<p>I think you are over thinking the problem. You need to classify the genes of interest and to do that a classifier such as eggNOG is used. This will remove any bias. I understand eggNOG because it's based on protein phylogenetics and phylogenetics is what I do and I can assure you its very solid and the algorithm nicely constructed.</p>
<p>DESeq2 (I ain't a huge fan BTW, but avoid controversy) will happily accept eggNOG classification, <a href="https://microscope.readthedocs.io/en/3.14.3/content/transcriptomic/NGSDiffExprAnalysis.html" rel="nofollow noreferrer">here</a></p>
<p>Then its simply three tests</p>
<ol>
<li>S1 vs S2</li>
<li>S1 vs hybrid</li>
<li>S2 vs hybrid</li>
</ol>
<p>I personally would perform unsupervised learning, particularly via <a href="https://scanpy.readthedocs.io/en/stable/" rel="nofollow noreferrer">Scanpy</a> first on S1, S2 and hybrid separately. This will give a rough indication of the key difference of expression profiles between S1 and S2 and see where the hydrid occurs.</p>

CC BY-SA 4.0
2024-03-12T23:39:10.087Z
2024-03-12T23:39:10.087Z
M__
<p>I am struggling to get my head around the correct design to use for my experiment.
I have RNAseq of 2 pure species (s1, s2) and of the hybrid (h). I aligned all three genotypes to the s1 genomes. I am interested in the comparison of hybrids vs s1 (maternal genome). But I want to remove a potential bias from reads from s2 chromosome being harder to align to s1. (The hybrid genome is composed of both s1 and s2.) I would like to correct my DE test by removing potential effect s2 vs s1. (I aligned s2 to s1 and got the count.)
But my issue is, I fail to have a full rank design.
I do not have any strong feelings about the library (deseq2, edgeR...).</p>
<p>Following the tutorial</p>
<p>below is the condition matrix, as you can see if I want to make the contrast ~ s2 + condition. The matrix is not full rank.
I believe I could write my design as ~s2 + h - s1 ?? But I don't know how to use deseq2 for this approach.</p>
<p>Any approach/tips to achieve my goal here? Is it event possible? what</p>
<div class="s-table-container"><table class="s-table">
<thead>
<tr>
<th>sample</th>
<th>condition</th>
<th>s2</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>s1</td>
<td>0</td>
</tr>
<tr>
<td>2</td>
<td>s1</td>
<td>0</td>
</tr>
<tr>
<td>3</td>
<td>s1</td>
<td>0</td>
</tr>
<tr>
<td>4</td>
<td>s2</td>
<td>1</td>
</tr>
<tr>
<td>5</td>
<td>s2</td>
<td>1</td>
</tr>
<tr>
<td>6</td>
<td>s2</td>
<td>1</td>
</tr>
<tr>
<td>7</td>
<td>h</td>
<td>0</td>
</tr>
<tr>
<td>8</td>
<td>h</td>
<td>0</td>
</tr>
<tr>
<td>9</td>
<td>h</td>
<td>0</td>
</tr>
</tbody>
</table></div>
<p>any help is warmly appreciated!</p>

<rna-seq><deseq2><edger><design>
rna seq matrix design hybrids
# rna seq matrix design hybrids

(Asked by: username_0 on 2024-03-12)

I am struggling to get my head around the correct design to use for my experiment.
I have RNAseq of 2 pure species (s1, s2) and of the hybrid (h). I aligned all three genotypes to the s1 genomes. I am interested in the comparison of hybrids vs s1 (maternal genome). But I want to remove a potential bias from reads from s2 chromosome being harder to align to s1. (The hybrid genome is composed of both s1 and s2.) I would like to correct my DE test by removing potential effect s2 vs s1. (I aligned s2 to s1 and got the count.)
But my issue is, I fail to have a full rank design.
I do not have any strong feelings about the library (deseq2, edgeR...).

Following the tutorial

below is the condition matrix, as you can see if I want to make the contrast ~ s2 + condition. The matrix is not full rank.
I believe I could write my design as ~s2 + h - s1 ?? But I don't know how to use deseq2 for this approach.

Any approach/tips to achieve my goal here? Is it event possible? what

| sample | condition | s2 |
| --- | --- | --- |
| 1 | s1 | 0 |
| 2 | s1 | 0 |
| 3 | s1 | 0 |
| 4 | s2 | 1 |
| 5 | s2 | 1 |
| 6 | s2 | 1 |
| 7 | h | 0 |
| 8 | h | 0 |
| 9 | h | 0 |

any help is warmly appreciated!

**Tags:** rna-seq, deseq2, edger, design

---

## 1 answers

### Answer by username_1 on 2024-03-12. Score: 1 | Accepted answer

I think you are over thinking the problem. You need to classify the genes of interest and to do that a classifier such as eggNOG is used. This will remove any bias. I understand eggNOG because it's based on protein phylogenetics and phylogenetics is what I do and I can assure you its very solid and the algorithm nicely constructed.

DESeq2 (I ain't a huge fan BTW, but avoid controversy) will happily accept eggNOG classification, [here](https://microscope.readthedocs.io/en/3.14.3/content/transcriptomic/NGSDiffExprAnalysis.html)

Then its simply three tests

1. S1 vs S2
2. S1 vs hybrid
3. S2 vs hybrid

I personally would perform unsupervised learning, particularly via [Scanpy](https://scanpy.readthedocs.io/en/stable/) first on S1, S2 and hybrid separately. This will give a rough indication of the key difference of expression profiles between S1 and S2 and see where the hydrid occurs.


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