text: user6192: 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
  
  user77: 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.
  
original_text: 6192: <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>
   77: <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>
  