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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?

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    $\begingroup$ 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) $\endgroup$
    – llrs
    Dec 6, 2017 at 11:31

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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.

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