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].


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install.packages("glmmSeq")

0.1.0 by Katriona Goldmann, 16 days ago


https://github.com/KatrionaGoldmann/glmmSeq


Report a bug at https://github.com/KatrionaGoldmann/glmmSeq/issues


Browse source code at https://github.com/cran/glmmSeq


Authors: Myles Lewis [aut] , Katriona Goldmann [aut, cre] , Elisabetta Sciacca [aut] , Cankut Cubuk [ctb] , Anna Surace [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports MASS, car, stats, gghalves, ggplot2, ggpubr, graphics, lme4, methods, plotly, qvalue, pbapply, pbmcapply

Suggests knitr, rmarkdown, kableExtra, edgeR


See at CRAN