https://github.com/animesh/hdwgcna

High dimensional weighted gene co-expression network analysis

https://github.com/animesh/hdwgcna

Repository

High dimensional weighted gene co-expression network analysis

https://github.com/animesh/hdWGCNA/blob/dev/

# high dimensional WGCNA 

[![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)
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hdWGCNA is an R package for performing weighted gene co-expression network analysis [(WGCNA)](https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/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](https://satijalab.org/seurat/index.html) objects,
one of the most ubiquitous formats for single-cell data. 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.

**Note:** 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.

If you use hdWGCNA in your research, please cite the following papers:

* [Morabito et al. bioRxiv 2022](https://www.biorxiv.org/content/10.1101/2022.09.22.509094v1)
* [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 up 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:
install.packages(c("Seurat", "WGCNA", "igraph", "devtools"))

```

Now you can install the hdWGCNA package using `devtools`.

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

## Suggested Reading

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](https://www.biorxiv.org/content/10.1101/2022.09.22.509094v1)
* [Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimers 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)

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