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 be used for scRNA-Seq?

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    $\begingroup$ I recommend this page to learn about other tools $\endgroup$ – llrs Mar 20 '18 at 22:13
  • $\begingroup$ @Llopis thanks. I'd recommend also this curated repository for Single-Cell analysis: github.com/seandavi/awesome-single-cell $\endgroup$ – gc5 Apr 17 '18 at 19:56
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    $\begingroup$ Yes, and there's also the online course, where it says that "Several computational analysis methods from bulk RNA-seq can be used" $\endgroup$ – llrs Apr 17 '18 at 20:00

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):


In my case I ended up using MAST (ref. 2, github).

  1. Jaakkola, M. K., Seyednasrollah, F., Mehmood, A. & Elo, L. L. Comparison of methods to detect differentially expressed genes between single-cell populations. 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. Genome Biol. 16, 1183 (2015).
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    $\begingroup$ In a paper of Soneson & Robinson (2018) in Nature Methods (doi:10.1038/nmeth.4612), the authors concluded that "bulk" methods do not perform worse than methods developed specifically for scRNA-seq. $\endgroup$ – benn May 9 '18 at 8:30
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    $\begingroup$ @b.nota Thank you! Can you edit my question to reflect also your comment? $\endgroup$ – gc5 May 9 '18 at 14:32

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