Answers.Body: <p>The biggest difference a large number of samples makes is that you can usually decrease the soft thresholding power. If you use power 6 (or 12 for a signed network), try decreasing it to 3 or even 2 (6 or 4 for signed network). Apart from that, it could also be that the co-expression modules in your data are simply smaller than what people usually find, nothing apriori wrong with that.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-11-18T18:22:11.120Z
Answers.Text: Okay, thank you. I am already using 2 as a threshold. I am afraid there may be  confounding factors between the samples that are making it difficult for detection of modules. Are there confounding factors that I should be looking at removing that are known to affect module detection?
Answers.UserDisplayName: Habesha
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Answers.CreationDate: 2020-11-30T07:37:13.280Z
Answers.Text: I cannot make a definite statement, it depends on the minutiae of the data. You could certainly try some noise filtering approaches although I have yet to see a bulk (not single cell) sequencing data set where these help.
Answers.UserDisplayName: Peter Langfelder
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Answers.CreationDate: 2020-11-13T22:10:19.383Z
Answers.LastActivityDate: 2020-11-13T22:10:19.383Z
Answers.UserDisplayName: Peter Langfelder
Body: <p>We are working with a lot of samples, more than 50,000 people! We are using WGCNA to get modules and it seems like there are less number of module genes the more people we use. We are following the tutorial guidelines. We set the min modulesize = 30 and deepSplit = 4 (max sensitivity) However, we are getting very small modules with numbers almost 30 for all. We want to know if using this many samples creates an artifact and the variation between the samples may reduce the correlation seen between the genes? If that is the case, do you suggest any way we can fix that? Also do you think z-scaling expression data improves the module detection?</p>
  <p>Thank you so much.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-13T20:43:07.650Z
Comments.Text: Weighted correlation network analysis would require a clear understanding of the a priori weightings in my opinion. The background is here
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-18T18:22:57.030Z
Comments.Text: Where can access this background on priori weightings you suggested? Thanks!
Comments.UserDisplayName: Habesha
Tags: <wgcna>
Title: Does module size decrease as the number of samples increase in WGCNA?
ThreadText: # Does module size decrease as the number of samples increase in WGCNA?
  
  (Asked by: username_0 on 2020-11-13)
  
  We are working with a lot of samples, more than 50,000 people! We are using WGCNA to get modules and it seems like there are less number of module genes the more people we use. We are following the tutorial guidelines. We set the min modulesize = 30 and deepSplit = 4 (max sensitivity) However, we are getting very small modules with numbers almost 30 for all. We want to know if using this many samples creates an artifact and the variation between the samples may reduce the correlation seen between the genes? If that is the case, do you suggest any way we can fix that? Also do you think z-scaling expression data improves the module detection?
  
  Thank you so much.
  
  **Tags:** wgcna
  
  > **Comments:**
  > **username_1**: Weighted correlation network analysis would require a clear understanding of the a priori weightings in my opinion. The background is here  
  > **username_0**: Where can access this background on priori weightings you suggested? Thanks!  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2020-11-13. Score: 1 | Accepted answer
  
  The biggest difference a large number of samples makes is that you can usually decrease the soft thresholding power. If you use power 6 (or 12 for a signed network), try decreasing it to 3 or even 2 (6 or 4 for signed network). Apart from that, it could also be that the co-expression modules in your data are simply smaller than what people usually find, nothing apriori wrong with that.
  
  > **Comments:**
  > **username_0**: Okay, thank you. I am already using 2 as a threshold. I am afraid there may be  confounding factors between the samples that are making it difficult for detection of modules. Are there confounding factors that I should be looking at removing that are known to affect module detection?  
  > **username_2**: I cannot make a definite statement, it depends on the minutiae of the data. You could certainly try some noise filtering approaches although I have yet to see a bulk (not single cell) sequencing data set where these help.  
  
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