Answers.Body: <p>I see a lot of people using xCell. There should also be papers systematically compare many different methods, which might interest you.</p>
  
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Answers.CreationDate: 2020-06-24T07:45:26.550Z
Answers.Text: Thanks for the pointer. Do you have any reference of a good up-to-date review paper? A simple description of the methods and requirements would be highly appreciated too
Answers.UserDisplayName: rvinas
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-24T15:23:04.420Z
Answers.Text: This one looks promising although it may not cover all the methods https://www.biorxiv.org/content/10.1101/2020.01.10.897116v1.full.pdf
Answers.UserDisplayName: Phoenix Mu
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Answers.CreationDate: 2020-06-23T21:14:31.323Z
Answers.LastActivityDate: 2020-06-23T21:14:31.323Z
Answers.UserDisplayName: Phoenix Mu
Body: <p>I would like to find the proportion of each cell-type in bulk RNA-Seq transcriptomics data. I am looking for some guidance on the following:</p>
  <ul>
  <li>What are the state-of-the-art methods?</li>
  <li>What are their assumptions? (e.g. do they assume that cell-type expression signatures are available in advance?)</li>
  </ul>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-23T11:50:07.643Z
Comments.Text: Can you describe your experimental design further? This is often solved with single cell RNA-seq
Comments.UserDisplayName: Chris_Rands
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-24T07:43:38.840Z
Comments.Text: It's not my experiment - I am just trying to deconvolve bulk RNA-Seq data from a publicly available database (GTEx), e.g. given a whole blood sample, find out the proportions of each cell type (e.g. lymphocytes, macrophages,...). Any suggestion?
Comments.UserDisplayName: rvinas
Tags: <rna-seq><gene-expression><machine-learning>
Title: What are the state-of-the-art cell-type RNA-Seq deconvolution methods?
ThreadText: # What are the state-of-the-art cell-type RNA-Seq deconvolution methods?
  
  (Asked by: username_0 on 2020-06-23)
  
  I would like to find the proportion of each cell-type in bulk RNA-Seq transcriptomics data. I am looking for some guidance on the following:
  
  * What are the state-of-the-art methods?
  * What are their assumptions? (e.g. do they assume that cell-type expression signatures are available in advance?)
  
  **Tags:** rna-seq, gene-expression, machine-learning
  
  > **Comments:**
  > **username_1**: Can you describe your experimental design further? This is often solved with single cell RNA-seq  
  > **username_0**: It's not my experiment - I am just trying to deconvolve bulk RNA-Seq data from a publicly available database (GTEx), e.g. given a whole blood sample, find out the proportions of each cell type (e.g. lymphocytes, macrophages,...). Any suggestion?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-06-23. Score: 1
  
  I see a lot of people using xCell. There should also be papers systematically compare many different methods, which might interest you.
  
  > **Comments:**
  > **username_0**: Thanks for the pointer. Do you have any reference of a good up-to-date review paper? A simple description of the methods and requirements would be highly appreciated too  
  > **username_2**: This one looks promising although it may not cover all the methods https://www.biorxiv.org/content/10.1101/2020.01.10.897116v1.full.pdf  
  
  ---
  
  