<p>This is an incomplete answer since it's not my field of expertise, but it sounds like Weighted correlation network analysis is what you are referring to.</p>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559" rel="nofollow noreferrer">There is a paper</a> which describes how it can be applied to gene expression data.</p>
<p>Langfelder, P., Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008). <a href="https://doi.org/10.1186/1471-2105-9-559" rel="nofollow noreferrer">https://doi.org/10.1186/1471-2105-9-559</a></p>

CC BY-SA 4.0
2022-01-20T10:36:40.573Z
2022-01-20T10:36:40.573Z
user438383
<p>There are many different kinds of &quot;gene regulatory networks&quot;, so how to model them with gene expression data depends on what you're trying to model and what type of regulatory mechanisms you want to study.</p>
<p>Pick a random gene in the human genome and use a resource database like <a href="https://stringdb.org" rel="nofollow noreferrer">StringDB</a>.
Let's take the <a href="https://string-db.org/cgi/network?taskId=bfdm466sjcp0&amp;sessionId=bEa7ENjHHZ2u" rel="nofollow noreferrer">TET1</a> protein for example.
Some edges in that graph represent co-expression that you'd be able to find from protein expression data (e.g. NANOG, DNMT3B, and more are linked to TET1 this way).
This type of graph tries to model how different proteins interact with each other inside a cell.</p>
<p>Alternatively, you can look at 3D chromatin interactions from Hi-C data, for example.
Nodes in that graph could be regulatory elements like enhancers and promoters, and the edges could be how frequently these elements interact with each other.
This type of graph tries to model how DNA elements interact with each other to control expression of nearby genes.
This is a different purpose to the one above, so it needs to be modelled and interpreted differently.</p>
<p>Each of these are their own type of &quot;gene regulatory network&quot; which require their own methods for constructing adjacency matrices.
For example, you could construct an adjacency matrix by looking at the correlation of expression for each pair of proteins (or transcripts, or genes) across a large set of samples.
Or in the promoter-enhancer interaction case, a Hi-C contact matrix is itself a type of adjacency matrix.</p>
<p>Different graph models (and thus different adjacency matrices) will be useful for different purposes.
So it might be worth trying to refine what you mean by a &quot;gene regulatory network&quot;, what aspect of biology you're trying to study using this network, and how different types of gene expression data may be useful to construct these networks.</p>
<p><a href="https://www.hsph.harvard.edu/profile/john-quackenbush/" rel="nofollow noreferrer">John Quackenbush</a>'s and <a href="https://califano.c2b2.columbia.edu/" rel="nofollow noreferrer">Andrea Califano</a>'s labs have produced a lot of papers modelling different types of gene regulatory networks, like <a href="https://netzoo.github.io/" rel="nofollow noreferrer">The Network Zoo</a> and <a href="https://califano.c2b2.columbia.edu/aracne" rel="nofollow noreferrer">ARACNe</a>.
These mostly use gene expression data from RNA-seq measurements, but you can read up on some of these to get some ideas on how these types of models may work.</p>

CC BY-SA 4.0
2022-01-20T19:55:58.240Z
2022-01-20T19:55:58.240Z
James Hawley
<p>I am trying to find out how one can using gene expression data can infer gene regulatory network applying graph theory concepts. But I could not find a proper reference that</p>
<ol>
<li><p>explain how one can get the adjacency matrix for a gene regulatory network? like what are nodes what are the edges? which kinds of parameters one should infer?</p>
</li>
<li><p>how one can use gene expression data for this modeling?</p>
</li>
</ol>
<p>Any reference or help regarding these questions is highly appreciated if even it is going to be a keyword.</p>

CC BY-SA 4.0
2022-01-22T00:06:53.050Z
Hi @Raz, this does not seem like a specific question, and needs more information to be properly answered. Could you please add in information about the specific situation or problem that you are trying to solve?
gringer
<genomics><gene-expression>
what are the nodes and edges values in gene regulatory networks?
# what are the nodes and edges values in gene regulatory networks?

(Asked by: username_0 on 2022-01-20)

I am trying to find out how one can using gene expression data can infer gene regulatory network applying graph theory concepts. But I could not find a proper reference that

1. explain how one can get the adjacency matrix for a gene regulatory network? like what are nodes what are the edges? which kinds of parameters one should infer?
2. how one can use gene expression data for this modeling?

Any reference or help regarding these questions is highly appreciated if even it is going to be a keyword.

**Tags:** genomics, gene-expression
> **Comments:**
> **username_1**: Hi @Raz, this does not seem like a specific question, and needs more information to be properly answered. Could you please add in information about the specific situation or problem that you are trying to solve?  

---

## 2 answers

### Answer by username_2 on 2022-01-20. Score: 3

This is an incomplete answer since it's not my field of expertise, but it sounds like Weighted correlation network analysis is what you are referring to.

[There is a paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559) which describes how it can be applied to gene expression data.

Langfelder, P., Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008). <https://doi.org/10.1186/1471-2105-9-559>


---

### Answer by username_3 on 2022-01-20. Score: 1

There are many different kinds of "gene regulatory networks", so how to model them with gene expression data depends on what you're trying to model and what type of regulatory mechanisms you want to study.

Pick a random gene in the human genome and use a resource database like [StringDB](https://stringdb.org).
Let's take the [TET1](https://string-db.org/cgi/network?taskId=bfdm466sjcp0&sessionId=bEa7ENjHHZ2u) protein for example.
Some edges in that graph represent co-expression that you'd be able to find from protein expression data (e.g. NANOG, DNMT3B, and more are linked to TET1 this way).
This type of graph tries to model how different proteins interact with each other inside a cell.

Alternatively, you can look at 3D chromatin interactions from Hi-C data, for example.
Nodes in that graph could be regulatory elements like enhancers and promoters, and the edges could be how frequently these elements interact with each other.
This type of graph tries to model how DNA elements interact with each other to control expression of nearby genes.
This is a different purpose to the one above, so it needs to be modelled and interpreted differently.

Each of these are their own type of "gene regulatory network" which require their own methods for constructing adjacency matrices.
For example, you could construct an adjacency matrix by looking at the correlation of expression for each pair of proteins (or transcripts, or genes) across a large set of samples.
Or in the promoter-enhancer interaction case, a Hi-C contact matrix is itself a type of adjacency matrix.

Different graph models (and thus different adjacency matrices) will be useful for different purposes.
So it might be worth trying to refine what you mean by a "gene regulatory network", what aspect of biology you're trying to study using this network, and how different types of gene expression data may be useful to construct these networks.

[John Quackenbush](https://www.hsph.harvard.edu/profile/john-quackenbush/)'s and [Andrea Califano](https://califano.c2b2.columbia.edu/)'s labs have produced a lot of papers modelling different types of gene regulatory networks, like [The Network Zoo](https://netzoo.github.io/) and [ARACNe](https://califano.c2b2.columbia.edu/aracne).
These mostly use gene expression data from RNA-seq measurements, but you can read up on some of these to get some ideas on how these types of models may work.


---

