Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-04-18T11:13:43.690Z
Answers.Text: 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
Answers.UserDisplayName: kcm
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-04-18T11:34:30.547Z
Answers.Text: 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
Answers.UserDisplayName: heathobrien
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-04-18T11:04:21.640Z
Answers.LastActivityDate: 2018-04-18T11:04:21.640Z
Answers.UserDisplayName: heathobrien
Body: <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>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-18T10:48:59.460Z
Comments.Text: What's your sample design? How many biological reps do you have?
Comments.UserDisplayName: heathobrien
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-18T11:00:20.567Z
Comments.Text: I have updated it
Comments.UserDisplayName: kcm
Tags: <rna-seq><r><deseq2>
Title: Deseq2 output issue
ThreadText: # 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](https://support.bioconductor.org/p/64787) 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  
  
  ---
  
  