Body: <p>I have a dataset of RNA-seq samples for testing different drugs on the presence of another drug. One of my samples is the normal cells with no drugs (control negative) and another is the cells with just the base drug (control positive). All of the other samples are cells with the base drug and another testing drug. So, my question is how to do design a proper Design Matrix for comparing these samples. I can separately compare with control negative and control positive. But, my intention is to somehow neutralize the effect of one of the controls in all the samples and compare to the other one. Is that possible? How should I design the matrix?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-02T17:18:23.667Z
Comments.Text: Literally one undrugged control?
Comments.UserDisplayName: swbarnes2
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-02T17:29:45.460Z
Comments.Text: Yes, Literally! Unfortunately I don't have biological replicates for any of these samples.
Comments.UserDisplayName: Reza Rezaei
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-02T17:37:19.710Z
Comments.Text: That's unfortunate.  I guess you can do some ratios in excel, but you can't do anything sophisticated without replicates.  And your conclusions will be really shaky.  Wet lab people are going to have to test your conclusions with better experiments.
Comments.UserDisplayName: swbarnes2
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-03T09:09:57.823Z
Comments.Text: I know that. But the whole experiment's purpose was to generate some hypotheses for further testing with replicates. So, I guess it can give me some insights!
Comments.UserDisplayName: Reza Rezaei
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-03T16:02:20.253Z
Comments.Text: Or it might give who a whole lot of false positives that waste time and money.  Everything  hinges on your one replicate being perfectly representative of what the control should look like.
Comments.UserDisplayName: swbarnes2
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-03T18:00:33.713Z
Comments.Text: I totally agree and discussed this issue a lot in the lab. But people still come to me with their no replicate datasets and I have to give them some results or insight! What can I do?
Comments.UserDisplayName: Reza Rezaei
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-04T10:07:32.260Z
Comments.Text: Give them a big 'told you so' 2 years down the line :-)
Comments.UserDisplayName: Pallie
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-04T12:57:44.437Z
Comments.Text: Are there any technical replicates? Could you share a table or list of the amount of samples and their conditions?
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-05T04:48:23.467Z
Comments.Text: Condition is as I described. One sample is Mock, another sample is Drug1 and all the other samples are Drug1 + Drug2-7. Deug2-7 means 6 different drugs which are being combined with drug1 separately to see their simultanous effect. What do you mean by the amount of samples? Like number of Reads?
Comments.UserDisplayName: Reza Rezaei
Tags: <rna-seq><bioconductor><differential-expression><deseq2><edger>
Title: a proper Design Matrix for several drug treatments with both control negative and control positive
ThreadText: # a proper Design Matrix for several drug treatments with both control negative and control positive
  
  (Asked by: username_0 on 2020-08-02)
  
  I have a dataset of RNA-seq samples for testing different drugs on the presence of another drug. One of my samples is the normal cells with no drugs (control negative) and another is the cells with just the base drug (control positive). All of the other samples are cells with the base drug and another testing drug. So, my question is how to do design a proper Design Matrix for comparing these samples. I can separately compare with control negative and control positive. But, my intention is to somehow neutralize the effect of one of the controls in all the samples and compare to the other one. Is that possible? How should I design the matrix?
  
  **Tags:** rna-seq, bioconductor, differential-expression, deseq2, edger
  
  > **Comments:**
  > **username_1**: Literally one undrugged control?  
  > **username_0**: Yes, Literally! Unfortunately I don't have biological replicates for any of these samples.  
  > **username_1**: That's unfortunate.  I guess you can do some ratios in excel, but you can't do anything sophisticated without replicates.  And your conclusions will be really shaky.  Wet lab people are going to have to test your conclusions with better experiments.  
  > **username_0**: I know that. But the whole experiment's purpose was to generate some hypotheses for further testing with replicates. So, I guess it can give me some insights!  
  > **username_1**: Or it might give who a whole lot of false positives that waste time and money.  Everything  hinges on your one replicate being perfectly representative of what the control should look like.  
  > **username_0**: I totally agree and discussed this issue a lot in the lab. But people still come to me with their no replicate datasets and I have to give them some results or insight! What can I do?  
  > **username_2**: Give them a big 'told you so' 2 years down the line :-)  
  > **username_3**: Are there any technical replicates? Could you share a table or list of the amount of samples and their conditions?  
  > **username_0**: Condition is as I described. One sample is Mock, another sample is Drug1 and all the other samples are Drug1 + Drug2-7. Deug2-7 means 6 different drugs which are being combined with drug1 separately to see their simultanous effect. What do you mean by the amount of samples? Like number of Reads?  
  
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  ## 0 answers
  
  