Commit 4bfaa5eb authored by smorabit's avatar smorabit
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docstring for PlotKMEs/

parent 2774e8e1
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@@ -16,7 +16,7 @@ hdWGCNA constructs co-expression networks in a cell-type-specific manner,
identifies robust modules of inerconnected genes, and provides biological
context for these modules. hdWGCNA is directly compatible with
[Seurat](https://satijalab.org/seurat/index.html) objects, one of the most ubiquitous
formats for single-cell data. Check out the [hdWGCNA basics tutorial](articles/basic_tutorial.html) to get started.
formats for single-cell data. Check out the [hdWGCNA basics tutorial](https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html) to get started.


## Installation
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</h1></div>

<p><strong>Note:</strong> hdWGCNA is under active development, so you will likely run into errors if you choose to use hdWGCNA before its first stable release.</p>
<p>hdWGCNA, formerly known as scWGCNA, is an R package for performing weighted gene co-expression network analysis <a href="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 constructs co-expression networks in a cell-type-specific manner, identifies robust modules of inerconnected genes, and provides biological context for these modules. hdWGCNA is directly compatible with <a href="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 <a href="articles/basic_tutorial.html">hdWGCNA basics tutorial</a> to get started.</p>
<p>hdWGCNA, formerly known as scWGCNA, is an R package for performing weighted gene co-expression network analysis <a href="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 constructs co-expression networks in a cell-type-specific manner, identifies robust modules of inerconnected genes, and provides biological context for these modules. hdWGCNA is directly compatible with <a href="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 <a href="https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html" class="external-link">hdWGCNA basics tutorial</a> to get started.</p>
<div class="section level2">
<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
</h2>