<p>According to <a href="https://peerj.com/articles/3859/#supplemental-information" rel="nofollow noreferrer">https://peerj.com/articles/3859/#supplemental-information</a>, it is most appropriate to bin and normalize by taxa to avoid expression changes that might be expected simply due to a change of community composition.</p>
<p>In my particular instance, I am using the same initial community in an experiment and the communities do not change too much by the time of sampling, so the &quot;global&quot; data are likely still valuable for a large-scale view.</p>

CC BY-SA 4.0
2024-10-23T18:39:19.377Z
2024-10-23T18:39:19.377Z
MockCommunity1
<p>I want to perform differential gene expression analysis from metatranscrptomic data.</p>
<p>I have metatranscriptomic data, processed through the Eukrhythmic pipeline. I am diving a bit into to the taxonomic bins and am wondering which way seems most appropriate to do so.</p>
<p>I can either subset the transcripts and their read numbers by taxa before input into DESeq2, or after.
The difference here would be in the size factor, which is used to normalize for sequencing effort between samples.</p>
<p>My samples aren't terribly different in their taxonomic composition, but you can imagine a scenario where the relative abundance in one sample is different than another, which would result in very different sequencing depths between the samples.</p>
<p>Would subsetting first so that the size factor is generated &quot;within&quot; taxa be the best route here?</p>

CC BY-SA 4.0
2024-10-11T00:47:54.273Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Community
CC BY-SA 4.0
2024-10-22T02:30:45.207Z
This seems like DESeq2 might be the wrong tool to use, because it has a core assumption that most genes / features are not differentially expressed. Can you provide any links to support this approach?
gringer
CC BY-SA 4.0
2024-10-23T18:35:20.123Z
Not sure why that assumption would make DESeq2 any less applicable than any other RNA-seq experiment. 
I found a paper directly addressing my question, and the answer is to normalize at the taxa-level, because the global expression data is subject to changes by community composition. https://peerj.com/articles/3859/#supplemental-information
MockCommunity1
<rna-seq><deseq2><transcriptome>
Subsetting metatranscriptomic data by taxon, before or after DESeq size factor calculation?
# Subsetting metatranscriptomic data by taxon, before or after DESeq size factor calculation?

(Asked by: username_0 on 2024-10-10)

I want to perform differential gene expression analysis from metatranscrptomic data.

I have metatranscriptomic data, processed through the Eukrhythmic pipeline. I am diving a bit into to the taxonomic bins and am wondering which way seems most appropriate to do so.

I can either subset the transcripts and their read numbers by taxa before input into DESeq2, or after.
The difference here would be in the size factor, which is used to normalize for sequencing effort between samples.

My samples aren't terribly different in their taxonomic composition, but you can imagine a scenario where the relative abundance in one sample is different than another, which would result in very different sequencing depths between the samples.

Would subsetting first so that the size factor is generated "within" taxa be the best route here?

**Tags:** rna-seq, deseq2, transcriptome
> **Comments:**
> **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
> **username_2**: This seems like DESeq2 might be the wrong tool to use, because it has a core assumption that most genes / features are not differentially expressed. Can you provide any links to support this approach?  
> **username_0**: Not sure why that assumption would make DESeq2 any less applicable than any other RNA-seq experiment.  I found a paper directly addressing my question, and the answer is to normalize at the taxa-level, because the global expression data is subject to changes by community composition. https://peerj.com/articles/3859/#supplemental-information  

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## 1 answers

### Answer by username_0 on 2024-10-23. Score: 1 | Accepted answer

According to <https://peerj.com/articles/3859/#supplemental-information>, it is most appropriate to bin and normalize by taxa to avoid expression changes that might be expected simply due to a change of community composition.

In my particular instance, I am using the same initial community in an experiment and the communities do not change too much by the time of sampling, so the "global" data are likely still valuable for a large-scale view.


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