<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>

CC BY-SA 4.0
2018-05-09T08:30:58.657Z
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.
benn
CC BY-SA 4.0
2018-05-09T14:32:05.467Z
@b.nota Thank you! Can you edit my question to reflect also your comment?
gc5
CC BY-SA 4.0
2018-05-08T19:42:09.730Z
2018-05-08T19:42:09.730Z
gc5
<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>

CC BY-SA 3.0
2018-03-20T22:13:12.947Z
I recommend this [page](https://www.scrna-tools.org) to learn about other tools
llrs
CC BY-SA 3.0
2018-04-17T19:56:07.820Z
@Llopis thanks. I'd recommend also this curated repository for Single-Cell analysis: https://github.com/seandavi/awesome-single-cell
gc5
CC BY-SA 3.0
2018-04-17T20:00:26.577Z
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"
llrs
<rna-seq><scrnaseq><differential-expression>
Which tools for differential expression analysis in scRNA-Seq?
# 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?  

---

