Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models.

ABSTRACT:

Background

Power analysis becomes an inevitable step in experimental design of current biomedical research. Complex designs allowing diverse correlation structures are commonly used in RNA-Seq experiments. However, the field currently lacks statistical methods to calculate sample size and estimate power for RNA-Seq differential expression studies using such designs. To fill the gap, simulation based methods have a great advantage by providing numerical solutions, since theoretical distributions of test statistics are typically unavailable for such designs.

Results

In this paper, we propose a novel simulation based procedure for power estimation of differential expression with the employment of generalized linear mixed effects models for correlated expression data. We also pr

SUBMITTER: Yu L 

PROVIDER: S-EPMC7236949 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature