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hdWGCNA

High-Dimensional Weighted Gene Co-expression Network Analysis

📖 Documentation: https://zaoqu-liu.github.io/hdWGCNA/

Overview

hdWGCNA is a comprehensive R package for performing weighted gene co-expression network analysis (WGCNA) in high-dimensional transcriptomics data, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) datasets.

The package implements a metacell aggregation strategy to address the inherent sparsity of single-cell data, enabling robust construction of gene co-expression networks. hdWGCNA is designed with a modular architecture that facilitates context-specific network analysis across cellular and spatial hierarchies.

Key Features

Feature Description
🧬 Metacell Aggregation Addresses single-cell data sparsity through intelligent cell grouping
🔗 Modular Networks Identifies co-expressed gene modules via hierarchical clustering
📊 Module Eigengenes Computes representative expression signatures for each module
🎯 TF Network Analysis Infers regulatory relationships using XGBoost modeling
🔬 Module Preservation Assesses reproducibility across independent datasets
🔧 Seurat Integration Native support for Seurat v4 and v5 objects

Installation 

install.packages("hdWGCNA", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub 

# Install devtools if not already installed
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

# Install hdWGCNA
devtools::install_github("Zaoqu-Liu/hdWGCNA")

Using Conda Environment (Optional) 

# Create conda environment
conda create -n hdWGCNA -c conda-forge r-base=4.4

# Activate and install dependencies
conda activate hdWGCNA
conda install -c conda-forge -c bioconda r-seurat r-wgcna r-igraph r-devtools

Quick Start 

library(hdWGCNA)
library(Seurat)

# 1. Setup hdWGCNA experiment
seurat_obj <- SetupForWGCNA(seurat_obj, wgcna_name = "tutorial")

# 2. Construct metacells
seurat_obj <- MetacellsByGroups(seurat_obj, group.by = "cell_type")
seurat_obj <- NormalizeMetacells(seurat_obj)

# 3. Set expression matrix
seurat_obj <- SetDatExpr(seurat_obj, group_name = "INH", group.by = "cell_type")

# 4. Test soft power threshold
seurat_obj <- TestSoftPowers(seurat_obj)

# 5. Construct network
seurat_obj <- ConstructNetwork(seurat_obj)

# 6. Compute module eigengenes
seurat_obj <- ModuleEigengenes(seurat_obj)
seurat_obj <- ModuleConnectivity(seurat_obj)

Documentation

Comprehensive tutorials are available at https://zaoqu-liu.github.io/hdWGCNA/:

Getting Started

Core Tutorials

Advanced Topics

Citation

If you use hdWGCNA in your research, please cite:

Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Reports Methods (2023). DOI: 10.1016/j.crmeth.2023.100498

For transcription factor network analysis:

Childs JE, Morabito S, et al. Relapse to cocaine seeking is regulated by medial habenula NR4A2/NURR1 in mice. Cell Reports (2024). DOI: 10.1016/j.celrep.2024.113956

Contributing

We welcome contributions! Please submit issues for bug reports or feature requests.

License

GPL-3