high dimensional WGCNA

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 context-specific co-expression networks across cellular and spatial hierarchies. hdWGNCA identifies modules of highly co-expressed genes and provides context for these modules via statistical testing and biological knowledge sources. hdWGCNA uses datasets formatted as Seurat objects. Check out the hdWGCNA in single-cell data tutorial or the hdWGCNA in spatial transcriptomics data tutorial to get started.

New functionality hdWGCNA is now able to perform Transcription Factor Regulatory Network Analysis. This functionality was introduced in our publication Childs & Morabito et al., Cell Reports (2024).

If you use hdWGCNA in your research, please cite the following papers in addition to the original WGCNA publication:

• Morabito et al., Cell Reports Methods (2023)
• Morabito & Miyoshi et al., Nature Genetics (2021)

Installation

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 R and install the required dependencies:

• Bioconductor, an R-based software ecosystem for bioinformatics and biostatistics.
• devtools, a package for package development in R.
• Seurat, a general-purpose toolkit for single-cell data science.

# install BiocManager
install.packages("BiocManager")

# install Bioconductor core packages
BiocManager::install()

# install devtools
BiocManager::install("devtools")

# install latest version of Seurat from CRAN
install.packages("Seurat")

# alternatively, install Seurat v4
install.packages("Seurat", repos = c("https://satijalab.r-universe.dev', 'https://cloud.r-project.org"))

Now you can install the hdWGCNA package using devtools.

devtools::install_github('smorabit/hdWGCNA', ref='dev')

Suggested Reading

Check out the paper describing hdWGCNA, our paper introducing transcription factor regulatory network analysis with hdWGCNA, 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.

• hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
• Relapse to cocaine seeking is regulated by medial habenula NR4A2/NURR1 in mice
• Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease

WGCNA and related algorithms:

• 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?

Note about package development: 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.