Seurat/DESeq2 - single cell RNA-seq differential expression
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Clara ▴ 10
Last seen 2.4 years ago

Hi all,

I am approaching the analysis of single-cell RNA-seq data.

I have seen that Seurat package offers the option in FindMarkers (or also with the function DESeq2DETest) to use DESeq2 to analyze differential expression in two group of cells.

Assuming I have group A containing n_A cells and group_B containing n_B cells, is the result of the analysis identical to running DESeq2 on raw counts of each gene in n_A versus n_B samples? And is there a way to speed up the analysis when n_A and n_B are in the order if a few thousands cells?

In addition, I have a 'technical' question:

when I have a count table in the form of a data.frame (for example read with read.table from a text file), is it necessary to force it to matrix such as cts <- as.matrix(cts) before providing it as input to DESeqDataSetFromMatrix? It seems to be I can just provide the count table as a data frame.



seurat deseq2 • 12k views
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Last seen 1 hour ago
United States


As to speed, DESeq2 will be a lot slower than say a linear model when you have thousands of replicates. The software wasn’t optimized for this use case. According to a recent paper from Soneson and Robinson, you can use Wilcoxon effectively here for DE.

If you want to compare hundreds of cells with DESeq2, please use the zinbwave integration pipeline:

Finally, if you provide a data frame, DESeq2 I believe just does the conversion to matrix internally.

Entering edit mode

Thank you! I had seen in this paper by Soneson&Robinson that DESeq2 was employed, however the number of cells were much lower. In addition, input used was transcripts per million, while I think Seurat uses raw counts. In any case, I will look at the recommendations you point out, which I had missed.

Regarding the internal matrix conversion, then this was maybe already present in DESeq2 versions from years ago as I had seen using a data frame in some old code as well.


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