<p>i try to implement LIMMA for paired samples in order to compare gene expression before/after treatment. But...i'm not confident in my results since every single gene expression seems to be significantly associeted with the treatment!</p>
<p>Here is my methodology:</p>
<p><code>mat&lt;-t(df)</code></p>
<p>where df is a dataframe with:
-1000 columns for different gene expression
-+ 1 column 'id' (10 different id, repeated 2 times)
-+ 1 column 'ttt' (0=before, 1=after treatment)
-20 rows of observations (10 patients observed twice)</p>
<p>So once I get my matrix:</p>
<p><code>subject_id&lt;-df$id</code> A vector with 10 different id repeated 2 times</p>
<p><code>ttt&lt;-as.factor(df$ttt)</code> A vector with the treatment status (before/after)</p>
<p>Here is my design matrix:</p>
<p><code>design&lt;-model.matrix(~ 0+ttt)</code></p>
<p><code>colnames(design) &lt;- levels(ttt)</code></p>
<p>As asked in limma() description i specify the correlations:</p>
<p><code>corfit &lt;- duplicateCorrelation(mat,design,block=subject_id)</code></p>
<p>Fiting of the model:</p>
<p><code>fit&lt;-lmFit(mat,design,block =subject_id,correlation = corfit$consensus)</code></p>
<p><code>fit2&lt;-eBayes(fit)</code></p>
<p>Getting adjusted pvalues for every gene comparison before/after treatment:</p>
<p><code>topTable(fit2, adjust.method='BH',number=1000)</code></p>
<p>But every single gene expression seems to be relevant, so i'm very doubtful about my methodology
Any idea?
Many thanks!</p>

CC BY-SA 4.0
2020-12-04T22:56:40.447Z
what do you actually have, RNAseq or microarray data? I suspect you have RNAseq data and you need to normalize it
StupidWolf
CC BY-SA 4.0
2020-12-05T18:28:01.750Z
Hello, thank you for your response. I have transcriptomic responses, with logarithmic transformation (one value per gene expression).
Gwénolé
CC BY-SA 4.0
2020-12-06T02:13:45.053Z
What u have above should be ok. Just note that for the last step u are testing all coefficients to be zero. Not sure if that is what u want
StupidWolf
CC BY-SA 4.0
2020-12-06T02:15:04.043Z
But if you specify treatment and groups correctly. It should be ok. Without more info, hard to tell
StupidWolf
<gene-expression><limma>
Troubles implementing LIMMA for paired samples (before/after treatment) comparaisons
# Troubles implementing LIMMA for paired samples (before/after treatment) comparaisons

(Asked by: username_0 on 2020-12-03)

i try to implement LIMMA for paired samples in order to compare gene expression before/after treatment. But...i'm not confident in my results since every single gene expression seems to be significantly associeted with the treatment!

Here is my methodology:

`mat<-t(df)`

where df is a dataframe with:
-1000 columns for different gene expression
-+ 1 column 'id' (10 different id, repeated 2 times)
-+ 1 column 'ttt' (0=before, 1=after treatment)
-20 rows of observations (10 patients observed twice)

So once I get my matrix:

`subject_id<-df$id` A vector with 10 different id repeated 2 times

`ttt<-as.factor(df$ttt)` A vector with the treatment status (before/after)

Here is my design matrix:

`design<-model.matrix(~ 0+ttt)`

`colnames(design) <- levels(ttt)`

As asked in limma() description i specify the correlations:

`corfit <- duplicateCorrelation(mat,design,block=subject_id)`

Fiting of the model:

`fit<-lmFit(mat,design,block =subject_id,correlation = corfit$consensus)`

`fit2<-eBayes(fit)`

Getting adjusted pvalues for every gene comparison before/after treatment:

`topTable(fit2, adjust.method='BH',number=1000)`

But every single gene expression seems to be relevant, so i'm very doubtful about my methodology
Any idea?
Many thanks!

**Tags:** gene-expression, limma
> **Comments:**
> **username_1**: what do you actually have, RNAseq or microarray data? I suspect you have RNAseq data and you need to normalize it  
> **username_0**: Hello, thank you for your response. I have transcriptomic responses, with logarithmic transformation (one value per gene expression).  
> **username_1**: What u have above should be ok. Just note that for the last step u are testing all coefficients to be zero. Not sure if that is what u want  
> **username_1**: But if you specify treatment and groups correctly. It should be ok. Without more info, hard to tell  

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## 0 answers

