<p>While you can use networks to find differentially expressed genes (see the <a href="https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/" rel="nofollow noreferrer">WGCNA</a> package, which does this) in my experience this ends up largely matching what you'd get using a traditional package with a looser threshold for significance. Given the time savings of traditional packages, there's rarely any gain to using networks (it won't hurt to try, just note that it'll take some time). If some very interesting genes are just above your significance threshold then change your threshold. You need to validate your findings in some way anyway, so your p-value threshold is partly just a way to protect you from wasting your time (but if background knowledge suggests that your time wouldn't be wasted...).</p>

CC BY-SA 3.0
2017-08-16T06:41:26.923Z
2017-08-16T06:41:26.923Z
Devon Ryan
<p>Co-expression network will give you an idea about the genes having similar expression patterns and the nodes will be decided on the basis of correlation scores and nothing much you will get from differential gene expression perspective.</p>

<p>I would suggest you to try some other models for differential expression analysis like <a href="http://bioconductor.org/packages/release/bioc/html/baySeq.html" rel="nofollow noreferrer">baySeq</a> , <a href="http://bioconductor.org/packages/release/bioc/html/DESeq2.html" rel="nofollow noreferrer">DESeq</a>, <a href="https://bioconductor.org/packages/release/bioc/html/edgeR.html" rel="nofollow noreferrer">edgeR</a>, <a href="https://www.bioconductor.org/packages/release/bioc/html/NOISeq.html" rel="nofollow noreferrer">NOIseq</a>. Depending upon the model you use for analysis you might get some extra significant DEG. Also, try different significance cutoff and see how it's changing the analysis result to get the threshold that suits the data best.</p>

CC BY-SA 3.0
2017-08-16T08:27:56.623Z
2017-08-16T08:27:56.623Z
arup
<p>In my RNAseq dataset of differentiated stem-cell lines, some samples have far fewer significantly differentially expressed genes than others. QC shows that this is because there are way fewer reads for one sample than the others.</p>

<p>Can gene co-expression networks be used to infer differential expression? For example, if certain genes just miss the cutoff q-value cutoff  If so, what papers have done so? </p>

CC BY-SA 3.0
2017-08-16T08:28:34.377Z
Are you using Tuxedo protocol for analysis?
arup
CC BY-SA 3.0
2017-08-16T09:27:58.120Z
How many samples do you have? It seems strange that because a single sample has fewer reads there are less DEG. Did you check for outliers? What is your normalization process?
llrs
<rna-seq><differential-expression><networks>
Can gene co-expression networks be used to help identify differentially expressed genes?
# Can gene co-expression networks be used to help identify differentially expressed genes?

(Asked by: username_0 on 2017-08-16)

In my RNAseq dataset of differentiated stem-cell lines, some samples have far fewer significantly differentially expressed genes than others. QC shows that this is because there are way fewer reads for one sample than the others.

Can gene co-expression networks be used to infer differential expression? For example, if certain genes just miss the cutoff q-value cutoff If so, what papers have done so?

**Tags:** rna-seq, differential-expression, networks
> **Comments:**
> **username_1**: Are you using Tuxedo protocol for analysis?  
> **username_2**: How many samples do you have? It seems strange that because a single sample has fewer reads there are less DEG. Did you check for outliers? What is your normalization process?  

---

## 2 answers

### Answer by username_3 on 2017-08-16. Score: 2 | Accepted answer

While you can use networks to find differentially expressed genes (see the [WGCNA](https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/) package, which does this) in my experience this ends up largely matching what you'd get using a traditional package with a looser threshold for significance. Given the time savings of traditional packages, there's rarely any gain to using networks (it won't hurt to try, just note that it'll take some time). If some very interesting genes are just above your significance threshold then change your threshold. You need to validate your findings in some way anyway, so your p-value threshold is partly just a way to protect you from wasting your time (but if background knowledge suggests that your time wouldn't be wasted...).


---

### Answer by username_1 on 2017-08-16. Score: 2

Co-expression network will give you an idea about the genes having similar expression patterns and the nodes will be decided on the basis of correlation scores and nothing much you will get from differential gene expression perspective.

I would suggest you to try some other models for differential expression analysis like [baySeq](http://bioconductor.org/packages/release/bioc/html/baySeq.html) , [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), [NOIseq](https://www.bioconductor.org/packages/release/bioc/html/NOISeq.html). Depending upon the model you use for analysis you might get some extra significant DEG. Also, try different significance cutoff and see how it's changing the analysis result to get the threshold that suits the data best.


---

