coexpressionclustering.qmd

---
title: "Co-expression clustering"
author: "Cox Lab"
format: 
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editor: source
date: today
bibliography: references.bib
csl: nature.csl
---

This analysis is provided through the `R`-language integration into Perseus and therefore requires `R` as well as the `WGCNA` package to be installed. Visit [WGCNA](WGCNA.html) [@langfelder2008] page for more information and installation instructions.

More information about co-expression clustering can be found at the following resources:

-   [~~official WGCNA tutorials~~](https://labs.genetics.ucla.edu/horvath/htCoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html).
-   [Network analysis with WGCNA](https://bioinformaticsworkbook.org/tutorials/wgcna.html#gsc.tab=0)
-   [WGCNA - RNA-seq](https://alexslemonade.github.io/refinebio-examples/04-advanced-topics/network-analysis_rnaseq_01_wgcna.html)

# Description

The co-expression network is created using the defined correlation function. The determined power is applied to the network (see [Soft-threshold](softthreshold.html) for more info). 
Topological overlap distance is used to create the hierarchical clustering dendrogram. The co-expression modules are determined using the dynamic tree-cut method. For each module, a module eigengene is reported, with its name corresponding to the color of the cluster.

# Output

* Hierarchical clustering heatmap with a dendrogram and automatic cluster assignments. 
* Matrix of module eigengenes that represent a cluster. See [Correlate](correlate.html) for identifying clusters that correlate with clinical/phenotype data.