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glmmSeq
View on CRAN: Click
here
Download and install glmmSeq package within the R console
Install from CRAN:
install.packages("glmmSeq")
Install from Github:
library("remotes")
install_github("cran/glmmSeq") Install by package version:
library("remotes")
install_version("glmmSeq", "0.5.7") Attach the package and use:
library("glmmSeq")
Maintained by
Myles Lewis
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-03-11
Latest Update: 2025-09-01
Description:
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 .
How to cite:
Myles Lewis (2021). glmmSeq: General Linear Mixed Models for Gene-Level Differential Expression. R package version 0.5.7, https://cran.r-project.org/web/packages/glmmSeq. Accessed 01 Sep. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for glmmSeq
Functions, R codes and Examples using
the glmmSeq R package
Some associated functions: GlmmSeq-class . fcPlot . ggmodelPlot . glmmQvals . glmmRefit . glmmSeq . lmmSeq-class . lmmSeq . maPlot . metadata . modelPlot . summary.lmmSeq . tpm .
Some associated R codes: AllClass.R . PEAC_minimal_load.R . fcPlot.R . ggmodelPlot.R . glmmQvals.R . glmmRefit.R . glmmSeq.R . lmer_wald.R . lmmSeq.R . maPlot.R . modelPlot.R . summary_lmmSeq.R . Full glmmSeq package functions and examples
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