glmmSeq: General Linear Mixed Models for Gene-Level Differential Expression (original) (raw)
Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.
| Version: | 0.5.5 |
|---|---|
| Depends: | R (≥ 3.6.0) |
| Imports: | MASS, car, stats, ggplot2, ggpubr, glmmTMB, graphics, lme4, lmerTest, methods, plotly, qvalue, pbapply, pbmcapply |
| Suggests: | knitr, rmarkdown, kableExtra, DESeq2, edgeR, emmeans |
| Published: | 2022-10-08 |
| DOI: | 10.32614/CRAN.package.glmmSeq |
| Author: | Myles Lewis  [aut, cre], Katriona Goldmann [aut], Elisabetta Sciacca [aut], Cankut Cubuk [ctb], Anna Surace [ctb] |
| Maintainer: | Myles Lewis <myles.lewis at qmul.ac.uk> |
| BugReports: | https://github.com/myles-lewis/glmmSeq/issues |
| License: | MIT + file |
| URL: | https://myles-lewis.github.io/glmmSeq/,https://github.com/myles-lewis/glmmSeq |
| NeedsCompilation: | no |
| Language: | en-gb |
| Materials: | README NEWS |
| In views: | Omics |
| CRAN checks: | glmmSeq results |
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