<p>To me there is a clear batch effect separating points along PC1. Samples left of x=0 are batch1 and samples right of x=0 are batch 2. Does this make sense, so have samples been processed in batches? That would be the first thing to correct for, see DESeq2 vignette on batch effects and visualization. Beyond that, it seems your treatments do not seem to have a great effect, but lets first do a PCA after correcting for the PC1 separation, and then lets see.</p>

CC BY-SA 4.0
2024-06-17T12:09:09.840Z
Thank you for your response. That it what I thought as well, but according to the sequencing company all of my samples were sequenced on the same flowcell and lane. I could ask them about the library prep, but I believe that they also process these in the same batch. I added the PCA with batch effect removed in my question.
NotmyName
CC BY-SA 4.0
2024-06-17T12:53:13.473Z
Do you get DEGs now? Effect is not big obviously, but maybe some genes change. And if not, that's still a message.
ATpoint
CC BY-SA 4.0
2024-06-17T13:25:24.457Z
Still no DEGs, which is a bit unfortunate. Thanks for the help!
NotmyName
CC BY-SA 4.0
2024-06-17T14:57:09.623Z
How did you "remove" the batch effect?
Ram RS
CC BY-SA 4.0
2024-06-17T19:35:36.587Z
Sorry, I should've clarified. I am not sure whether my samples actually have been processed in batches (I contacted the sequencing company). However, assuming there is a batch effect on these samples splitting them along the first component, I added a batch factor in my design using: design= ~ batch + condition and then made the PCA as described in the deseq2 vignette.
NotmyName
CC BY-SA 4.0
2024-06-17T11:17:30.253Z
2024-06-17T11:17:30.253Z
ATpoint
<p>I think you should start by taking this data at face value; your virus variants are not causing any significant changes to the cell's global RNA expression.</p>
<p>Have to looked at what genes are driving PC1?  73% is a rather strong component, you should see if you can see a biological logic behind those genes.</p>
<p>Did you outsource all the work for this?  Because putting the library on the instrument doesn't introduce any significant batch effect.  It's RNA prep and library preps that do.  Surely whatever company you sent you samples to is not so stupid as to introduce a batch effect?</p>

CC BY-SA 4.0
2024-11-18T22:17:54.743Z
2024-11-18T22:17:54.743Z
swbarnes2
<p>I am very new to analyzing RNAseq and I am in a group with very little experience in this regard and I am looking for some advice.</p>
<p>My PCA after performing DESEQ2 analysis on my dataset doesn't show clustering of my samples as I would've expected. I am also not getting any differentially expressed genes from the analysis. Now, I know that for this experiment there is a chance that we wouldn't see any differences, but I find the PCA to be very odd because the variation among samples is very high and it almost appears as if there are two large clusters, one on the left and one on the right of the plot. Could someone give me some advice on how to continue from here?</p>
<p>Some background on the experiment:
I infected tissue culture cells with different viruses and extracted total RNA shortly after infection (24 hours). The viruses are different mutants of the same virus and we want to see how the host cell responds to these variants. Hence, why I not necesarily expect large changes as the response to the viruses could be very similar. I sent my samples to a company for paired-end mRNA sequencing. After obtaining the raw data I used cutadapt -&gt; STAR -&gt; featureCounts -&gt; DESeq2.
The quality of the reads was very high in general and there didn't seem to be any obvious issues.</p>
<p><a href="https://i.sstatic.net/T3UnPSJj.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/T3UnPSJj.png" alt="PCA plot" /></a></p>
<p>@ATpoint pointed out a potential batch effect, I tried removing the batch effect and plotted again the PCA:
<a href="https://i.sstatic.net/2mGl4lM6.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/2mGl4lM6.png" alt="enter image description here" /></a></p>

CC BY-SA 4.0
2024-06-17T14:27:06.800Z
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
2024-06-17T22:17:15.390Z
What were you expecting to see from the PCA?
gringer
CC BY-SA 4.0
2024-06-18T04:49:33.353Z
I was expecting my groups to cluster together as these are infected by the same mutant virus. In the event that the host doesnt respond differently to my different viruses I wouldve expected to at least see all groups closer together than what I see now. As in that case, the samples are basically cell lines treated the same way.
NotmyName
CC BY-SA 4.0
2024-12-26T15:53:46.640Z
Is there any kind of positive control expressed gene that you can think of that you can inspect that would be a response to the virus? I am not super familiar with gene expression ordination, but have you tried playing around with different normalizations of your expression matrix?
Maximilian Press
<rna-seq><deseq2><clustering><pca>
PCA of bulk RNA-seq doesn't show clustering and show large variation in general
# PCA of bulk RNA-seq doesn't show clustering and show large variation in general

(Asked by: username_0 on 2024-06-17)

I am very new to analyzing RNAseq and I am in a group with very little experience in this regard and I am looking for some advice.

