Answers.Body: <p>Treat your disease samples as individual groups and then follow the normal routine in limma to use contrasts to compare two group (the control group versus individual disease samples). Note that you need to think long and hard about what these results then mean. Normally we compare groups because then the results should generalize to other samples. That will not be the case when you look at individual disease samples, so I suggest you do some clustering of the samples to see if you perhaps have any coherent clustering of the disease samples into subsets that could then be used as more useful groups in DE testing.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-12T10:01:38.167Z
Answers.Text: Thanks for the help!
Answers.UserDisplayName: Vinay Bharadhwaj
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-11T12:02:10.757Z
Answers.LastActivityDate: 2019-09-11T12:02:10.757Z
Answers.UserDisplayName: Devon Ryan
Body: <p>I am new to R and biocondunctor. I have the normalized expression values for 20 samples for a disease and for 10 controls. I wanted to get the differential expressed values for each sample with all 10 controls.</p>
  
  <p>How could I do that with limma. </p>
  
  <p>Any help will be appreciated.</p>
  
  <p>Edit: What I want to achieve is single-sample t-test between the control population and the single sample values to get DE values</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:16:26.103Z
Comments.Text: You need raw count data, and let limma normalize it (with e.g., voom). Read the manual, it is pretty well written.
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:18:20.750Z
Comments.Text: @benn But I have the normalized data already, I want to get single expression values
Comments.UserDisplayName: Vinay Bharadhwaj
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:33:12.850Z
Comments.Text: Try to get raw counts.
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:34:43.607Z
Comments.Text: @benn Ok , and then, I should normalize with voom, after which how should I get DE values for each sample separately?
Comments.UserDisplayName: Vinay Bharadhwaj
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:41:57.863Z
Comments.Text: Please go over the limma manual (https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf)  and edit your question with the potential problems you will encounter. Be careful to use the right section given the technology used (microarray or RNA-seq).
Comments.UserDisplayName: haci
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:44:32.370Z
Comments.Text: @haci Hey, I have added the microarray tag, I am sorry for the confusion
Comments.UserDisplayName: Vinay Bharadhwaj
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:45:29.557Z
Comments.Text: Okay I assumed it was RNAseq... Just read the manual...
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:50:38.707Z
Comments.Text: @benn I am sorry if I have confused you, I tried to find this in the manual but I could not find any instance of comparing single sample vs a population
Comments.UserDisplayName: Vinay Bharadhwaj
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T09:58:38.047Z
Comments.Text: If you do statistics you compare groups, not separate samples. Separate samples do not have information about variance which is needed for statistics.
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-11T10:02:00.933Z
Comments.Text: @benn But what I am doing is One Sample t Test. It may not have meaning as such, but I wanted to try it out with DE Values
Comments.UserDisplayName: Vinay Bharadhwaj
Tags: <differential-expression><microarray><limma>
Title: How to perform DE analysis for each sample
ThreadText: # How to perform DE analysis for each sample
  
  (Asked by: username_0 on 2019-09-11)
  
  I am new to R and biocondunctor. I have the normalized expression values for 20 samples for a disease and for 10 controls. I wanted to get the differential expressed values for each sample with all 10 controls.
  
  How could I do that with limma.
  
  Any help will be appreciated.
  
  Edit: What I want to achieve is single-sample t-test between the control population and the single sample values to get DE values
  
  **Tags:** differential-expression, microarray, limma
  
  > **Comments:**
  > **username_1**: You need raw count data, and let limma normalize it (with e.g., voom). Read the manual, it is pretty well written.  
  > **username_0**: @username_1 But I have the normalized data already, I want to get single expression values  
  > **username_1**: Try to get raw counts.  
  > **username_0**: @username_1 Ok , and then, I should normalize with voom, after which how should I get DE values for each sample separately?  
  > **username_2**: Please go over the limma manual (https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf)  and edit your question with the potential problems you will encounter. Be careful to use the right section given the technology used (microarray or RNA-seq).  
  > **username_0**: @username_2 Hey, I have added the microarray tag, I am sorry for the confusion  
  > **username_1**: Okay I assumed it was RNAseq... Just read the manual...  
  > **username_0**: @username_1 I am sorry if I have confused you, I tried to find this in the manual but I could not find any instance of comparing single sample vs a population  
  > **username_1**: If you do statistics you compare groups, not separate samples. Separate samples do not have information about variance which is needed for statistics.  
  > **username_0**: @username_1 But what I am doing is One Sample t Test. It may not have meaning as such, but I wanted to try it out with DE Values  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2019-09-11. Score: 1 | Accepted answer
  
  Treat your disease samples as individual groups and then follow the normal routine in limma to use contrasts to compare two group (the control group versus individual disease samples). Note that you need to think long and hard about what these results then mean. Normally we compare groups because then the results should generalize to other samples. That will not be the case when you look at individual disease samples, so I suggest you do some clustering of the samples to see if you perhaps have any coherent clustering of the disease samples into subsets that could then be used as more useful groups in DE testing.
  
  > **Comments:**
  > **username_0**: Thanks for the help!  
  
  ---
  
  