LmerSeq: an R Package for Analyzing Transformed RNA-Seq Data with Linear Mixed Effects Models

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2022 Nov 17

PMID 36384492

Authors

Brian E Vestal

Elizabeth Wynn

Camille M Moore

Affiliations

Soon will be listed here.

Abstract

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.

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