<p>In &quot;normal&quot; (that is replicated) experiments such structures usually are due to poor/insufficient/no prefiltering when there are unreliable count outliers (like two samples have zero counts for a gene and one has a lot).</p>
<p>As you cannot really meaningfully do any prefiltering other than requiring that either one or both samples have a certain read count you should probably just see these &quot;DE results&quot; (it's not differential really as it is unreplicated) as a ranking. Fold changes and p-values are very unreliable at 1 vs 1 so just rank by something like signed p-value and go along with that. Don't overinterpret these sorts of plots which are mostly meaningless in such a setup.</p>
<p>That having said, for prefilter I would probably require that at least one sample has a good number of reads, like 50 to at least dampen unreliably large fold changes a little bit in this setup.</p>

CC BY-SA 4.0
2024-03-21T09:36:05.790Z
Duly noted! 
As for the filter cutoff, how do you choose the number? Is there someway of determining this or does this come with experience and intuition? Also if I have multiple comparisons with the same cell type (just different culturing conditions) would you keep this cutoff constant or does it depended on the data?
Timi
CC BY-SA 4.0
2024-03-20T22:42:08.677Z
2024-03-20T22:42:08.677Z
ATpoint
<p>Might as well transfer my reddit answer here...</p>
<p>If your p-value is smaller than the reciprocal of the number of atoms in the universe, the model used for calculating that differential expression is almost certainly wrong.</p>
<p>Do an MA plot; that will give you a better idea of what's going on.</p>
<ul>
<li>X axis - mean of (log expression for all samples); should be in the range of 0~15</li>
<li>Y axis - log2 fold change</li>
<li>colour - p-value (linear scale)</li>
</ul>
<p>I suspect that the curves you're seeing are likely to be low count genes, in other words, genes on the left hand side of the graph in an MA plot.</p>
<p>I also suspect, given the super-astronomical p-values, that you've got an offset L2FC (i.e. the &quot;spine&quot; of the MA plot doesn't sit at L2FC = 0), suggesting that a covariate hasn't been appropriately accounted for. This is common when processing data from multiple samples from multiple different methods.</p>

CC BY-SA 4.0
2024-03-21T12:45:59.997Z
2024-03-21T12:45:59.997Z
gringer
<p><em>This question was also asked on <a href="https://www.reddit.com/r/bioinformatics/comments/1bjil16/genes_aligned_on_curve_in_volcano_plots_any_idea/" rel="nofollow noreferrer">reddit</a></em></p>
<p><a href="https://i.sstatic.net/BK9SI.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BK9SI.png" alt="Volcano plot of differentially expressed genes" /></a></p>
<p>Does anyone know how to correct the abnormal genes that seemed to be located on the curves near the extremities?
The analysis was done using R package edgeR on samples measured once. However, we couldn't figure out what was causing the anomaly or how to fix it.
Does anyone have any advice on this matter?
The code used for analysis is from the following website:  <a href="https://blog.csdn.net/u012110870/article/details/102804557" rel="nofollow noreferrer">https://blog.csdn.net/u012110870/article/details/102804557</a></p>

CC BY-SA 4.0
2024-03-20T16:35:59.773Z
On samples measured once as in no replicates?
Ram RS
CC BY-SA 4.0
2024-03-20T17:12:33.463Z
@RamRS Yes. I know that this makes the data unreliable, but long story short, I am trying to make the best of this situation.
Timi
<r><rna-seq>
Abnormal representation of differentially expressed genes in volcano plots located at the extremities
# Abnormal representation of differentially expressed genes in volcano plots located at the extremities

(Asked by: username_0 on 2024-03-20)

*This question was also asked on [reddit](https://www.reddit.com/r/bioinformatics/comments/1bjil16/genes_aligned_on_curve_in_volcano_plots_any_idea/)*

[![Volcano plot of differentially expressed genes](https://i.sstatic.net/BK9SI.png)](https://i.sstatic.net/BK9SI.png)

Does anyone know how to correct the abnormal genes that seemed to be located on the curves near the extremities?
The analysis was done using R package edgeR on samples measured once. However, we couldn't figure out what was causing the anomaly or how to fix it.
Does anyone have any advice on this matter?
The code used for analysis is from the following website: <https://blog.csdn.net/u012110870/article/details/102804557>

**Tags:** r, rna-seq
> **Comments:**
> **username_1**: On samples measured once as in no replicates?  
> **username_0**: @RamRS Yes. I know that this makes the data unreliable, but long story short, I am trying to make the best of this situation.  

---

## 2 answers

### Answer by username_2 on 2024-03-20. Score: 1

In "normal" (that is replicated) experiments such structures usually are due to poor/insufficient/no prefiltering when there are unreliable count outliers (like two samples have zero counts for a gene and one has a lot).

As you cannot really meaningfully do any prefiltering other than requiring that either one or both samples have a certain read count you should probably just see these "DE results" (it's not differential really as it is unreplicated) as a ranking. Fold changes and p-values are very unreliable at 1 vs 1 so just rank by something like signed p-value and go along with that. Don't overinterpret these sorts of plots which are mostly meaningless in such a setup.

That having said, for prefilter I would probably require that at least one sample has a good number of reads, like 50 to at least dampen unreliably large fold changes a little bit in this setup.

> **Comments:**
> **username_0**: Duly noted!  As for the filter cutoff, how do you choose the number? Is there someway of determining this or does this come with experience and intuition? Also if I have multiple comparisons with the same cell type (just different culturing conditions) would you keep this cutoff constant or does it depended on the data?  

---

### Answer by username_3 on 2024-03-21. Score: 0

Might as well transfer my reddit answer here...

If your p-value is smaller than the reciprocal of the number of atoms in the universe, the model used for calculating that differential expression is almost certainly wrong.

Do an MA plot; that will give you a better idea of what's going on.

* X axis - mean of (log expression for all samples); should be in the range of 0~15
* Y axis - log2 fold change
* colour - p-value (linear scale)

I suspect that the curves you're seeing are likely to be low count genes, in other words, genes on the left hand side of the graph in an MA plot.

I also suspect, given the super-astronomical p-values, that you've got an offset L2FC (i.e. the "spine" of the MA plot doesn't sit at L2FC = 0), suggesting that a covariate hasn't been appropriately accounted for. This is common when processing data from multiple samples from multiple different methods.


---

