Answers.Body: <p>There's no need to normalize them, you're not comparing them. Just use them as they are.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-27T19:31:08.177Z
Answers.Text: Thank you, even if one comes from single cell RNA-seq and another comes from proteomics? The ultimate goal would be visualizing their relationship
Answers.UserDisplayName: Zizogolu
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-27T19:33:05.860Z
Answers.Text: The sources doesn’t really matter. You can scale them a bit if it helps for visualization, but that’s as much as would be useful.
Answers.UserDisplayName: Devon Ryan
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-28T12:13:53.997Z
Answers.Text: Sorry to coming back again. If I want to combine these two data sets; for instance, I would have gene A common between two data sets. So, If I want to visualize both data in combination not comparison, how I deal with two values from different experiments for this overlap genes? Can I simply take average of values?
Answers.UserDisplayName: Zizogolu
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-28T12:36:43.000Z
Answers.Text: That's up to you, there's no single answer to such questions.
Answers.UserDisplayName: Devon Ryan
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-27T19:23:14.000Z
Answers.LastActivityDate: 2019-05-27T19:23:14.000Z
Answers.UserDisplayName: Devon Ryan
Body: <p>I have average log fold change for a cluster of cells versus another cluster of cells by Seurat like below </p>
  
  <pre><code>         avg_logFC
  TAGLN   -2.507833541
  TXNRD1  1.093672244
  MYL9    -1.579165463
  ACTB    -1.482386539
  ACTA2   -2.379757433
  SQSTM1  1.227562767
  ACTG2   -2.144757086
  </code></pre>
  
  <p>I have also mean log2 ratio of a set of proteins in treatment versus control like below (I converted protein IDs to gene symbol)</p>
  
  <pre><code>mean log2ratio  Gene Symbol
  -0.3    CYFIP1
  0.4     GNPTG
  -0.3    WWC2
  -0.5    TRPV4
  -0.4    CHRM2
  0.3     MFSD11
  -0.3    ANXA11
  0.3     CENPE
  -0.4    RPL22L1
  0.3     SAMD1
  </code></pre>
  
  <p>How I can normalize these two data or make them compatible for plotting a heatmap, pathway analysis, etc?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-27T19:40:15.873Z
Comments.Text: You cannot normalise this further because it is already transformed and standardied "mean log2 ratio".  Sauret uses PCA and tSNE, where normalisation is not needed. You have previously demontrated advanced skills in machine learning, for example using neural networks, so I am very surprised by this question
Comments.UserDisplayName: M__
Tags: <r><scrnaseq><normalization><proteomics><heatmap>
Title: How I normalize these two sets of data
ThreadText: # How I normalize these two sets of data
  
  (Asked by: username_0 on 2019-05-27)
  
  I have average log fold change for a cluster of cells versus another cluster of cells by Seurat like below
  
  ```
           avg_logFC
  TAGLN   -2.507833541
  TXNRD1  1.093672244
  MYL9    -1.579165463
  ACTB    -1.482386539
  ACTA2   -2.379757433
  SQSTM1  1.227562767
  ACTG2   -2.144757086
  
  ```
  
  I have also mean log2 ratio of a set of proteins in treatment versus control like below (I converted protein IDs to gene symbol)
  
  ```
  mean log2ratio  Gene Symbol
  -0.3    CYFIP1
  0.4     GNPTG
  -0.3    WWC2
  -0.5    TRPV4
  -0.4    CHRM2
  0.3     MFSD11
  -0.3    ANXA11
  0.3     CENPE
  -0.4    RPL22L1
  0.3     SAMD1
  
  ```
  
  How I can normalize these two data or make them compatible for plotting a heatmap, pathway analysis, etc?
  
  **Tags:** r, scrnaseq, normalization, proteomics, heatmap
  
  > **Comments:**
  > **username_1**: You cannot normalise this further because it is already transformed and standardied "mean log2 ratio".  Sauret uses PCA and tSNE, where normalisation is not needed. You have previously demontrated advanced skills in machine learning, for example using neural networks, so I am very surprised by this question  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-05-27. Score: 2 | Accepted answer
  
  There's no need to normalize them, you're not comparing them. Just use them as they are.
  
  > **Comments:**
  > **username_0**: Thank you, even if one comes from single cell RNA-seq and another comes from proteomics? The ultimate goal would be visualizing their relationship  
  > **username_2**: The sources doesn’t really matter. You can scale them a bit if it helps for visualization, but that’s as much as would be useful.  
  > **username_0**: Sorry to coming back again. If I want to combine these two data sets; for instance, I would have gene A common between two data sets. So, If I want to visualize both data in combination not comparison, how I deal with two values from different experiments for this overlap genes? Can I simply take average of values?  
  > **username_2**: That's up to you, there's no single answer to such questions.  
  
  ---
  
  