scWGCNA is a full-featured bioinformatics package based on Seurat and WGCNA to perform co-expression network analysis in single-cell or single-nucleus RNA-seq datasets. WGCNA was originally built for the analysis of bulk gene expression datasets, and the performance of vanilla WGCNA on single-cell data is limited due to the inherent sparsity of scRNA-seq data. To account for this, scWGCNA has a function to aggregate transcriptionally similar cells into pseudo-bulk metacells before running the WGCNA pipeline, greatly reducing the sparsity of the dataset while preserving cellular heterogeneity. Furthermore, WGCNA is a well established tool with many different options and parameters, so we recommend trying different options in network construction that are best suited to your dataset.
To run scWGCNA, you first need to have a single-cell transcriptomic dataset in Seurat format with clustering and dimensionality reduction already computed. If this all sounds like gibberish to you, I would recommend first looking at the Seurat guided clustering tutorial.
scWGCNA has been tested only on R 3.6 and 4.0 on Mac OS and Linux environments (sorry to all Windows bioinformaticians, if there are any of you out there). To run scWGCNA, there are a few other R packages that you need to install. Open up a R session and enter the following commands:
install.packages('WGCNA')
install.packages('igraph')
install.packages('devtools')
# install Seurat, check their website for the most up-to-date instructions
install.packages('Seurat')
Now you can install the scWGCNA package using devtools:
devtools::install_github('smorabit/scWGCNA')
If scWGCNA is useful in your research, please consider citing our publication.
This section is under construction since I have totally changed how scWGCNA works!