<ol>
<li>The fold changes for those are huge (on the order of 32x for the largest), so it's unsurprising for the adjusted p-values to be absurdly small.</li>
<li>There's no reason to treat them differently.</li>
</ol>

CC BY-SA 4.0
2020-11-27T15:09:37.247Z
2020-11-27T15:09:37.247Z
Devon Ryan
<p>After I got dds from my count matrix by DESeq(), I use results() method to get Padj and log2 fold changes for volcano plotting,
then I found some gene's Padj values are infinite and the P-value is very close to 0;
this makes those dots on my volcano plotting located very top to Y-aix and also confused me:</p>
<ol>
<li>is this normal or not, such a p-value (very close to 0) and -logPadj is infinite;</li>
<li>how should I treat those data: are they very different expressed between samples and control or just abnormal data which should be taken off?</li>
</ol>
<p>Here follows the table:
<a href="https://i.sstatic.net/Gju8a.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Gju8a.png" alt="enter image description here" /></a></p>
<p>table 1 is dds results, -log10(padj) show inf;
table 2 is count matrix from featureCount
table 3 is size factor of samples</p>
<p>Please help me !</p>

CC BY-SA 4.0
2020-11-27T10:26:48.520Z
It is not normal to get p-value close to zero, especially if you have not a lot of samples. If I look at your table 2, the mean counts don't tally with the genes in table 1... so can you sort this out
StupidWolf
CC BY-SA 4.0
2020-11-27T10:28:48.247Z
there are some instances, for example you knock out or over-express a gene, then of course you find this insanely differentially expressed..
StupidWolf
CC BY-SA 4.0
2020-11-27T13:41:38.907Z
The SE are really low in this case though, rather than huge LFC. Very consistent gene expression?
Greg
CC BY-SA 4.0
2020-11-27T13:41:46.763Z
For log(0) error add an arbitrarily small number to the 0 p.adj values (e.g.10^-99)
Greg
CC BY-SA 4.0
2020-12-04T09:45:51.487Z
@StupidWolf Thank you StupidWolf and Greg for answer my question. I didn't list all the samples out, and one of the compounds has the opposite effect from the others, like: d has a positive effect on empty control, and a b c has a negative effect on empty control; According to the answers, I think this may cause the so much differentially expressed(but i'm not so sure).
Chu
CC BY-SA 4.0
2020-12-04T10:00:44.747Z
what is being tested in this table? Hey our answers are as good as guesses if you just show a table. It's like reading the oracle from bones
StupidWolf
<rna-seq><deseq2>
Question of Padj value (very high, infinite) got from dds
# Question of Padj value (very high, infinite) got from dds

(Asked by: username_0 on 2020-11-27)

After I got dds from my count matrix by DESeq(), I use results() method to get Padj and log2 fold changes for volcano plotting,
then I found some gene's Padj values are infinite and the P-value is very close to 0;
this makes those dots on my volcano plotting located very top to Y-aix and also confused me:

1. is this normal or not, such a p-value (very close to 0) and -logPadj is infinite;
2. how should I treat those data: are they very different expressed between samples and control or just abnormal data which should be taken off?

Here follows the table:
[![enter image description here](https://i.sstatic.net/Gju8a.png)](https://i.sstatic.net/Gju8a.png)

table 1 is dds results, -log10(padj) show inf;
table 2 is count matrix from featureCount
table 3 is size factor of samples

Please help me !

**Tags:** rna-seq, deseq2
> **Comments:**
> **username_1**: It is not normal to get p-value close to zero, especially if you have not a lot of samples. If I look at your table 2, the mean counts don't tally with the genes in table 1... so can you sort this out  
> **username_1**: there are some instances, for example you knock out or over-express a gene, then of course you find this insanely differentially expressed..  
> **username_2**: The SE are really low in this case though, rather than huge LFC. Very consistent gene expression?  
> **username_2**: For log(0) error add an arbitrarily small number to the 0 p.adj values (e.g.10^-99)  
> **username_0**: @StupidWolf Thank you StupidWolf and Greg for answer my question. I didn't list all the samples out, and one of the compounds has the opposite effect from the others, like: d has a positive effect on empty control, and a b c has a negative effect on empty control; According to the answers, I think this may cause the so much differentially expressed(but i'm not so sure).  
> **username_1**: what is being tested in this table? Hey our answers are as good as guesses if you just show a table. It's like reading the oracle from bones  

---

## 1 answers

### Answer by username_3 on 2020-11-27. Score: 1 | Accepted answer

1. The fold changes for those are huge (on the order of 32x for the largest), so it's unsurprising for the adjusted p-values to be absurdly small.
2. There's no reason to treat them differently.


---

