Answers.Body: <p>Single-cell tools were developed to address some specific factors unique to scRNA-seq, such as:</p>
  
  <ul>
  <li>a lot of dropouts, so hundreds or thousands of detected genes, not tens of thousands (for mammalian genomes)</li>
  <li>hundreds or thousands of replicates</li>
  <li>not necessarily well-defined groups</li>
  </ul>
  
  <p>Thus, I would say your experiment is a lot closer to bulk RNA-seq than scRNA-seq.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-12-13T18:51:29.993Z
Answers.LastActivityDate: 2017-12-13T18:51:29.993Z
Answers.UserDisplayName: burger
Body: <p>We have RNA-seq data from libraries prepared using the Smartseq2 single-cell protocol on <strong>500 cells</strong> (mini-bulk) / library. The complexity is much better than with single-cells (~14k genes for 1.5M reads).</p>
  
  <p>6 cell types / biological replicates were collected with flow cytometry, and there are 4 control and 5 diseased biological replicates (patients).</p>
  
  <p>We are mostly interested in the differences between control and disease either overall or within each cell type.</p>
  
  <p>Can single-cell tools (e.g. Seurat) be used on this dataset? It is neither bulk nor single-cell data. How would you approach this? </p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-12-06T11:31:18.570Z
Comments.Text: I would process it both ways and check if they are comparable. If not then I would need look into the methods more in detail (To see if they are applicable in this data)
Comments.UserDisplayName: llrs
Tags: <rna-seq><sequence-analysis><single-cell>
Title: Mini-bulk RNA-seq and scRNA-seq analysis differences?
ThreadText: # Mini-bulk RNA-seq and scRNA-seq analysis differences?
  
  (Asked by: username_0 on 2017-12-06)
  
  We have RNA-seq data from libraries prepared using the Smartseq2 single-cell protocol on **500 cells** (mini-bulk) / library. The complexity is much better than with single-cells (~14k genes for 1.5M reads).
  
  6 cell types / biological replicates were collected with flow cytometry, and there are 4 control and 5 diseased biological replicates (patients).
  
  We are mostly interested in the differences between control and disease either overall or within each cell type.
  
  Can single-cell tools (e.g. Seurat) be used on this dataset? It is neither bulk nor single-cell data. How would you approach this?
  
  **Tags:** rna-seq, sequence-analysis, single-cell
  
  > **Comments:**
  > **username_1**: I would process it both ways and check if they are comparable. If not then I would need look into the methods more in detail (To see if they are applicable in this data)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-12-13. Score: 2
  
  Single-cell tools were developed to address some specific factors unique to scRNA-seq, such as:
  
  * a lot of dropouts, so hundreds or thousands of detected genes, not tens of thousands (for mammalian genomes)
  * hundreds or thousands of replicates
  * not necessarily well-defined groups
  
  Thus, I would say your experiment is a lot closer to bulk RNA-seq than scRNA-seq.
  
  
  ---
  
  