<p>I need to identify differential genes across 8 groups. I know this can be done using DESeq2 or EdgeR as these are better suited and equipped.</p>

<p>However, I was thinking if something like this piece of code could be tweaked. Here it works for two groups each with 3 replicates and generates a heatmap. The code is in R </p>

<pre><code>pvals &lt;- apply(dm, 1, function(x) wilcox.test(x~c(rep(0,3), rep(1,9)))$p.value)
heatmap(dm[pvals&lt;0.05,])
</code></pre>

<p>Thank you for your inputs and suggestions.</p>

CC BY-SA 4.0
2019-08-14T15:11:36.677Z
What is your reason not to use DESeq2 or EdgeR? Their models will work much better for RNAseq data than a simple Wilcoxon test.
PPK
CC BY-SA 4.0
2019-08-14T20:06:15.817Z
Welcome to Bioinformatics.SE! At the moment, your question simply asks "what about this?" while ignoring a tremendous amount of scholarship on differential expression analysis. Unless you can more clearly present a specific question or concern, there is a good chance this question will be closed as too broad or unclear.
Daniel Standage
CC BY-SA 4.0
2019-08-16T15:26:36.487Z
For a single measurement of some fixed, observable quantity like height or grades, using a Wilcoxon test isn't a bad idea. But RNA-seq is a very complicated system and can't simply be analyzed by something like a Wilcoxon test. Your results will be full of mistakes if you do this. I would recommend that you read some introduction to RNA-seq papers or textbooks and watch some presentations by Wolfgang Huber, Gordon Smyth, and Lior Pachter to see why this is the case
James Hawley
<r><rna-seq><differential-expression>
Identification of differential genes across 8 groups
# Identification of differential genes across 8 groups

(Asked by: username_0 on 2019-08-14)

I need to identify differential genes across 8 groups. I know this can be done using DESeq2 or EdgeR as these are better suited and equipped.
However, I was thinking if something like this piece of code could be tweaked. Here it works for two groups each with 3 replicates and generates a heatmap. The code is in R 
pvals <- apply(dm, 1, function(x) wilcox.test(x~c(rep(0,3), rep(1,9)))$p.value)
heatmap(dm[pvals<0.05,])

Thank you for your inputs and suggestions.

**Tags:** r, rna-seq, differential-expression
> **Comments:**
> **username_1**: What is your reason not to use DESeq2 or EdgeR? Their models will work much better for RNAseq data than a simple Wilcoxon test.  
> **username_2**: Welcome to Bioinformatics.SE! At the moment, your question simply asks "what about this?" while ignoring a tremendous amount of scholarship on differential expression analysis. Unless you can more clearly present a specific question or concern, there is a good chance this question will be closed as too broad or unclear.  
> **username_3**: For a single measurement of some fixed, observable quantity like height or grades, using a Wilcoxon test isn't a bad idea. But RNA-seq is a very complicated system and can't simply be analyzed by something like a Wilcoxon test. Your results will be full of mistakes if you do this. I would recommend that you read some introduction to RNA-seq papers or textbooks and watch some presentations by Wolfgang Huber, Gordon Smyth, and Lior Pachter to see why this is the case  

---

## 0 answers

