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
data such as single-cell RNA-seq or spatial transcriptomics.
hdWGCNA is highly modular and can construct co-expression networks to facilitate multi-scale analysis
of cellular and spatial hierarchies. hdWGNCA identifies robust modules of inerconnected genes, and
provides biologicalcontext for these modules through various biological knowledge sources.
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
<ahref="ST_basics.html">hdWGCNA in spatial transcriptomics data</a><aclass="anchor"aria-label="anchor"href="#hdwgcna-in-spatial-transcriptomics-data"></a>
</h3>
<p>This tutorial covers the essential functions to construct a co-expression network in spatial transcriptomics data with hdWGCNA.</p>
<p>Morabito et al. High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems. bioRxiv (2022) [hdWGCNA]</p>
<pre>@Article{,
title = {High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems},
author = {Samuel Morabito and Emily Miyoshi and Neethu Michael and Saba Shahin and Alessandra Cadete Martini and Elizabeth Head and Justine Silva and Kelsey Leavy and Mari Perez-Rosendahl and Vivek Swarup},
<p>Morabito and Miyoshi et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease. Nature Genetics (2021) [scWGCNA]</p>
<pre>@Article{,
title = {Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease},
author = {Samuel Morabito and Emily Miyoshi and Neethu Michael and Saba Shahin and Alessandra Cadete Martini and Elizabeth Head and Justine Silva and Kelsey Leavy and Mari Perez-Rosendahl and Vivek Swarup},
<p>hdWGCNA is an R package for performing weighted gene co-expression network analysis <ahref="https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/"class="external-link">(WGCNA)</a> in high dimensional data such as single-cell RNA-seq or spatial transcriptomics. hdWGCNA is highly modular and can construct co-expression networks to facilitate multi-scale analysis of cellular and spatial hierarchies. hdWGNCA identifies robust modules of inerconnected genes, and provides biologicalcontext for these modules through various biological knowledge sources. hdWGCNA requires data formatted as <ahref="https://satijalab.org/seurat/index.html"class="external-link">Seurat</a> objects, one of the most ubiquitous formats for single-cell data. Check out the <ahref="https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html">hdWGCNA in single-cell data tutorial</a> or the <ahref="https://smorabit.github.io/hdWGCNA/articles/ST_basics.html">hdWGCNA in spatial transcriptomics data tutorial</a> to get started.</p>
<p><strong>Note:</strong> hdWGCNA is under active development, so you may run into errors and small typos. We welcome users to write <ahref="https://docs.github.com/en/issues/tracking-your-work-with-issues/creating-an-issue"class="external-link">GitHub issues</a> to, report bugs, ask for help and ask for potential enhancements. GitHub issues are preferred to emailing the authors directly.</p>
<p>hdWGCNA is an R package for performing weighted gene co-expression network analysis <ahref="https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/"class="external-link">(WGCNA)</a> 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 <ahref="https://satijalab.org/seurat/index.html"class="external-link">Seurat</a> objects, one of the most ubiquitous formats for single-cell data. Check out the <ahref="https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html">hdWGCNA in single-cell data tutorial</a> or the <ahref="https://smorabit.github.io/hdWGCNA/articles/ST_basics.html">hdWGCNA in spatial transcriptomics data tutorial</a> to get started.</p>
<p><strong>Note:</strong> hdWGCNA is under active development, so you may run into errors and small typos. We welcome users to write <ahref="https://docs.github.com/en/issues/tracking-your-work-with-issues/creating-an-issue"class="external-link">GitHub issues</a> to report bugs, ask for help, and to request potential enhancements.</p>
<p>If you use hdWGCNA in your research, please cite the following papers:</p>
<ul>
<li><ahref="https://www.biorxiv.org/content/10.1101/2022.09.22.509094v1"class="external-link">Morabito et al. bioRxiv 2022</a></li>
<li><ahref="https://doi.org/10.1038/s41588-021-00894-z"class="external-link">Morabito & Miyoshi et al. Nature Genetics 2021</a></li>
<p>If you are unfamiliar with WGCNA, we suggest reading the original WGCNA publication:</p>
<p>Check out the hdWGCNA manuscript on bioRxiv, and our original description of applying WGCNA to single-nucleus RNA-seq data:</p>
<ul>
<li><ahref="https://doi.org/10.1186/1471-2105-9-559"class="external-link">WGCNA: an R package for weighted correlation network analysis</a></li>
<li><ahref="https://www.biorxiv.org/content/10.1101/2022.09.22.509094v1"class="external-link">High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems</a></li>
<li><ahref="https://doi.org/10.1038/s41588-021-00894-z"class="external-link">Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease</a></li>
</ul>
<p>There are a number of additional relevant publications for WGCNA and related algorithms like Dynamic Tree Cut and Module Preservation analysis:</p>
<p>For additional reading, we suggest the original WGCNA publication and papers describing relevant algorithms for co-expression network analysis:</p>
<ul>
<li><ahref="https://doi.org/10.1186/1471-2105-9-559"class="external-link">WGCNA: an R package for weighted correlation network analysis</a></li>
<li><ahref="https://doi.org/10.1093/bioinformatics/btm563"class="external-link">Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R</a></li>
<li><ahref="https://doi.org/10.1186/1752-0509-1-54"class="external-link">Eigengene networks for studying the relationships between co-expression modules</a></li>
<li><ahref="https://doi.org/10.1371/journal.pcbi.1000117"class="external-link">Geometric Interpretation of Gene Coexpression Network Analysis</a></li>
<li><ahref="https://doi.org/10.1371/journal.pcbi.1001057"class="external-link">Is My Network Module Preserved and Reproducible?</a></li>
</ul>
<p>Our original description of applying WGCNA to single-nucleus RNA-seq data:</p>
<ul>
<li><ahref="https://doi.org/10.1038/s41588-021-00894-z"class="external-link">Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease</a></li>
"Morabito et al. High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems. bioRxiv (2022) [hdWGCNA]"
"Morabito and Miyoshi et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease. Nature Genetics (2021) [scWGCNA]"
