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LmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models.

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.

SUBMITTER: Vestal BE

PROVIDER: S-EPMC9670578 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models.

Vestal Brian E BE Wynn Elizabeth E Moore Camille M CM

BMC bioinformatics 20221116 1

<h4>Background</h4>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.<h4>Results</h4>In a simulation study comparing lmerSeq and two ex ...[more]

PMID: 36384492

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