Answers.Body: <p>According to Jaakkola et al. (ref. 1):</p>
  
  <blockquote>
    <p>Based on our comparisons, DESeq and Limma without any modifications are not suitable for scRNA-seq data analysis, and yet, they have performed well in the context of bulk RNA-seq data.</p>
  </blockquote>
  
  <p>They also provided a summary of the comparison between 5 tools (2 designed for Single-Cell, 2 designed for bulk, 1 general): </p>
  
  <p><a href="https://i.sstatic.net/Q6Sj6.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Q6Sj6.png" alt="compar"></a> </p>
  
  <p>In my case I ended up using MAST (ref. 2, <a href="https://github.com/RGLab/MAST" rel="nofollow noreferrer">github</a>).</p>
  
  <ol>
  <li>Jaakkola, M. K., Seyednasrollah, F., Mehmood, A. &amp; Elo, L. L. <a href="https://academic.oup.com/bib/article/18/5/735/2562772" rel="nofollow noreferrer">Comparison of methods to detect differentially expressed genes between single-cell populations.</a> Briefings in Bioinformatics 18, 735–743 (2017).</li>
  <li>Finak, G. et al. <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0844-5" rel="nofollow noreferrer">MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.</a> Genome Biol. 16, 1183 (2015).</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-09T08:30:58.657Z
Answers.Text: In a paper of Soneson & Robinson (2018) in Nature Methods ([doi:10.1038/nmeth.4612](https://www.nature.com/articles/nmeth.4612)), the authors concluded that "bulk" methods do not perform worse than methods developed specifically for scRNA-seq.
Answers.UserDisplayName: benn
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Answers.CreationDate: 2018-05-09T14:32:05.467Z
Answers.Text: @b.nota Thank you! Can you edit my question to reflect also your comment?
Answers.UserDisplayName: gc5
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Answers.CreationDate: 2018-05-08T19:42:09.730Z
Answers.LastActivityDate: 2018-05-08T19:42:09.730Z
Answers.UserDisplayName: gc5
Body: <p>I am starting to run analysis for differential expression in scRNA-Seq. Which tools are available for this kind of analysis?</p>
  
  <p>Can tools for bulk RNA-Seq like <a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq/inst/doc/DESeq.pdf" rel="noreferrer">DESeq</a> be used for scRNA-Seq?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-03-20T22:13:12.947Z
Comments.Text: I recommend this [page](https://www.scrna-tools.org) to learn about other tools
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-17T19:56:07.820Z
Comments.Text: @Llopis thanks. I'd recommend also this curated repository for Single-Cell analysis: https://github.com/seandavi/awesome-single-cell
Comments.UserDisplayName: gc5
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-17T20:00:26.577Z
Comments.Text: Yes, and there's also the [online course](https://hemberg-lab.github.io/scRNA.seq.course/introduction-to-single-cell-rna-seq.html), where it says that "Several computational analysis methods from bulk RNA-seq can be used"
Comments.UserDisplayName: llrs
Tags: <rna-seq><scrnaseq><differential-expression>
Title: Which tools for differential expression analysis in scRNA-Seq?
ThreadText: # Which tools for differential expression analysis in scRNA-Seq?
  
  (Asked by: username_0 on 2018-03-20)
  
  I am starting to run analysis for differential expression in scRNA-Seq. Which tools are available for this kind of analysis?
  
  Can tools for bulk RNA-Seq like [DESeq](https://bioconductor.org/packages/release/bioc/vignettes/DESeq/inst/doc/DESeq.pdf) be used for scRNA-Seq?
  
  **Tags:** rna-seq, scrnaseq, differential-expression
  
  > **Comments:**
  > **username_1**: I recommend this [page](https://www.scrna-tools.org) to learn about other tools  
  > **username_0**: @Llopis thanks. I'd recommend also this curated repository for Single-Cell analysis: https://github.com/seandavi/awesome-single-cell  
  > **username_1**: Yes, and there's also the [online course](https://hemberg-lab.github.io/scRNA.seq.course/introduction-to-single-cell-rna-seq.html), where it says that "Several computational analysis methods from bulk RNA-seq can be used"  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2018-05-08. Score: 1 | Accepted answer
  
  According to Jaakkola et al. (ref. 1):
  
  > Based on our comparisons, DESeq and Limma without any modifications are not suitable for scRNA-seq data analysis, and yet, they have performed well in the context of bulk RNA-seq data.
  
  They also provided a summary of the comparison between 5 tools (2 designed for Single-Cell, 2 designed for bulk, 1 general):
  
  [![compar](https://i.sstatic.net/Q6Sj6.png)](https://i.sstatic.net/Q6Sj6.png)
  
  In my case I ended up using MAST (ref. 2, [github](https://github.com/RGLab/MAST)).
  
  1. Jaakkola, M. K., Seyednasrollah, F., Mehmood, A. & Elo, L. L. [Comparison of methods to detect differentially expressed genes between single-cell populations.](https://academic.oup.com/bib/article/18/5/735/2562772) Briefings in Bioinformatics 18, 735–743 (2017).
  2. Finak, G. et al. [MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0844-5) Genome Biol. 16, 1183 (2015).
  
  > **Comments:**
  > **username_2**: In a paper of Soneson & Robinson (2018) in Nature Methods ([doi:10.1038/nmeth.4612](https://www.nature.com/articles/nmeth.4612)), the authors concluded that "bulk" methods do not perform worse than methods developed specifically for scRNA-seq.  
  > **username_0**: @b.nota Thank you! Can you edit my question to reflect also your comment?  
  
  ---
  
  