General Linear Mixed Models for Gene-Level Differential Expression
Plotly or ggplot fold change plots
Mixed model effects plot using ggplot2
Glmm Sequencing qvalues
Refit mixed effects model
An S4 class to define the glmmSeq output
GLMM with negative binomial distribution for sequencing count data
An S4 class to define the lmmSeq output
Linear mixed models for data matrix
MA plots
Minimal metadata from PEAC
Mixed model effects plot
Summarise a 'glmmSeq'/'lmmSeq' object
TPM count data from PEAC
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>.
Useful links