https://github.com/myles-lewis/glmmseq

Gene-level general linear mixed model
https://github.com/myles-lewis/glmmseq

bioinformatics cran differential-gene-expression gene-expression glmm mixed-models transcriptomics

Last synced: 3 months ago
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Gene-level general linear mixed model

• Host: GitHub
• URL: https://github.com/myles-lewis/glmmseq
• Owner: myles-lewis
• License: other
• Created: 2020-08-12T11:04:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2025-10-01T19:55:16.000Z (4 months ago)
• Last Synced: 2025-10-04T05:48:45.366Z (4 months ago)
• Topics: bioinformatics, cran, differential-gene-expression, gene-expression, glmm, mixed-models, transcriptomics
• Language: R
• Homepage: https://myles-lewis.github.io/glmmSeq/
• Size: 13.9 MB
• Stars: 23
• Watchers: 4
• Forks: 10
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE

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# glmmSeq 



This R package is designed to model gene expression with a general linear mixed model (GLMM). 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)

package which fits a GLMM.

This package focuses in particular on changes in genes expression between different response or treatment groups over time. 

# Loading the package

### From CRAN

```

install.packages("glmmSeq")

```

### From Github

```

devtools::install_github("myles-lewis/glmmSeq")

```

### Locally

You can also download the source directory and load the functions individually:

```

functions = list.files("./R", full.names = TRUE)

invisible(lapply(functions, source))

```

But you will need to load in the additional libraries then:

```

# Install CRAN packages

invisible(lapply(c("MASS", "car", "ggplot2", "ggpubr", "lme4", 

                   "lmerTest", "methods", "parallel", "plotly", 

                   "pbapply", "pbmcapply"),

                 function(p){

                   if(! p %in% rownames(installed.packages())) {

                     install.packages(p)

                   }

                   library(p, character.only=TRUE)

                 }))

# Install BioConductor packages

if (!requireNamespace("BiocManager", quietly = TRUE))

  install.packages("BiocManager")

invisible(lapply(c("qvalue"), function(p){

  if(! p %in% rownames(installed.packages())) BiocManager::install(p)

  library(p, character.only=TRUE)

}))

```

# Example script

For examples see the [vignette](https://myles-lewis.github.io/glmmSeq/articles/glmmSeq.html). 

# Reference

glmmSeq was developed by the bioinformatics team at the [Experimental Medicine & Rheumatology department](https://www.qmul.ac.uk/whri/emr/) at Queen Mary University London.

If you use this package please cite as:

```

citation("glmmSeq")

## To cite package ‘glmmSeq’ in publications use:

##

##  Myles Lewis, Katriona Goldmann, Elisabetta Sciacca, Cankut Cubuk and Anna Surace (2021). 

##  glmmSeq: General Linear Mixed Models for Gene-level Differential Expression. 

##  R package version 0.5.4. https://github.com/myles-lewis/glmmSeq

##

## A BibTeX entry for LaTeX users is

##

##  @Manual{,

##    title = {glmmSeq: General Linear Mixed Models for Gene-level Differential Expression},

##    author = {Myles Lewis and Katriona Goldmann and Elisabetta Sciacca and Cankut Cubuk and Anna Surace},

##    year = {2022},

##    note = {R package version 0.5.4},

##    url = {https://github.com/myles-lewis/glmmSeq},

##  }

```