hdWGCNA is an R package for performing weighted gene co-expression network analysis (WGCNA) in high dimensional transcriptomics data such as single-cell RNA-seq or spatial transcriptomics. hdWGCNA is highly modular and can construct co-expression networks across multi-scale cellular and spatial hierarchies. hdWGNCA identifies robust modules of inerconnected genes, and provides context for these modules through various biological knowledge sources. hdWGCNA requires data formatted as Seurat objects, one of the most ubiquitous formats for single-cell data. Check out the hdWGCNA in single-cell data tutorial or the hdWGCNA in spatial transcriptomics data tutorial to get started.
Note: hdWGCNA is under active development, so you may run into errors and small typos. We welcome users to write GitHub issues to report bugs, ask for help, and to request potential enhancements.
If you use hdWGCNA in your research, please cite the following papers:
We recommend creating an R conda environment environment for hdWGCNA.
# create new conda environment for R conda create -n hdWGCNA -c conda-forge r-base r-essentials # activate conda environment conda activate hdWGCNA
Next, open up R and install the required dependencies:
# install BiocManager install.packages("BiocManager") # install Bioconductor core packages BiocManager::install() # install additional packages: install.packages(c("Seurat", "WGCNA", "igraph", "devtools"))
Now you can install the hdWGCNA package using
Check out the hdWGCNA manuscript on bioRxiv, and our original description of applying WGCNA to single-nucleus RNA-seq data:
For additional reading, we suggest the original WGCNA publication and papers describing relevant algorithms for co-expression network analysis: