<p>Look up a tutorial on DESeq2.  To keep things simple for your first time, I'd reduce your phenotypes to "high" and "low" bins instead of trying to use the cell count numbers as they are.  Then once you understand what the software is doing, you can get fancier.</p>

CC BY-SA 4.0
2019-10-20T17:34:18.683Z
Thank you I need some genes predicting for these traits, so can I simply do differentially expressed regarding each trait individually? But I am not sure if I need getting important genes for interaction of these traits?
Zizogolu
CC BY-SA 4.0
2019-10-21T16:59:15.887Z
Hi Angel, you need to be a bit more precise about what you are looking for. When you say "find genes differential expressed in these patients", it means genes that are significantly different between two groups. @swbarnes was correct in pointing you to defining binary phenotypes
StupidWolf
CC BY-SA 4.0
2019-10-21T17:00:29.757Z
If you want "some genes predicting for these traits", what you are looking for is correlation between gene expression and phenotype. In this case I would do a spearman correlation between gene expression and IC for example.
StupidWolf
CC BY-SA 4.0
2019-10-21T21:05:25.870Z
Correlation based on raw counts is probably not the best idea.
swbarnes2
CC BY-SA 4.0
2019-10-20T05:55:49.413Z
2019-10-20T05:55:49.413Z
swbarnes2
<p>I have a gene expression raw counts like below for 16 patients </p>

<pre><code>         A  B    C      D   E   F
A2M     511 1623 665    208 553 469
AADAT   34  137 372     7   52  124
ABCB1   119 114 123    22   25  89
ABCB11  27  186 200    27   30  49
ABCC2   1   10  21     1    8   3
</code></pre>

<p>For each patients I also have some characteristics </p>

<pre><code>Patients                          IC          TCs
A1                                3            3
B1                                2            0
C1                                2            0
D1                                0            0
</code></pre>

<p>I want to find genes differential expressed in these patients regarding these characteristics but I don't know how to do that</p>

<p>Any help please?</p>

<r><rna-seq><differential-expression><modelling>
How I deal with this expression set?
# How I deal with this expression set?

(Asked by: username_0 on 2019-10-19)

I have a gene expression raw counts like below for 16 patients 
         A  B    C      D   E   F
A2M     511 1623 665    208 553 469
AADAT   34  137 372     7   52  124
ABCB1   119 114 123    22   25  89
ABCB11  27  186 200    27   30  49
ABCC2   1   10  21     1    8   3

For each patients I also have some characteristics 
Patients                          IC          TCs
A1                                3            3
B1                                2            0
C1                                2            0
D1                                0            0

I want to find genes differential expressed in these patients regarding these characteristics but I don't know how to do that
Any help please?

**Tags:** r, rna-seq, differential-expression, modelling

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## 1 answers

### Answer by username_1 on 2019-10-20. Score: 1

Look up a tutorial on DESeq2. To keep things simple for your first time, I'd reduce your phenotypes to "high" and "low" bins instead of trying to use the cell count numbers as they are. Then once you understand what the software is doing, you can get fancier.

> **Comments:**
> **username_0**: Thank you I need some genes predicting for these traits, so can I simply do differentially expressed regarding each trait individually? But I am not sure if I need getting important genes for interaction of these traits?  
> **username_2**: Hi Angel, you need to be a bit more precise about what you are looking for. When you say "find genes differential expressed in these patients", it means genes that are significantly different between two groups. @swbarnes was correct in pointing you to defining binary phenotypes  
> **username_2**: If you want "some genes predicting for these traits", what you are looking for is correlation between gene expression and phenotype. In this case I would do a spearman correlation between gene expression and IC for example.  
> **username_1**: Correlation based on raw counts is probably not the best idea.  

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