Related Experiment Video
Updated: Aug 21, 2025
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models
Brian E Vestal1, Elizabeth Wynn2, Camille M Moore3
1Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson St, Denver, CO, 80206, USA. vestalb@njhealth.org.
Background:
Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity. This R package contains a set of utilities to fit linear mixed effects models to transformed RNA-Seq counts that properly account for this dependence when performing statistical analyses.
Results:
In a simulation study comparing lmerSeq and two existing methodologies that also work with transformed RNA-Seq counts, we found that lmerSeq was comprehensively better in terms of nominal error rate control and statistical power.
Conclusions:
Existing R packages for analyzing transformed RNA-Seq data with linear mixed models are limited in the variance structures they allow and/or the transformation methods they support. The lmerSeq package offers more flexibility in both of these areas and gave substantially better results in our simulations.
