<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>

CC BY-SA 4.0
2019-09-12T10:01:38.167Z
Thanks for the help!
Vinay Bharadhwaj
CC BY-SA 4.0
2019-09-11T12:02:10.757Z
2019-09-11T12:02:10.757Z
Devon Ryan
<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>

CC BY-SA 4.0
2019-09-11T09:16:26.103Z
You need raw count data, and let limma normalize it (with e.g., voom). Read the manual, it is pretty well written.
benn
CC BY-SA 4.0
2019-09-11T09:18:20.750Z
@benn But I have the normalized data already, I want to get single expression values
Vinay Bharadhwaj
CC BY-SA 4.0
2019-09-11T09:33:12.850Z
Try to get raw counts.
benn
CC BY-SA 4.0
2019-09-11T09:34:43.607Z
@benn Ok , and then, I should normalize with voom, after which how should I get DE values for each sample separately?
Vinay Bharadhwaj
CC BY-SA 4.0
2019-09-11T09:41:57.863Z
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).
haci
CC BY-SA 4.0
2019-09-11T09:44:32.370Z
@haci Hey, I have added the microarray tag, I am sorry for the confusion
Vinay Bharadhwaj
CC BY-SA 4.0
2019-09-11T09:45:29.557Z
Okay I assumed it was RNAseq... Just read the manual...
benn
CC BY-SA 4.0
2019-09-11T09:50:38.707Z
@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
Vinay Bharadhwaj
CC BY-SA 4.0
2019-09-11T09:58:38.047Z
If you do statistics you compare groups, not separate samples. Separate samples do not have information about variance which is needed for statistics.
benn
CC BY-SA 4.0
2019-09-11T10:02:00.933Z
@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
Vinay Bharadhwaj
<differential-expression><microarray><limma>
How to perform DE analysis for each sample
# 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**: @benn But I have the normalized data already, I want to get single expression values  
> **username_1**: Try to get raw counts.  
> **username_0**: @benn 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**: @haci 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**: @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  
> **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**: @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  

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## 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!  

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