<p>Yes, it's okay to not fully-categorise cells; you can spend months digging deeper and never get to the bottom.</p>
<p>I work at an immunology institute, and it's common for the immunologists to have debates about their cell categorisations from single cell sequencing heatmaps.</p>

CC BY-SA 4.0
2024-04-08T13:00:48.933Z
2024-04-08T13:00:48.933Z
gringer
<blockquote>
<p>Notably, I am having a hard time differentiating between clusters that I think are macrophages and others that I believe to be B cells.</p>
</blockquote>
<p>Generally, that should not be too difficult. Macrophages will express high levels of mature-myeloid transcription factors, such as Spi1/PU.1 while B cells will express typical lymphoid transcription factors such as Pax5. In general, if you're not interested in these cells then I would at least give them a very crude working label based on obvious markers that you can find in typical papers.</p>
<blockquote>
<p>would it be ok if I just broadly annotated these other clusters as immune cells and move onto differential expression analysis?</p>
</blockquote>
<p>Yes, absolutely. Do what you need to get your paper going. Ignore things you don't need for your story. If you're not interested in immune cells then ignore them. For example, make a UMAP with CD45 (PTPRC) to show that these clusters you struggle with are leukocytes/immune cells, and call it a day.</p>
<blockquote>
<p>and not great for reproducibility</p>
</blockquote>
<p>To be fair, if anyone reprocesses your data they will find different clusters and markers just by using slightly different code and software versions. Document your code and software, that's what you need to do also for yourself to be able to work on the same dataset in the future without redoing everything. That's fine.</p>

CC BY-SA 4.0
2024-04-10T10:37:10.953Z
2024-04-11T09:24:41.653Z
2024-04-11T09:24:41.653Z
ATpoint
<p>I hope you all are doing well. I am currently conducting a scRNA-seq analysis on a data set containing mono-nuclear, non-myofiber muscle and muscle resident cells post injury. My research question is concerned with comparing the expression of SCAMPs within satellite/muscle stem cells in old and young mice. I am at the stage of cluster annotation and I have successfully annotated the satellite cells and FAP cells. I plan to annotate other cell types but I have noticed that I have been finding it very difficult to annotate clusters that are expressing markers related immune cells. Notably, I am having a hard time differentiating between clusters that I think are macrophages and others that I believe to be B cells. Considering that my analysis is mainly concerned with satellite cells, and I am running out of time before I need to draft and submit my poster, would it be ok if I just broadly annotated these other clusters as immune cells and move onto differential expression analysis? I know that this bad practice and not great for reproducibility, but this is my first time conducting this sort of analysis.</p>

CC BY-SA 4.0
2024-04-08T02:42:56.930Z
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
<scrnaseq>
Is it ok to shortcut cluster annotation in scRNA-seq?
# Is it ok to shortcut cluster annotation in scRNA-seq?

(Asked by: username_0 on 2024-04-08)

I hope you all are doing well. I am currently conducting a scRNA-seq analysis on a data set containing mono-nuclear, non-myofiber muscle and muscle resident cells post injury. My research question is concerned with comparing the expression of SCAMPs within satellite/muscle stem cells in old and young mice. I am at the stage of cluster annotation and I have successfully annotated the satellite cells and FAP cells. I plan to annotate other cell types but I have noticed that I have been finding it very difficult to annotate clusters that are expressing markers related immune cells. Notably, I am having a hard time differentiating between clusters that I think are macrophages and others that I believe to be B cells. Considering that my analysis is mainly concerned with satellite cells, and I am running out of time before I need to draft and submit my poster, would it be ok if I just broadly annotated these other clusters as immune cells and move onto differential expression analysis? I know that this bad practice and not great for reproducibility, but this is my first time conducting this sort of analysis.

**Tags:** scrnaseq
> **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.  

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## 2 answers

### Answer by username_2 on 2024-04-08. Score: 3

Yes, it's okay to not fully-categorise cells; you can spend months digging deeper and never get to the bottom.

I work at an immunology institute, and it's common for the immunologists to have debates about their cell categorisations from single cell sequencing heatmaps.


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### Answer by username_3 on 2024-04-10. Score: 3

> Notably, I am having a hard time differentiating between clusters that I think are macrophages and others that I believe to be B cells.

Generally, that should not be too difficult. Macrophages will express high levels of mature-myeloid transcription factors, such as Spi1/PU.1 while B cells will express typical lymphoid transcription factors such as Pax5. In general, if you're not interested in these cells then I would at least give them a very crude working label based on obvious markers that you can find in typical papers.

> would it be ok if I just broadly annotated these other clusters as immune cells and move onto differential expression analysis?

Yes, absolutely. Do what you need to get your paper going. Ignore things you don't need for your story. If you're not interested in immune cells then ignore them. For example, make a UMAP with CD45 (PTPRC) to show that these clusters you struggle with are leukocytes/immune cells, and call it a day.

> and not great for reproducibility

To be fair, if anyone reprocesses your data they will find different clusters and markers just by using slightly different code and software versions. Document your code and software, that's what you need to do also for yourself to be able to work on the same dataset in the future without redoing everything. That's fine.


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