Answers.Body: <ol>
  <li>A wilcoxon test is what you're looking for.</li>
  <li>Yes, since there will inevitably be some small batch effects to compensate for.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-10-31T13:43:22.703Z
Answers.Text: Thank you! Could you give me tips about normalization because of batch effects?
  Would you do a PCA as well to see which variables have the biggest effect on gene expression?
Answers.UserDisplayName: vhio
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-10-31T21:17:32.273Z
Answers.Text: In qPCR the assumption is that there’s just a scaling difference between batches. So normally you load the same sample on multiple batches and scale according to that.
Answers.UserDisplayName: Devon Ryan
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-10-29T05:38:26.430Z
Answers.LastActivityDate: 2021-10-29T05:38:26.430Z
Answers.UserDisplayName: Devon Ryan
Body: <p>I have &gt;100 qPCR experiments that I'd like to analyze together, each containing the same set of genes (10 genes of interest and 2 reference genes).
  I have four different samples (untreated &amp; 3 treatments) from the same cell culture. Our setup allows two of them to be run at once, i.e. the samples from the same cell culture are split to two qPCR runs.</p>
  <ol>
  <li><p>I would like to do statistics with the dCq values after normalizing to the reference genes, but the dataset doesn't pass any normality tests.
  What methods are suitable for finding valid differences between treatments (or any other features like age)?</p>
  </li>
  <li><p>If the same amount of cDNA was used and reference genes were added to every run, is it still necessary to normalize between runs or between cell cultures?</p>
  </li>
  </ol>
  
Tags: <r><normalization><qpcr>
Title: What statistics can I use on qPCR data composed of several runs if all normality tests fail on the dataset?
ThreadText: # What statistics can I use on qPCR data composed of several runs if all normality tests fail on the dataset?
  
  (Asked by: username_0 on 2021-10-28)
  
  I have >100 qPCR experiments that I'd like to analyze together, each containing the same set of genes (10 genes of interest and 2 reference genes).
  I have four different samples (untreated & 3 treatments) from the same cell culture. Our setup allows two of them to be run at once, i.e. the samples from the same cell culture are split to two qPCR runs.
  
  1. I would like to do statistics with the dCq values after normalizing to the reference genes, but the dataset doesn't pass any normality tests.
     What methods are suitable for finding valid differences between treatments (or any other features like age)?
  2. If the same amount of cDNA was used and reference genes were added to every run, is it still necessary to normalize between runs or between cell cultures?
  
  **Tags:** r, normalization, qpcr
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-10-29. Score: 1
  
  1. A wilcoxon test is what you're looking for.
  2. Yes, since there will inevitably be some small batch effects to compensate for.
  
  > **Comments:**
  > **username_0**: Thank you! Could you give me tips about normalization because of batch effects? Would you do a PCA as well to see which variables have the biggest effect on gene expression?  
  > **username_1**: In qPCR the assumption is that there’s just a scaling difference between batches. So normally you load the same sample on multiple batches and scale according to that.  
  
  ---
  
  