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This allows us to include random effects as well as fixed effects. For the purpose of the package we use the `glmer` function from the [`lme4`](https://CRAN.R-project.org/package=lme4)\npackage which fits a GLMM.\n\nThis package focuses in particular on changes in genes expression between different response or treatment groups over time. \n\n\n# Loading the package\n\n### From CRAN\n\n```\ninstall.packages(\"glmmSeq\")\n```\n\n### From Github\n\n```\ndevtools::install_github(\"myles-lewis/glmmSeq\")\n```\n\n### Locally\n\nYou can also download the source directory and load the functions individually:\n\n```\nfunctions = list.files(\"./R\", full.names = TRUE)\ninvisible(lapply(functions, source))\n```\n\nBut you will need to load in the additional libraries then:\n\n```\n# Install CRAN packages\ninvisible(lapply(c(\"MASS\", \"car\", \"ggplot2\", \"ggpubr\", \"lme4\", \n                   \"lmerTest\", \"methods\", \"parallel\", \"plotly\", \n                   \"pbapply\", \"pbmcapply\"),\n                 function(p){\n                   if(! p %in% rownames(installed.packages())) {\n                     install.packages(p)\n                   }\n                   library(p, character.only=TRUE)\n                 }))\n\n# Install BioConductor packages\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n  install.packages(\"BiocManager\")\ninvisible(lapply(c(\"qvalue\"), function(p){\n  if(! p %in% rownames(installed.packages())) BiocManager::install(p)\n  library(p, character.only=TRUE)\n}))\n```\n\n# Example script\n\nFor examples see the [vignette](https://myles-lewis.github.io/glmmSeq/articles/glmmSeq.html). \n\n# Reference\n\nglmmSeq was developed by the bioinformatics team at the [Experimental Medicine \u0026 Rheumatology department](https://www.qmul.ac.uk/whri/emr/) at Queen Mary University London.\n\nIf you use this package please cite as:\n\n```\ncitation(\"glmmSeq\")\n\n## To cite package ‘glmmSeq’ in publications use:\n##\n##  Myles Lewis, Katriona Goldmann, Elisabetta Sciacca, Cankut Cubuk and Anna Surace (2021). \n##  glmmSeq: General Linear Mixed Models for Gene-level Differential Expression. \n##  R package version 0.5.4. https://github.com/myles-lewis/glmmSeq\n##\n## A BibTeX entry for LaTeX users is\n##\n##  @Manual{,\n##    title = {glmmSeq: General Linear Mixed Models for Gene-level Differential Expression},\n##    author = {Myles Lewis and Katriona Goldmann and Elisabetta Sciacca and Cankut Cubuk and Anna Surace},\n##    year = {2022},\n##    note = {R package version 0.5.4},\n##    url = {https://github.com/myles-lewis/glmmSeq},\n##  }\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmyles-lewis%2Fglmmseq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmyles-lewis%2Fglmmseq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmyles-lewis%2Fglmmseq/lists"}