# hdWGCNA-2

**Repository Path**: joyeric_admin_admin/hd-wgcna-2

## Basic Information

- **Project Name**: hdWGCNA-2
- **Description**: https://github.com/smorabit/hdWGCNA
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-06-13
- **Last Updated**: 2024-06-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# high dimensional WGCNA <img src="man/figures/logo.png" align="right" height="20%" width="20%" />


[![R](https://img.shields.io/github/r-package/v/smorabit/hdWGCNA)](https://github.com/smorabit/hdWGCNA/tree/dev)
[![ISSUES](https://img.shields.io/github/issues/smorabit/hdWGCNA)](https://github.com/smorabit/hdWGCNA/issues)
[![Publication](https://img.shields.io/badge/publication-Cell%20Rep%20Meth-%2300A1D7)](https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00127-3)
[![Lifecycle:Maturing](https://img.shields.io/badge/Lifecycle-Maturing-007EC6)](https://github.com/smorabit/hdWGCNA)
[![Stars](https://img.shields.io/github/stars/smorabit/hdWGCNA?style=social)](https://github.com/smorabit/hdWGCNA/)


hdWGCNA is an R package for performing weighted gene co-expression network analysis [(WGCNA)](https://doi.org/10.1186/1471-2105-9-559) 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](https://satijalab.org/seurat/index.html) objects. Check out the [hdWGCNA in single-cell data tutorial](https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html) or the [hdWGCNA in spatial transcriptomics data tutorial](https://smorabit.github.io/hdWGCNA/articles/ST_basics.html) to get started.

**Seurat v5 compatibility** As of hdWGCNA v0.3.00 [Seurat version 5](https://satijalab.org/seurat/articles/get_started_v5.html) is now supported in addition to Seurat v4. 

If you use hdWGCNA in your research, please cite the following papers in addition to the [original WGCNA publication](https://doi.org/10.1186/1471-2105-9-559):

* [Morabito et al., Cell Reports Methods (2023)](https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00127-3)
* [Morabito & Miyoshi et al., Nature Genetics (2021)](https://doi.org/10.1038/s41588-021-00894-z)


## Installation

We recommend creating an R [conda environment](https://docs.conda.io/en/latest/) environment for hdWGCNA.

```bash
# 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](https://www.bioconductor.org/), an R-based software ecosystem for bioinformatics and biostatistics.
* [Seurat](https://satijalab.org/seurat/index.html), a general-purpose toolkit for single-cell data science.
* [WGCNA](https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/), a package for co-expression network analysis.
* [igraph](https://igraph.org/r/), a package for general network analysis and visualization.
* [devtools](https://devtools.r-lib.org/), a package for package development in R.

```r
# install BiocManager
install.packages("BiocManager")

# install Bioconductor core packages
BiocManager::install()

# install additional packages:
BiocManager::install(c("WGCNA", "igraph", "devtools", "GeneOverlap", "ggrepel", "UCell"))
devtools::install_github("NightingaleHealth/ggforestplot")

# install Seurat v5 
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`.

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

## Suggested Reading

Check out the paper describing hdWGCNA, and our original description of applying WGCNA to single-nucleus RNA-seq data:

* [hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data](https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00127-3) 

* [Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease](https://doi.org/10.1038/s41588-021-00894-z) 

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](https://doi.org/10.1186/1471-2105-9-559)
* [Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R](https://doi.org/10.1093/bioinformatics/btm563)
* [Eigengene networks for studying the relationships between co-expression modules](https://doi.org/10.1186/1752-0509-1-54)
* [Geometric Interpretation of Gene Coexpression Network Analysis](https://doi.org/10.1371/journal.pcbi.1000117)
* [Is My Network Module Preserved and Reproducible?](https://doi.org/10.1371/journal.pcbi.1001057)

**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](https://docs.github.com/en/issues/tracking-your-work-with-issues/creating-an-issue)
to report bugs, ask for help, and to request potential enhancements.