glmmSeq: General Linear Mixed Models for Gene-Level Differential Expression

Using random and fixed effects to model expression at an individual gene level can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, therefore do not capture interaction terms. This package uses negative binomial mixed effects models to fit gene expression with matched samples. This is particularly useful for investigating changes in gene expression between groups of individuals over time, as seen in: Rivellese F., Surace A.E.A., Goldmann K., Sciacca E., Giorli G., Cubuk C., John C.R., Nerviani A., Fossati-Jimack L., Thorborn G., Humby F., Bombardieri M., Lewis M.J., Pitzalis C. (2021) "Molecular Pathology Profiling of Synovial Tissue Predicts Response to Biologic Treatment in Rheumatoid Arthritis" [Manuscript in preparation].

Version: 0.1.0
Depends: R (≥ 3.6.0)
Imports: MASS, car, stats, gghalves, ggplot2, ggpubr, graphics, lme4, methods, plotly, qvalue, pbapply, pbmcapply
Suggests: knitr, rmarkdown, kableExtra, edgeR
Published: 2021-03-30
Author: Myles Lewis ORCID iD [aut], Katriona Goldmann ORCID iD [aut, cre], Elisabetta Sciacca ORCID iD [aut], Cankut Cubuk ORCID iD [ctb], Anna Surace ORCID iD [ctb]
Maintainer: Katriona Goldmann <k.goldmann at>
License: MIT + file LICENSE
NeedsCompilation: no
Language: en-gb
Materials: README NEWS
CRAN checks: glmmSeq results


Reference manual: glmmSeq.pdf
Vignettes: glmmSeq
Package source: glmmSeq_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: glmmSeq_0.1.0.tgz, r-oldrel: glmmSeq_0.1.0.tgz
Old sources: glmmSeq archive


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