<p>There's no need to normalize them, you're not comparing them. Just use them as they are.</p>

CC BY-SA 4.0
2019-05-27T19:31:08.177Z
Thank you, even if one comes from single cell RNA-seq and another comes from proteomics? The ultimate goal would be visualizing their relationship
Zizogolu
CC BY-SA 4.0
2019-05-27T19:33:05.860Z
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.
Devon Ryan
CC BY-SA 4.0
2019-05-28T12:13:53.997Z
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?
Zizogolu
CC BY-SA 4.0
2019-05-28T12:36:43.000Z
That's up to you, there's no single answer to such questions.
Devon Ryan
CC BY-SA 4.0
2019-05-27T19:23:14.000Z
2019-05-27T19:23:14.000Z
Devon Ryan
<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>

CC BY-SA 4.0
2019-05-27T19:40:15.873Z
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
M__
<r><scrnaseq><normalization><proteomics><heatmap>
How I normalize these two sets of data
# 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.  

---

