Answers.Body: <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>
  
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Answers.Text: Please could you mark this answer as accepted? That would be very welcome.
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Body: <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>
  
Tags: <differential-expression><proteomics><limma><mass-spectrometry>
Title: Help with DIA-Mass Spectrometry data analysis with several conditions (limma?)
ThreadText: # 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:
  
  [![head of the results topTable](https://i.sstatic.net/Sl8xX.png)](https://i.sstatic.net/Sl8xX.png)
  
  Any help would be very welcome,
  
  Best regards
  
  **Tags:** differential-expression, proteomics, limma, mass-spectrometry
  
  
  ---
  
  ## 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.  
  
  ---
  
  