPMC7520039 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature