<p>I think the typical way to do this would be to identify a small number of marker genes whose presence and absence is specific for that cell type.</p>

CC BY-SA 4.0
2022-08-29T17:54:57.213Z
2022-08-29T17:54:57.213Z
swbarnes2
<p>I have a single cell dataset which I consider to be a reference , let say, for an human organ, I have identified some new clusters that correspond to cell types. I also have WGCNA modules that correspond to a identified cluster.
Given this background, is there any possibilities to validate the presence of the cell types from my reference dataset inside another single cell dataset ?</p>
<p>I was thinking about GSEA, but all the gene in a set have the same importance.
Could I use eigen vectors from first principal component for a given module expressed in the corresponding cell type, as a weight. Then calculate inside another dataset a sort of &quot;first principal component&quot; using the previous eigen vectors. Then I would simply calculate the correlation between the first principal component and the calculated one to validate whether my cell type is present or not.
I was also thinking about a sort of weigthed GSEA, if anything like this ever existed.</p>

CC BY-SA 4.0
2022-08-29T15:51:10.427Z
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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<single-cell><transcriptome><wgcna><gsea>
How can to validate the presence of a certain type of cells in a single cell dataset?
# How can to validate the presence of a certain type of cells in a single cell dataset?

(Asked by: username_0 on 2022-08-29)

I have a single cell dataset which I consider to be a reference , let say, for an human organ, I have identified some new clusters that correspond to cell types. I also have WGCNA modules that correspond to a identified cluster.
Given this background, is there any possibilities to validate the presence of the cell types from my reference dataset inside another single cell dataset ?

I was thinking about GSEA, but all the gene in a set have the same importance.
Could I use eigen vectors from first principal component for a given module expressed in the corresponding cell type, as a weight. Then calculate inside another dataset a sort of "first principal component" using the previous eigen vectors. Then I would simply calculate the correlation between the first principal component and the calculated one to validate whether my cell type is present or not.
I was also thinking about a sort of weigthed GSEA, if anything like this ever existed.

**Tags:** single-cell, transcriptome, wgcna, gsea
> **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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## 1 answers

### Answer by username_2 on 2022-08-29. Score: 1

I think the typical way to do this would be to identify a small number of marker genes whose presence and absence is specific for that cell type.


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