<p>There is a paper that did what you are asking: <a href="https://www.nature.com/articles/sdata201861" rel="nofollow noreferrer">Unifying cancer and normal RNA sequencing data from different sources</a>.</p>

CC BY-SA 4.0
2020-11-19T22:38:30.883Z
2020-11-19T22:38:30.883Z
burger
<p>This has been asked and answered here and Biostars. My take home message from many similar posts is &quot;not to bother with correcting batch effects when performing differential expression analysis but to include 'batch' as a covariate&quot;. The latter accounts for the batch effects.</p>
<p>EDIT: This does not really answer OP's question, as @swbarnes2 and @ATpoint pointed out, they have a completely confounded design and the linear regression model would not accept such a model matrix.</p>

CC BY-SA 4.0
2020-11-17T03:30:59.583Z
This cannot possibly work if batch is totally confounded with sample type.
swbarnes2
CC BY-SA 4.0
2020-11-17T12:12:47.133Z
Thanks for your answer. Is it OK to ignore the batch effects?
Kai He
CC BY-SA 4.0
2020-11-17T12:24:29.923Z
Personally I don't think it will be OK to ignore the batch effects. If you still perform the DEA, you should be extremely cautious about the results, you will not be able to tell if the differential expression is the result of true biology or batch effect. You might want to turn to the bench to validate your results if possible.
haci
CC BY-SA 4.0
2020-11-16T11:06:49.443Z
2020-11-17T08:50:07.797Z
2020-11-17T08:50:07.797Z
haci
<p>I want to play some differentially expressed genes for gliomas cancer. I collected cancer tissue profiles from the TCGA database and normal tissue profiles from the GTEx database. I straightforward use ComBat from R package &quot;sva&quot;, and the result shows little differentially expressed genes (DEGs) were found. How can I remove batch effects correctly?</p>
<p>Thanks</p>

CC BY-SA 4.0
2020-11-17T03:30:25.430Z
Yup.  I'm very sorry, but RNASeq doesn't work like that.  It is far too sensitive to batch effects.
swbarnes2
<combat><gtex>
How to remove batch effect from TCGA and GTEx data
# How to remove batch effect from TCGA and GTEx data

(Asked by: username_0 on 2020-11-16)

I want to play some differentially expressed genes for gliomas cancer. I collected cancer tissue profiles from the TCGA database and normal tissue profiles from the GTEx database. I straightforward use ComBat from R package "sva", and the result shows little differentially expressed genes (DEGs) were found. How can I remove batch effects correctly?

Thanks

**Tags:** combat, gtex
> **Comments:**
> **username_1**: Yup.  I'm very sorry, but RNASeq doesn't work like that.  It is far too sensitive to batch effects.  

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## 2 answers

### Answer by username_2 on 2020-11-19. Score: 1

There is a paper that did what you are asking: [Unifying cancer and normal RNA sequencing data from different sources](https://www.nature.com/articles/sdata201861).


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### Answer by username_3 on 2020-11-16. Score: 0

This has been asked and answered here and Biostars. My take home message from many similar posts is "not to bother with correcting batch effects when performing differential expression analysis but to include 'batch' as a covariate". The latter accounts for the batch effects.

EDIT: This does not really answer OP's question, as @swbarnes2 and @ATpoint pointed out, they have a completely confounded design and the linear regression model would not accept such a model matrix.

> **Comments:**
> **username_1**: This cannot possibly work if batch is totally confounded with sample type.  
> **username_0**: Thanks for your answer. Is it OK to ignore the batch effects?  
> **username_3**: Personally I don't think it will be OK to ignore the batch effects. If you still perform the DEA, you should be extremely cautious about the results, you will not be able to tell if the differential expression is the result of true biology or batch effect. You might want to turn to the bench to validate your results if possible.  

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