My PCA after performing DESEQ2 analysis on my dataset doesn't show clustering of my samples as I would've expected. I am also not getting any differentially expressed genes from the analysis. Now, I know that for this experiment there is a chance that we wouldn't see any differences, but I find the PCA to be very odd because the variation among samples is very high and it almost appears as if there are two large clusters, one on the left and one on the right of the plot. Could someone give me some advice on how to continue from here?

Some background on the experiment:
I infected tissue culture cells with different viruses and extracted total RNA shortly after infection (24 hours). The viruses are different mutants of the same virus and we want to see how the host cell responds to these variants. Hence, why I not necesarily expect large changes as the response to the viruses could be very similar. I sent my samples to a company for paired-end mRNA sequencing. After obtaining the raw data I used cutadapt -> STAR -> featureCounts -> DESeq2.
The quality of the reads was very high in general and there didn't seem to be any obvious issues.

[![PCA plot](https://i.sstatic.net/T3UnPSJj.png)](https://i.sstatic.net/T3UnPSJj.png)

@ATpoint pointed out a potential batch effect, I tried removing the batch effect and plotted again the PCA:
[![enter image description here](https://i.sstatic.net/2mGl4lM6.png)](https://i.sstatic.net/2mGl4lM6.png)

**Tags:** rna-seq, deseq2, clustering, pca
> **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**: What were you expecting to see from the PCA?  
> **username_0**: I was expecting my groups to cluster together as these are infected by the same mutant virus. In the event that the host doesnt respond differently to my different viruses I wouldve expected to at least see all groups closer together than what I see now. As in that case, the samples are basically cell lines treated the same way.  
> **username_3**: Is there any kind of positive control expressed gene that you can think of that you can inspect that would be a response to the virus? I am not super familiar with gene expression ordination, but have you tried playing around with different normalizations of your expression matrix?  

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## 2 answers

### Answer by username_4 on 2024-06-17. Score: 1

To me there is a clear batch effect separating points along PC1. Samples left of x=0 are batch1 and samples right of x=0 are batch 2. Does this make sense, so have samples been processed in batches? That would be the first thing to correct for, see DESeq2 vignette on batch effects and visualization. Beyond that, it seems your treatments do not seem to have a great effect, but lets first do a PCA after correcting for the PC1 separation, and then lets see.

> **Comments:**
> **username_0**: Thank you for your response. That it what I thought as well, but according to the sequencing company all of my samples were sequenced on the same flowcell and lane. I could ask them about the library prep, but I believe that they also process these in the same batch. I added the PCA with batch effect removed in my question.  
> **username_4**: Do you get DEGs now? Effect is not big obviously, but maybe some genes change. And if not, that's still a message.  
> **username_0**: Still no DEGs, which is a bit unfortunate. Thanks for the help!  
> **username_5**: How did you "remove" the batch effect?  
> **username_0**: Sorry, I should've clarified. I am not sure whether my samples actually have been processed in batches (I contacted the sequencing company). However, assuming there is a batch effect on these samples splitting them along the first component, I added a batch factor in my design using: design= ~ batch + condition and then made the PCA as described in the deseq2 vignette.  

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### Answer by username_6 on 2024-11-18. Score: 1

I think you should start by taking this data at face value; your virus variants are not causing any significant changes to the cell's global RNA expression.

Have to looked at what genes are driving PC1? 73% is a rather strong component, you should see if you can see a biological logic behind those genes.

Did you outsource all the work for this? Because putting the library on the instrument doesn't introduce any significant batch effect. It's RNA prep and library preps that do. Surely whatever company you sent you samples to is not so stupid as to introduce a batch effect?


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