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 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 [issues need fixing before 2025-09-01] Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=glmmSeq to link to this page.
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