An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.

ABSTRACT: The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without m

SUBMITTER: Xu M 

PROVIDER: S-EPMC5875907 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature