update readme to include suggested reading

[Seurat](https://satijalab.org/seurat/index.html) objects, one of the most ubiquitous

formats for single-cell data. Check out the [hdWGCNA basics tutorial](https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html) to get started.

## Installation

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

```r

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

```

## Suggested Reading

If you are unfamiliar with WGCNA, we suggest reading the original WGCNA publication:

* [WGCNA: an R package for weighted correlation network analysis](https://doi.org/10.1186/1471-2105-9-559)

There are a number of additional relevant publications for WGCNA and related algorithms

like Dynamic Tree Cut and Module Preservation analysis:

* [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)

Our original description of applying WGCNA to single-nucleus RNA-seq data:

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

<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">

<code class="sourceCode R"><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://devtools.r-lib.org/reference/remote-reexports.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">'smorabit/hdWGCNA'</span>, ref<span class="op">=</span><span class="st">'dev'</span><span class="op">)</span></code></pre></div>

</div>

<div class="section level2">

<h2 id="suggested-reading">Suggested Reading<a class="anchor" aria-label="anchor" href="#suggested-reading"></a>

</h2>

<p>If you are unfamiliar with WGCNA, we suggest reading the original WGCNA publication:</p>

<ul>

<li><a href="https://doi.org/10.1186/1471-2105-9-559" class="external-link">WGCNA: an R package for weighted correlation network analysis</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>

<ul>

<li><a href="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><a href="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><a href="https://doi.org/10.1371/journal.pcbi.1000117" class="external-link">Geometric Interpretation of Gene Coexpression Network Analysis</a></li>

<li><a href="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><a href="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>

</div>

</div>

  </div>