# multiWGCNA: an R package for deep mining gene co-expression networks in multi-trait expression data
The multiWGCNA R package builds on the existing weighted gene co-expression
network analysis (WGCNA) package by extending workflows to expression data with
two dimensions. multiWGCNA is especially useful for the study of
disease-associated modules across time or space. For more information, please
see the multiWGCNA paper available at https://doi.org/10.1186/s12859-023-05233-z.
# Installation
The multiWGCNA R package can be installed from Bioconductor like this:
```
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("multiWGCNA")
```
The development version of multiWGCNA can be installed from GitHub like this:
```
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("fogellab/multiWGCNA")
```
# Vignettes
We recommend running through both of the vignettes before applying multiWGCNA to your own data:
* The autism_full_workflow.Rmd vignette provides a quick example of how to use multiWGCNA.
* The astrocyte_map2.Rmd vignette provides a more in-depth tutorial discussing the preservation analyses from the manuscript and functions for visualization.
# Citation
To cite multiWGCNA in publications, please use:
Tommasini, D, Fogel, BL (2023). multiWGCNA: an R package for deep mining gene
co-expression networks in multi-trait expression data. BMC Bioinformatics, 24,
1:115.
For LaTeX users, a BibTeX entry is available here:
```
@Article{,
title = {multiWGCNA: an R package for deep mining gene co-expression
networks in multi-trait expression data},
author = {Dario Tommasini and Brent L. Fogel},
journal = {BMC Bioinformatics},
year = {2023},
volume = {24},
number = {1},
pages = {115},
}
```
