<p>A p-value of 0 is expected in this case. The p-values are calculated by comparing the Wald stat to a standard normal, so you can confirm that this is correct like this:</p>

<pre><code>&gt; pnorm(-42.9203348727)
[1] 0
</code></pre>

<p>It really means p &lt; 2.225074e-308 See <a href="https://support.bioconductor.org/p/64787" rel="nofollow noreferrer">here</a> for a more details</p>

CC BY-SA 3.0
2018-04-18T11:13:43.690Z
Yes i checked the post it has similar issue ,but pardon my ignorance but how come the deseq2 normalised value are so large CB1 CB2 CB3 GD5_1 GD5_2
3327.4890036209 3267.1877800456 3362.7407570783 98.5079578403 97.1475639145 
 yet these are not significant? can you explain me in simpler manner and relate with the biology , you can expand your current answer ..thank you in advance
kcm
CC BY-SA 3.0
2018-04-18T11:34:30.547Z
small p-values are more significant, so a p-values less than 2.3e-308 is very, very significant. It's as significant as you can get
heathobrien
CC BY-SA 3.0
2018-04-18T11:04:21.640Z
2018-04-18T11:04:21.640Z
heathobrien
<p>3 Control replicate and 2 test sample ,so Im comparing my Control which is my Progenitor cells to Monocyte which is mature stage</p>

<p>this is my code im using</p>

<pre><code>condition &lt;- factor(c(rep("Control", 3),rep("Test", 2)),levels=c("Control", "Test"))


(coldata &lt;- data.frame(row.names=colnames(countdata), condition))
dds &lt;- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~condition)
</code></pre>

<p>This is my deseq2 output </p>

<pre><code>gene baseMean log2FoldChange lfcSE  stat pvalue padj CB1 CB2 CB3 GD5_1  GD5_2
ENSG00000144655 2030.6146124999 -5.0812043868   0.1183868766    -42.9203348727  0   0   3327.4890036209 3267.1877800456 3362.7407570783 98.5079578403   97.1475639145
</code></pre>

<p>Im not sure how come the pvalue and  padj is 0? even though the sample value there is a difference and there is also fold change ,something wrong with the result ? </p>

CC BY-SA 3.0
2018-04-18T10:48:59.460Z
What's your sample design? How many biological reps do you have?
heathobrien
CC BY-SA 3.0
2018-04-18T11:00:20.567Z
I have updated it
kcm
<rna-seq><r><deseq2>
Deseq2 output issue
# Deseq2 output issue

(Asked by: username_0 on 2018-04-18)

3 Control replicate and 2 test sample ,so Im comparing my Control which is my Progenitor cells to Monocyte which is mature stage
this is my code im using
condition <- factor(c(rep("Control", 3),rep("Test", 2)),levels=c("Control", "Test"))


(coldata <- data.frame(row.names=colnames(countdata), condition))
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~condition)

This is my deseq2 output 
gene baseMean log2FoldChange lfcSE  stat pvalue padj CB1 CB2 CB3 GD5_1  GD5_2
ENSG00000144655 2030.6146124999 -5.0812043868   0.1183868766    -42.9203348727  0   0   3327.4890036209 3267.1877800456 3362.7407570783 98.5079578403   97.1475639145

Im not sure how come the pvalue and  padj is 0? even though the sample value there is a difference and there is also fold change ,something wrong with the result ?

**Tags:** rna-seq, r, deseq2
> **Comments:**
> **username_1**: What's your sample design? How many biological reps do you have?  
> **username_0**: I have updated it  

---

## 1 answers

### Answer by username_1 on 2018-04-18. Score: 2

A p-value of 0 is expected in this case. The p-values are calculated by comparing the Wald stat to a standard normal, so you can confirm that this is correct like this:
> pnorm(-42.9203348727)
[1] 0

It really means p < 2.225074e-308 See here for a more details

> **Comments:**
> **username_0**: Yes i checked the post it has similar issue ,but pardon my ignorance but how come the deseq2 normalised value are so large CB1 CB2 CB3 GD5_1 GD5_2 3327.4890036209 3267.1877800456 3362.7407570783 98.5079578403 97.1475639145   yet these are not significant? can you explain me in simpler manner and relate with the biology , you can expand your current answer ..thank you in advance  
> **username_1**: small p-values are more significant, so a p-values less than 2.3e-308 is very, very significant. It's as significant as you can get  

---

