<p>Regarding the big FC values, I just realise the Limma lmfit function requires as input a log2 matrix... so I just transformed my data and now makes more sense. Still I have the doubt regarding the collapsing technical replicates and and if some one can verify the pipeline would be like that.</p>
<p>Thanks!!!</p>
<p>Fer</p>

CC BY-SA 4.0
2024-03-15T19:55:29.480Z
Please could you mark this answer as accepted? That would be very welcome.
M__
CC BY-SA 4.0
2021-06-28T12:09:25.370Z
2021-06-28T12:09:25.370Z
FJ_nw
<p>I am trying to learn how to analyse normalised DIA-MS data and I am struggling with it ://</p>
<p>The original dataset I got is <strong>(6 conditions (2 samples each)) with 3 technical replicates (total: 36 sample columns x 1128 proteins in rows)</strong>. I removed the proteins with more than 80% of NA, and impute with kNN the rest of missing values.</p>
<p>Then I collapse the replicates doing the average of the 3 replicates (is this correct?)</p>
<p><strong>So now, I have a dataframe with 12 columns x 1072 rows.</strong></p>
<p>The question is... I am not sure how to perform the differential analysis from here (assuming I did well the previous steps lol)</p>
<p>Could someone help me with the tools or code to compare all the 6 conditions between them?</p>
<p>I read something about Limma package, but not sure if it can be used in this DIA-ms data and neither sure of how to use limma for it (I already check the vignette with no succeed)</p>
<p>UPDATE: I tried to use Limma as follows:</p>
<pre><code>fit1 &lt;- lmFit(norm.data_collapsed, design)
fit2 &lt;- contrasts.fit(fit1, contrasts = contrast)
fit3 &lt;- eBayes(fit2)
limma.res.01 &lt;- topTable(fit3, coef = comb.contrast[1], n= Inf)
</code></pre>
<p>This returns me a non-sense values for logFC or significance:</p>
<p><a href="https://i.sstatic.net/Sl8xX.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Sl8xX.png" alt="head of the results topTable" /></a></p>
<p>Any help would be very welcome,</p>
<p>Best regards</p>

<differential-expression><proteomics><limma><mass-spectrometry>
Help with DIA-Mass Spectrometry data analysis with several conditions (limma?)
# Help with DIA-Mass Spectrometry data analysis with several conditions (limma?)

(Asked by: username_0 on 2021-06-25)

I am trying to learn how to analyse normalised DIA-MS data and I am struggling with it ://
The original dataset I got is (6 conditions (2 samples each)) with 3 technical replicates (total: 36 sample columns x 1128 proteins in rows). I removed the proteins with more than 80% of NA, and impute with kNN the rest of missing values.
Then I collapse the replicates doing the average of the 3 replicates (is this correct?)
So now, I have a dataframe with 12 columns x 1072 rows.
The question is... I am not sure how to perform the differential analysis from here (assuming I did well the previous steps lol)
Could someone help me with the tools or code to compare all the 6 conditions between them?
I read something about Limma package, but not sure if it can be used in this DIA-ms data and neither sure of how to use limma for it (I already check the vignette with no succeed)
UPDATE: I tried to use Limma as follows:
fit1 <- lmFit(norm.data_collapsed, design)
fit2 <- contrasts.fit(fit1, contrasts = contrast)
fit3 <- eBayes(fit2)
limma.res.01 <- topTable(fit3, coef = comb.contrast[1], n= Inf)

This returns me a non-sense values for logFC or significance:

Any help would be very welcome,
Best regards

**Tags:** differential-expression, proteomics, limma, mass-spectrometry

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## 1 answers

### Answer by username_0 on 2021-06-28. Score: 1

Regarding the big FC values, I just realise the Limma lmfit function requires as input a log2 matrix... so I just transformed my data and now makes more sense. Still I have the doubt regarding the collapsing technical replicates and and if some one can verify the pipeline would be like that.

Thanks!!!

Fer

> **Comments:**
> **username_1**: Please could you mark this answer as accepted? That would be very welcome.  

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