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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 |
Maintainer: | Myles Lewis <myles.lewis at qmul.ac.uk> |
BugReports: | https://github.com/myles-lewis/glmmSeq/issues |
License: | MIT + file LICENSE |
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 |
Reference manual: | glmmSeq.pdf |
Vignettes: |
glmmSeq |
Package source: | glmmSeq_0.5.5.tar.gz |
Windows binaries: | r-devel: glmmSeq_0.5.5.zip, r-release: glmmSeq_0.5.5.zip, r-oldrel: glmmSeq_0.5.5.zip |
macOS binaries: | r-release (arm64): glmmSeq_0.5.5.tgz, r-oldrel (arm64): glmmSeq_0.5.5.tgz, r-release (x86_64): glmmSeq_0.5.5.tgz, r-oldrel (x86_64): glmmSeq_0.5.5.tgz |
Old sources: | glmmSeq archive |
Reverse suggests: | tidybulk |
Please use the canonical form https://CRAN.R-project.org/package=glmmSeq to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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