Answers.Body: <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>
  
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Answers.CreationDate: 2022-08-29T17:54:57.213Z
Answers.LastActivityDate: 2022-08-29T17:54:57.213Z
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Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-29T15:51:10.427Z
Comments.Text: 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.
Comments.UserDisplayName: Community
Tags: <single-cell><transcriptome><wgcna><gsea>
Title: How can to validate the presence of a certain type of cells in a single cell dataset?
ThreadText: # 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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