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 in addition to the original WGCNA publication:
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 hdWGCNANext, open up R and install the required dependencies:
- Bioconductor, an R-based software ecosystem for bioinformatics and biostatistics.
- Seurat, a general-purpose toolkit for single-cell data science.
- WGCNA, a package for co-expression network analysis.
- igraph, a package for general network analysis and visualization.
- devtools, a package for package development in R.
# 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 devtools.
devtools::install_github('smorabit/hdWGCNA', ref='dev')Check out the hdWGCNA manuscript on bioRxiv, and our original description of applying WGCNA to single-nucleus RNA-seq data:
- High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems
- Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
For additional reading, we suggest the original WGCNA publication and papers describing relevant algorithms for co-expression network analysis:
- WGCNA: an R package for weighted correlation network analysis
- Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
- Eigengene networks for studying the relationships between co-expression modules
- Geometric Interpretation of Gene Coexpression Network Analysis
- Is My Network Module Preserved and Reproducible?