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 li...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05019-9 |
| _version_ | 1828127853619707904 |
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| author | Brian E. Vestal Elizabeth Wynn Camille M. Moore |
| author_facet | Brian E. Vestal Elizabeth Wynn Camille M. Moore |
| author_sort | Brian E. Vestal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T15:55:56Z |
| format | Article |
| id | doaj.art-a54bd06a29fe4c959d9b929f57c56dc8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-11T15:55:56Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a54bd06a29fe4c959d9b929f57c56dc82022-12-22T04:15:10ZengBMCBMC Bioinformatics1471-21052022-11-0123111310.1186/s12859-022-05019-9lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects modelsBrian E. Vestal0Elizabeth Wynn1Camille M. Moore2Center for Genes, Environment and Health, National Jewish HealthDepartment of Biostatistics and Informatics, University of Colorado DenverCenter for Genes, Environment and Health, National Jewish HealthAbstract 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.https://doi.org/10.1186/s12859-022-05019-9RNA-SeqLinear mixed modelsCorrelated data |
| spellingShingle | Brian E. Vestal Elizabeth Wynn Camille M. Moore lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models BMC Bioinformatics RNA-Seq Linear mixed models Correlated data |
| title | lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models |
| title_full | lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models |
| title_fullStr | lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models |
| title_full_unstemmed | lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models |
| title_short | lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models |
| title_sort | lmerseq an r package for analyzing transformed rna seq data with linear mixed effects models |
| topic | RNA-Seq Linear mixed models Correlated data |
| url | https://doi.org/10.1186/s12859-022-05019-9 |
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